Exemplified methods and systems facilitate presentation of data derived from measurements of the heart in a non-invasive procedure (e.g., via phase space tomography analysis). In particular, the exemplified methods and systems facilitate presentation of such measurements in a graphical user interface, or “GUI” (e.g., associated with a healthcare provider web portal to be used by physicians, researchers, or patients, and etc.) and/or in a report for diagnosis of heart pathologies and disease. The presentation facilitates a unified and intuitive visualization that includes three-dimensional visualizations and two-dimensional visualizations that are concurrently presented within a single interactive interface and/or report.
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/02 - Measuring pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography; Heart catheters for measuring blood pressure
A61B 6/50 - specially adapted for specific body parts; specially adapted for specific clinical applications
G06T 19/20 - Editing of 3D images, e.g. changing shapes or colours, aligning objects or positioning parts
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
G16H 30/00 - ICT specially adapted for the handling or processing of medical images
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
2.
METHODS AND SYSTEMS TO CONFIGURE AND USE NEURAL NETWORKS IN CHARACTERIZING PHYSIOLOGICAL SYSTEMS
The exemplified methods and systems facilitate the configuration and training of a neural network (e.g., a deep neural network, a convolutional neural network (CNN), etc.), or ensemble(s) thereof, with a biophysical signal data set to ascertain estimate for the presence or non-presence of disease or pathology in a subject as well as to assess and/or classify disease or pathology, including for example in some cases the severity of such disease or pathology, in a subject. In the context of the heart, the methods and systems described herein facilitate the configuration and training of a neural network, or ensemble(s) thereof, with a cardiac signal data set to ascertain estimate for the presence or non-presence of coronary artery disease or coronary pathology.
An exemplary method is disclosed that can be used in the diagnosis of hypertrophic cardiomyopathy (HCM) using a biophysical-sensor system configured to non-invasively and concurrently acquire electrocardiographic signals, seismographic signals, photoplethysmographic, and/or phonocardiographic signals, collectively referred to herein as biophysical signals, from at least the thoracic region of a subject. The acquired biophysical signals may be assessed for one or more conditions or indicators of hypertrophic cardiomyopathy and concurrently with other cardiac diseases, conditions, or indicators of either.
A61B 5/02 - Measuring pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography; Heart catheters for measuring blood pressure
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
4.
MULTI-SENSOR MEMS SYSTEM AND MACHINE-LEARNED ANALYSIS METHOD FOR HYPERTROPHIC CARDIOMYOPATHY ESTIMATION
An exemplary method is disclosed that can be used in the diagnosis of hypertrophic cardiomyopathy (HCM) using a biophysical-sensor system configured to non-invasively and concurrently acquire electrocardiographic signals, seismographic signals, photoplethysmographic, and/or phonocardiographic signals, collectively referred to herein as biophysical signals, from at least the thoracic region of a subject. The acquired biophysical signals may be assessed for one or more conditions or indicators of hypertrophic cardiomyopathy and concurrently with other cardiac diseases, conditions, or indicators of either.
A61B 5/02 - Measuring pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography; Heart catheters for measuring blood pressure
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
5.
Method and System to Assess Pulmonary Hypertension Using Phase Space Tomography and Machine Learning
Phase space tomography methods and systems to facilitate the analysis and evaluation of complex, quasi-periodic system by generating computed phase-space tomographic images and mathematical features as a representation of the dynamics of the quasi-periodic cardiac systems. The computed phase-space tomographic images can be presented to a physician to assist in the assessment of presence or non-presence of disease. In some implementations, the phase space tomographic images are used as input to a trained neural network classifier configured to assess for presence or non-presence of pulmonary hypertension, including pulmonary arterial hypertension.
The exemplified methods and systems (e.g., machine-learned systems) facilitate the use of respiration rate-related features, or parameters, in a model or classifier to estimate metrics associated with the physiological state of a subject, including for the presence or non-presence of a disease, medical condition, or indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases, medical conditions, or indication of either or in the treatment of said diseases or indicating conditions. In some cases, such respiration rate-related features are generated from a synthetic respiration waveform that represents, and is used as a proxy to, the true respiration waveform. The synthetic respiration waveform may be used in its own independent diagnostic and/or control applications in some embodiments.
The exemplified methods and systems facilitate the use, for diagnostics, monitoring, treatment, of one or more power spectral-based features or parameters determined from biophysical signals such as cardiac/biopotential signals and/or photoplethysmography signals that are acquired non-invasively from surface sensors placed on a patient while the patient is at rest. The power spectral-based features or parameters can be used in a model or classifier (e.g., a machine-learned classifier) to estimate metrics associated with the physiological state of a patient, including for the presence or non-presence of a disease, medical condition, or an indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases or conditions or in the treatment of said diseases or conditions.
A61B 5/021 - Measuring pressure in heart or blood vessels
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
8.
METHODS AND SYSTEMS FOR ENGINEERING VISUAL FEATURES FROM BIOPHYSICAL SIGNALS FOR USE IN CHARACTERIZING PHYSIOLOGICAL SYSTEMS
A clinical evaluation system and method are disclosed that facilitate the use of one or more visual features or parameters determined from biophysical signals such as cardiac/biopotential signals and/or photoplethysmography signals that are acquired, in preferred embodiments, non-invasively from surface sensors placed on a patient while the patient is at rest. The visual features or parameters can be used in a model or classifier (e.g., a machine-learned classifier) to estimate metrics associated with the physiological state of a patient, including for the presence or non-presence of a disease, medical condition, or an indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases or conditions or in the treatment of said diseases or conditions.
A61B 5/24 - Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
9.
Methods and Systems for Engineering Cardiac Waveform Features From Biophysical Signals for Use in Characterizing Physiological Systems
A clinical evaluation system and method are disclosed that facilitate the use of one or more morphologic atrial depolarization waveform-based features or parameters determined from biophysical signals such as cardiac or biopotential signals that are acquired, in preferred embodiments, non-invasively from surface sensors placed on a patient while the patient is at rest. Morphologic atrial depolarization waveform-based features or parameters can be used in a model or classifier to estimate metrics associated with the physiological state of a patient, including the presence or non-presence of a disease, medical condition, or an indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases or conditions or in the treatment of said diseases or conditions.
The exemplified methods and systems facilitate the use for diagnostics, monitoring, treatment of one or more wavelet-based features or parameters determined from biophysical signals such as cardiac/biopotential signals and/or photoplethysmography signals that are acquired non-invasively. The wavelet-based features or parameters can be used, in one embodiment, within a model or classifier (e.g., a machine-learned classifier) to estimate metrics associated with the physiological state of a subject, including for the presence or non-presence of a disease or abnormal condition. Wavelet-based features or parameters may include measures that are derived from extractable properties or geometric characteristics of a spectral image or data of high-power spectral contents or high-coherence in waveform signals of interest in an acquired biophysical signal. Wavelet-based features or parameters may also include measures that are derived from a statistical quantification of the distribution of the power of the high-power spectral contents in the waveform signals of interest.
A61B 5/318 - Heart-related electrical modalities, e.g. electrocardiography [ECG]
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
11.
METHODS AND SYSTEMS FOR ENGINEERING CONDUCTION DEVIATION FEATURES FROM BIOPHYSICAL SIGNALS FOR USE IN CHARACTERIZING PHYSIOLOGICAL SYSTEMS
A clinical evaluation system and method are disclosed that facilitate the use of one or more conduction deviation features or parameters determined from biophysical signals such as cardiac or biopotentials signals. Conduction derivation features or parameters may include VD conduction derivation features or parameters and/or VD conduction derivation Poincaré features or parameters. The conduction derivation features or parameters can be used in a model or classifier (e.g., a machine-learned classifier) to estimate metrics associated with the physiological state of a patient, including for the presence or non-presence of a disease, a medical condition, or an indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases or conditions or in the treatment of said diseases or conditions.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/349 - Detecting specific parameters of the electrocardiograph cycle
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
12.
Method and System to Non-Invasively Assess Elevated Left Ventricular End-Diastolic Pressure
A clinical evaluation system and method are disclosed that facilitate the use of features or parameters extracted from biophysical signals in a model or classifier (e.g., a machine-learned classifier) to estimate metrics associated with the physiological state of a patient, including for the presence or non-presence of elevated left ventricular end-diastolic pressure (elevated LVEDP), as an example indicator of a disease medical condition that could be assessed by using the system and method described herein.
The exemplified methods and systems facilitate the use for diagnostics, monitoring, treatment of one or more wavelet-based features or parameters determined from biophysical signals such as cardiac/biopotential signals and/or photoplethysmography signals that are acquired non-invasively. The wavelet-based features or parameters can be used, in one embodiment, within a model or classifier (e.g., a machine-learned classifier) to estimate metrics associated with the physiological state of a subject, including for the presence or non-presence of a disease or abnormal condition. Wavelet-based features or parameters may include measures that are derived from extractable properties or geometric characteristics of a spectral image or data of high-power spectral contents or high-coherence in waveform signals of interest in an acquired biophysical signal. Wavelet-based features or parameters may also include measures that are derived from a statistical quantification of the distribution of the power of the high-power spectral contents in the waveform signals of interest.
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/318 - Heart-related electrical modalities, e.g. electrocardiography [ECG]
The exemplified methods and systems facilitate the use, for diagnostics, monitoring, or treatment, of one or more PPG waveform-based features or parameters determined from biophysical signals such as photoplethysmography signals that are acquired non-invasively from surface sensors placed on a patient while the patient is at rest. PPG waveform-based features or parameters may include PPG waveform features or parameters, VPG waveform features or parameters, and/or APG waveform features or parameters. The PPG waveform-based features or parameters can be used in a model or classifier to estimate metrics associated with the physiological state of a patient, including the presence or non-presence of a disease, medical condition, or an indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases or conditions or in the treatment of said diseases or conditions.
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/318 - Heart-related electrical modalities, e.g. electrocardiography [ECG]
A clinical evaluation system and method are disclosed that facilitate the use of one or more visual features or parameters determined from biophysical signals such as cardiac/biopotential signals and/or photoplethysmography signals that are acquired, in preferred embodiments, non-invasively from surface sensors placed on a patient while the patient is at rest. The visual features or parameters can be used in a model or classifier (e.g., a machine-learned classifier) to estimate metrics associated with the physiological state of a patient, including for the presence or non-presence of a disease, medical condition, or an indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases or conditions or in the treatment of said diseases or conditions.
A61B 5/318 - Heart-related electrical modalities, e.g. electrocardiography [ECG]
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
16.
METHODS AND SYSTEMS FOR ENGINEERING PHOTOPLETHYSMOGRAPHIC-WAVEFORM FEATURES FROM BIOPHYSICAL SIGNALS FOR USE IN CHARACTERIZING PHYSIOLOGICAL SYSTEMS
The exemplified methods and systems facilitate the use, for diagnostics, monitoring, or treatment, of one or more PPG waveform-based features or parameters determined from biophysical signals such as photoplethysmography signals that are acquired non-invasively from surface sensors placed on a patient while the patient is at rest. PPG waveform-based features or parameters may include PPG waveform features or parameters, VPG waveform features or parameters, and/or APG waveform features or parameters. The PPG waveform-based features or parameters can be used in a model or classifier to estimate metrics associated with the physiological state of a patient, including the presence or non-presence of a disease, medical condition, or an indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases or conditions or in the treatment of said diseases or conditions.
A61B 5/318 - Heart-related electrical modalities, e.g. electrocardiography [ECG]
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
17.
METHODS AND SYSTEMS FOR ENGINEERING CARDIAC WAVEFORM FEATURES FROM BIOPHYSICAL SIGNALS FOR USE IN CHARACTERIZING PHYSIOLOGICAL SYSTEMS
A clinical evaluation system and method are disclosed that facilitate the use of one or more morphologic atrial depolarization waveform-based features or parameters determined from biophysical signals such as cardiac or biopotential signals that are acquired, in preferred embodiments, non-invasively from surface sensors placed on a patient while the patient is at rest. Morphologic atrial depolarization waveform-based features or parameters can be used in a model or classifier to estimate metrics associated with the physiological state of a patient, including the presence or non-presence of a disease, medical condition, or an indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases or conditions or in the treatment of said diseases or conditions.
A61B 5/318 - Heart-related electrical modalities, e.g. electrocardiography [ECG]
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
18.
METHODS AND SYSTEMS FOR ENGINEERING CONDUCTION DEVIATION FEATURES FROM BIOPHYSICAL SIGNALS FOR USE IN CHARACTERIZING PHYSIOLOGICAL SYSTEMS
A clinical evaluation system and method are disclosed that facilitate the use of one or more conduction deviation features or parameters determined from biophysical signals such as cardiac or biopotentials signals. Conduction derivation features or parameters may include VD conduction derivation features or parameters and/or VD conduction derivation Poincaré features or parameters. The conduction derivation features or parameters can be used in a model or classifier (e.g., a machine-learned classifier) to estimate metrics associated with the physiological state of a patient, including for the presence or non-presence of a disease, a medical condition, or an indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases or conditions or in the treatment of said diseases or conditions.
A61B 5/318 - Heart-related electrical modalities, e.g. electrocardiography [ECG]
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
19.
METHOD AND SYSTEM TO NON-INVASIVELY ASSESS ELEVATED LEFT VENTRICULAR END-DIASTOLIC PRESSURE
A clinical evaluation system and method are disclosed that facilitate the use of features or parameters extracted from biophysical signals in a model or classifier (e.g., a machine-learned classifier) to estimate metrics associated with the physiological state of a patient, including for the presence or non-presence of elevated left ventricular end-diastolic pressure (elevated LVEDP), as an example indicator of a disease medical condition that could be assessed by using the system and method described herein.
A61B 5/318 - Heart-related electrical modalities, e.g. electrocardiography [ECG]
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
20.
METHODS AND SYSTEMS FOR ENGINEERING RESPIRATION RATE-RELATED FEATURES FROM BIOPHYSICAL SIGNALS FOR USE IN CHARACTERIZING PHYSIOLOGICAL SYSTEMS
The exemplified methods and systems (e.g., machine-learned systems) facilitate the use of respiration rate-related features, or parameters, in a model or classifier to estimate metrics associated with the physiological state of a subject, including for the presence or non-presence of a disease, medical condition, or indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases, medical conditions, or indication of either or in the treatment of said diseases or indicating conditions. In some cases, such respiration rate-related features are generated from a synthetic respiration waveform that represents, and is used as a proxy to, the true respiration waveform. The synthetic respiration waveform may be used in its own independent diagnostic and/or control applications in some embodiments.
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/318 - Heart-related electrical modalities, e.g. electrocardiography [ECG]
A clinical evaluation system and method are disclosed that facilitate the use of one or more visual features or parameters determined from biophysical signals such as cardiac/biopotential signals and/or photoplethysmography signals that are acquired, in preferred embodiments, non-invasively from surface sensors placed on a patient while the patient is at rest. The visual features or parameters can be used in a model or classifier (e.g., a machine-learned classifier) to estimate metrics associated with the physiological state of a patient, including for the presence or non-presence of a disease, medical condition, or an indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases or conditions or in the treatment of said diseases or conditions.
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/318 - Heart-related electrical modalities, e.g. electrocardiography [ECG]
A clinical evaluation system and method are disclosed that facilitate the use of features or parameters extracted from biophysical signals in a model or classifier (e.g., a machine-learned classifier) to estimate metrics associated with the physiological state of a patient, including for the presence or non-presence of elevated left ventricular end-diastolic pressure (elevated LVEDP), as an example indicator of a disease medical condition that could be assessed by using the system and method described herein.
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/318 - Heart-related electrical modalities, e.g. electrocardiography [ECG]
A clinical evaluation system and method are disclosed that facilitate the use of one or more morphologic atrial depolarization waveform-based features or parameters determined from biophysical signals such as cardiac or biopotential signals that are acquired, in preferred embodiments, non-invasively from surface sensors placed on a patient while the patient is at rest. Morphologic atrial depolarization waveform-based features or parameters can be used in a model or classifier to estimate metrics associated with the physiological state of a patient, including the presence or non-presence of a disease, medical condition, or an indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases or conditions or in the treatment of said diseases or conditions.
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/318 - Heart-related electrical modalities, e.g. electrocardiography [ECG]
The exemplified methods and systems facilitate the use, for diagnostics, monitoring, treatment, of one or more power spectral-based features or parameters determined from biophysical signals such as cardiac/biopotential signals and/or photoplethysmography signals that are acquired non-invasively from surface sensors placed on a patient while the patient is at rest. The power spectral-based features or parameters can be used in a model or classifier (e.g., a machine-learned classifier) to estimate metrics associated with the physiological state of a patient, including for the presence or non-presence of a disease, medical condition, or an indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases or conditions or in the treatment of said diseases or conditions.
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/318 - Heart-related electrical modalities, e.g. electrocardiography [ECG]
A clinical evaluation system and method are disclosed that facilitate the use of one or more conduction deviation features or parameters determined from biophysical signals such as cardiac or biopotentials signals. Conduction derivation features or parameters may include VD conduction derivation features or parameters and/or VD conduction derivation Poincaré features or parameters. The conduction derivation features or parameters can be used in a model or classifier (e.g., a machine-learned classifier) to estimate metrics associated with the physiological state of a patient, including for the presence or non-presence of a disease, a medical condition, or an indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases or conditions or in the treatment of said diseases or conditions.
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/318 - Heart-related electrical modalities, e.g. electrocardiography [ECG]
The exemplified methods and systems facilitate the use, for diagnostics, monitoring, treatment, of one or more power spectral-based features or parameters determined from biophysical signals such as cardiac/biopotential signals and/or photoplethysmography signals that are acquired non-invasively from surface sensors placed on a patient while the patient is at rest. The power spectral-based features or parameters can be used in a model or classifier (e.g., a machine-learned classifier) to estimate metrics associated with the physiological state of a patient, including for the presence or non-presence of a disease, medical condition, or an indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases or conditions or in the treatment of said diseases or conditions.
A61B 5/318 - Heart-related electrical modalities, e.g. electrocardiography [ECG]
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
27.
METHODS AND SYSTEMS FOR ENGINEERING RESPIRATION RATE-RELATED FEATURES FROM BIOPHYSICAL SIGNALS FOR USE IN CHARACTERIZING PHYSIOLOGICAL SYSTEMS
The exemplified methods and systems (e.g., machine-learned systems) facilitate the use of respiration rate-related features, or parameters, in a model or classifier to estimate metrics associated with the physiological state of a subject, including for the presence or non-presence of a disease, medical condition, or indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases, medical conditions, or indication of either or in the treatment of said diseases or indicating conditions. In some cases, such respiration rate-related features are generated from a synthetic respiration waveform that represents, and is used as a proxy to, the true respiration waveform. The synthetic respiration waveform may be used in its own independent diagnostic and/or control applications in some embodiments.
A61B 5/318 - Heart-related electrical modalities, e.g. electrocardiography [ECG]
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
28.
METHODS AND SYSTEMS FOR ENGINEERING WAVELET-BASED FEATURES FROM BIOPHYSICAL SIGNALS FOR USE IN CHARACTERIZING PHYSIOLOGICAL SYSTEMS
The exemplified methods and systems facilitate the use for diagnostics, monitoring, treatment of one or more wavelet-based features or parameters determined from biophysical signals such as cardiac/biopotential signals and/or photoplethysmography signals that are acquired non-invasively. The wavelet-based features or parameters can be used, in one embodiment, within a model or classifier (e.g., a machine-learned classifier) to estimate metrics associated with the physiological state of a subject, including for the presence or non-presence of a disease or abnormal condition. Wavelet-based features or parameters may include measures that are derived from extractable properties or geometric characteristics of a spectral image or data of high-power spectral contents or high-coherence in waveform signals of interest in an acquired biophysical signal. Wavelet-based features or parameters may also include measures that are derived from a statistical quantification of the distribution of the power of the high-power spectral contents in the waveform signals of interest.
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/318 - Heart-related electrical modalities, e.g. electrocardiography [ECG]
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/11 - Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
A system is provided that receives a signal file that includes multiple biophysical signals obtained from a patient by a signal capture or recorder device. The biophysical signals are measured from one or more sensors or probes of the signal capture device. The system executes one or more add-on modules that is each configured to generate information relevant to the health of the patient. Such information may include a score that in some embodiments represents a probability that the patient has and/or will develop a particular medical condition. The information generated for a patient from the signal file for each add-on module are provided to a health care provider and may be used to assist the healthcare provider in diagnosing the patient with respect to one or more of the medical conditions.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
The exemplified methods and systems facilitate the use, for diagnostics, monitoring, or treatment, of one or more PPG waveform-based features or parameters determined from biophysical signals such as photoplethysmography signals that are acquired non-invasively from surface sensors placed on a patient while the patient is at rest. PPG waveform-based features or parameters may include PPG waveform features or parameters, VPG waveform features or parameters, and/or APG waveform features or parameters. The PPG waveform-based features or parameters can be used in a model or classifier to estimate metrics associated with the physiological state of a patient, including the presence or non-presence of a disease, medical condition, or an indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases or conditions or in the treatment of said diseases or conditions.
A61B 5/1455 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using optical sensors, e.g. spectral photometrical oximeters
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
31.
MEDICAL EVALUATION SYSTEMS AND METHODS USING ADD-ON MODULES
A system is provided that receives a signal file that includes multiple biophysical signals obtained from a patient by a signal capture or recorder device. The biophysical signals are measured from one or more sensors or probes of the signal capture device. The system executes one or more add-on modules that is each configured to generate information relevant to the health of the patient. Such information may include a score that in some embodiments represents a probability that the patient has and/or will develop a particular medical condition. The information generated for a patient from the signal file for each add-on module are provided to a health care provider and may be used to assist the healthcare provider in diagnosing the patient with respect to one or more of the medical conditions.
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
32.
MEDICAL EVALUATION SYSTEMS AND METHODS USING ADD-ON MODULES
A system is provided that receives a signal file that includes multiple biophysical signals obtained from a patient by a signal capture or recorder device. The biophysical signals are measured from one or more sensors or probes of the signal capture device. The system executes one or more add-on modules that is each configured to generate information relevant to the health of the patient. Such information may include a score that in some embodiments represents a probability that the patient has and/or will develop a particular medical condition. The information generated for a patient from the signal file for each add-on module are provided to a health care provider and may be used to assist the healthcare provider in diagnosing the patient with respect to one or more of the medical conditions.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
The exemplified methods and systems facilitate one or more dynamical analyses that can characterize and identify synchronicity between acquired cardiac signals and photoplethysmographic signals to predict/estimate presence, non-presence, localization, and/or severity of abnormal cardiovascular conditions or disease, including, for example, but not limited to, coronary artery disease, heart failure (including but not limited to indicators of disease or conduction such as abnormal left ventricular end-diastolic pressure disease), and pulmonary hypertension, among others. In some embodiments, statistical properties of the synchronicity between cardiac signals and photoplethysmographic signals are evaluated. In some embodiments, statistical properties of histogram of synchronicity between cardiac signals and photoplethysmographic signals are evaluated. In some embodiments, statistical and/or geometric properties of Poincaré map of synchronicity between cardiac signals and photoplethysmographic signals are evaluated.
A61B 5/1455 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using optical sensors, e.g. spectral photometrical oximeters
A61B 5/021 - Measuring pressure in heart or blood vessels
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/349 - Detecting specific parameters of the electrocardiograph cycle
A61B 5/318 - Heart-related electrical modalities, e.g. electrocardiography [ECG]
A61B 5/0205 - Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
A61B 5/0245 - Measuring pulse rate or heart rate using sensing means generating electric signals
34.
METHOD AND SYSTEM FOR ENGINEERING CYCLE VARIABILITY-RELATED FEATURES FROM BIOPHYSICAL SIGNALS FOR USE IN CHARACTERIZING PHYSIOLOGICAL SYSTEMS
The exemplified methods and systems facilitate the use, for diagnostics, monitoring, or treatment, of one or more cycle variability based features or parameters determined from biophysical signals such as cardiac or photoplethysmography signals that are acquired non-invasively from surface sensors placed on a patient while the patient is at rest. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases or conditions or in the treatment of said diseases or conditions.
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/02 - Measuring pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography; Heart catheters for measuring blood pressure
A61B 5/24 - Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
35.
METHOD AND SYSTEM FOR ENGINEERING CYCLE VARIABILITY-RELATED FEATURES FROM BIOPHYSICAL SIGNALS FOR USE IN CHARACTERIZING PHYSIOLOGICAL SYSTEMS
The exemplified methods and systems facilitate the use, for diagnostics, monitoring, or treatment, of one or more cycle variability based features or parameters determined from biophysical signals such as cardiac or photoplethysmography signals that are acquired non-invasively from surface sensors placed on a patient while the patient is at rest. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases or conditions or in the treatment of said diseases or conditions.
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/24 - Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
A61B 5/02 - Measuring pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography; Heart catheters for measuring blood pressure
36.
METHOD AND SYSTEM FOR ENGINEERING CYCLE VARIABILITY-RELATED FEATURES FROM BIOPHYSICAL SIGNALS FOR USE IN CHARACTERIZING PHYSIOLOGICAL SYSTEMS
The exemplified methods and systems facilitate the use, for diagnostics, monitoring, or treatment, of one or more cycle variability based features or parameters determined from biophysical signals such as cardiac or photoplethysmography signals that are acquired non-invasively from surface sensors placed on a patient while the patient is at rest. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases or conditions or in the treatment of said diseases or conditions.
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/021 - Measuring pressure in heart or blood vessels
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
37.
METHODS AND SYSTEMS TO QUANTIFY AND REMOVE ASYNCHRONOUS NOISE IN BIOPHYSICAL SIGNALS
The exemplified methods and systems described herein facilitate the quantification and/or removal of asynchronous noise, such as muscle artifact noise contamination, to more accurately assess complex nonlinear variabilities in quasi-periodic biophysical-signal systems such as those in acquired cardiac signals, brain signals, etc.
The present disclosure facilitates capture (e.g., bipolar capture) of differentially-acquired wide-band phase gradient signals (e.g., wide-band cardiac phase gradient signals, wide-band cerebral phase gradient signals) that are simultaneously sampled. Notably, the exemplified system minimizes non-linear distortions (e.g., those that can be introduced via certain filters such as phase distortions) in the acquired wide-band phase gradient signals so as to not affect the information therein that can non-deterministically affect analysis of the wide-band phase gradient signal in the phase space domain. Further, a shield drive circuit and shield-drive voltage plane may be used to facilitate low noise and low interference operation of the acquisition system.
The exemplified methods and systems provide a phase space volumetric object in which the dynamics of a complex, quasi-periodic system, such as the electrical conduction patterns of the heart, or other biophysical-acquired signals of other organs, are represented as an image of a three dimensional volume having both a volumetric structure (e.g., a three dimensional structure) and a color map to which features can be extracted that are indicative the presence and/or absence of pathologies, e.g., ischemia relating to significant coronary arterial disease (CAD). In some embodiments, the phase space volumetric object can be assessed to extract topographic and geometric parameters that are used in models that determine indications of presence or non-presence of significant coronary artery disease.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/02 - Measuring pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography; Heart catheters for measuring blood pressure
A61B 5/0265 - Measuring blood flow using electromagnetic means, e.g. electromagnetic flow meter
09 - Scientific and electric apparatus and instruments
10 - Medical apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable and/or recorded computer software for
accessing, analyzing and displaying patient data for use in
the field of health care; downloadable and/or recorded
computer software for accessing, analyzing and displaying
patient data for use in the field of cardiology;
computer-based platforms embedded in a device or medical
apparatus for processing data representative of intrinsic
physiologic signals of a patient and for displaying data
related to such processed signals for use in assessing the
function and/or anatomical state of the patient's tissue or
the health state of the patient. Medical devices, apparatus and instruments, namely, signal
acquisition recorders for acquiring, processing and
transmitting intrinsic physiologic signals of a patient, in
particular, signals for use in assessing the function and/or
anatomical state of the patient's tissue or the health state
of the patient. Software as a service (SAAS) services featuring software for
accessing, analyzing and displaying patient health data for
use in the field of health care; software as a service
(SAAS) services featuring software for accessing, analyzing
and displaying patient data for use in the field of
cardiology; software as a service (SAAS) services featuring
software for the collection and processing of resting phase
biological signals through algorithms, machine-learning
technology, and modeling techniques for diagnostic and
predictive medical applications; providing a web site
featuring temporary use of non-downloadable software for
accessing, analyzing and displaying patient health data for
use in the field of health care (term considered too vague
by the International Bureau - Rule 13 (2) (b) of the
Regulations); providing a web site featuring temporary use
of non-downloadable software for accessing, analyzing and
displaying patient data for use in the field of cardiology
(term considered too vague by the International Bureau -
Rule 13 (2) (b) of the Regulations); providing temporary use
of on-line non-downloadable software and applications for
accessing, analyzing and displaying patient health data for
use in the field of health care; providing temporary use of
on- line non-downloadable software and applications for
accessing, analyzing and displaying patient data for use in
the field of cardiology.
41.
METHOD AND SYSTEM TO ASSESS DISEASE USING MULTI-SENSOR SIGNALS
The exemplified methods and systems (e.g., machine-learned systems) facilitate the acquisition of ballistocardiographic signals and the determination and use of ballistocardiographic signal related features, or parameters, in a model or classifier to estimate metrics associated with the physiological state of a subject, including for the presence or non-presence of a disease, medical condition, or indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases, medical condition, or indication of either or in the treatment of said diseases or indicating conditions. In some embodiments, certain ballistocardiographic signals can also be used to remove motion artifacts from biophysical signals used for the estimation.
The exemplified methods and systems (e.g., machine-learned systems) facilitate the acquisition of ballistocardiographic signals and the determination and use of ballistocardiographic signal related features, or parameters, in a model or classifier to estimate metrics associated with the physiological state of a subject, including for the presence or non-presence of a disease, medical condition, or indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases, medical condition, or indication of either or in the treatment of said diseases or indicating conditions. In some embodiments, certain ballistocardiographic signals can also be used to remove motion artifacts from biophysical signals used for the estimation.
The exemplified methods and systems (e.g., machine-learned systems) facilitate the acquisition of ballistocardiographic signals and the determination and use of ballistocardiographic signal related features, or parameters, in a model or classifier to estimate metrics associated with the physiological state of a subject, including for the presence or non-presence of a disease, medical condition, or indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases, medical condition, or indication of either or in the treatment of said diseases or indicating conditions. In some embodiments, certain ballistocardiographic signals can also be used to remove motion artifacts from biophysical signals used for the estimation.
A facility for identifying combinations of feature and machine learning algorithm parameters, where each combination can be combined with one or more machine learning algorithms to train a model, is disclosed. The facility evaluates each genome based on the ability of a model trained using that genome and a machine learning algorithm to produce accurate results when applied to a validation data set by, for example, generating a fitness or validation score for the trained model and the corresponding genome used to train the model. Genomes that produce fitness scores that exceed a fitness threshold are selected for mutation, mutated, and the process is repeated. These trained models can then be applied to new data to generate predictions for the underlying subject matter.
G16B 40/00 - ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
G16B 5/00 - ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
A facility providing systems and methods for discovering novel features to use in machine learning techniques. The facility receives, for a number of subjects, one or more sets of data representative of some output or condition of the subject over a period of time or capturing some physical aspect of the subject. The facility then extracts or computes values from the data and applies one or more feature generators to the extracted values. Based on the outputs of the feature generators, the facility identifies novel feature generators for use in at least one machine learning process and further mutates the novel feature generators, which can then be applied to the received data to identify additional novel feature generators.
G16B 40/00 - ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
G06N 3/04 - Architecture, e.g. interconnection topology
The present disclosure facilitates the evaluation of wide-band phase gradient information of the heart tissue to assess, e.g., the presence of heart ischemic heart disease. Notably, the present disclosure provides an improved and efficient method to identify and risk stratify coronary stenosis of the heart using a high resolution and wide-band cardiac gradient obtained from the patient. The patient data are derived from the cardiac gradient waveforms across one or more leads, in some embodiments, resulting in high-dimensional data and long cardiac gradient records that exhibit complex nonlinear variability. Space-time analysis, via numeric wavelet operators, is used to study the morphology of the cardiac gradient data as a phase space dataset by extracting dynamical and geometrical properties from the phase space dataset.
A61B 5/316 - Modalities, i.e. specific diagnostic methods
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/02 - Measuring pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography; Heart catheters for measuring blood pressure
09 - Scientific and electric apparatus and instruments
10 - Medical apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Downloadable and/or recorded computer software for accessing, analyzing and displaying patient data for use in the field of health care; downloadable and/or recorded computer software for accessing, analyzing and displaying patient data for use in the field of cardiology; computer-based platforms embedded in a device or medical apparatus for processing data representative of intrinsic physiologic signals of a patient and for displaying data related to such processed signals for use in assessing the function and/or anatomical state of the patient's tissue or the health state of the patient.
(2) Medical devices, apparatus and instruments, namely, signal acquisition recorders for acquiring, processing and transmitting intrinsic physiologic signals of a patient, in particular, signals for use in assessing the function and/or anatomical state of the patient's tissue or the health state of the patient. (1) Software as a service (SAAS) services featuring software for accessing, analyzing and displaying patient health data for use in the field of health care; software as a service (SAAS) services featuring software for accessing, analyzing and displaying patient data for use in the field of cardiology; software as a service (SAAS) services featuring software for the collection and processing of resting phase biological signals through algorithms, machine-learning technology, and modeling techniques for diagnostic and predictive medical applications; providing a web site featuring temporary use of non-downloadable software for accessing, analyzing and displaying patient health data for use in the field of health care (term considered too vague by the International Bureau - Rule 13 (2) (b) of the Regulations); providing a web site featuring temporary use of non-downloadable software for accessing, analyzing and displaying patient data for use in the field of cardiology (term considered too vague by the International Bureau - Rule 13 (2) (b) of the Regulations); providing temporary use of on-line non-downloadable software and applications for accessing, analyzing and displaying patient health data for use in the field of health care; providing temporary use of on- line non-downloadable software and applications for accessing, analyzing and displaying patient data for use in the field of cardiology.
48.
METHOD AND SYSTEM FOR SIGNAL QUALITY ASSESSMENT AND REJECTION USING HEART CYCLE VARIABILITY
The exemplified methods and systems facilitate the quantification of cardiac cycle-variability as a metric of signal quality of an acquired signal data set and the rejection, based on that quantification, of said acquired signal data set from one or more subsequent analyses that can predict and/or estimate a metric associated with the presence, non-presence, severity, and/or localization of abnormal cardiovascular conditions or disease, including, for example, but not limited to, coronary artery disease, abnormal left ventricular end-diastolic pressure disease (LVEDP), pulmonary hypertension and subcategories thereof, heart failure (HF), among others as discussed herein. The quantification of levels of cycle-variability assessed noise such as skeletal-muscle-related-signal contamination and muscle-artifact-noise contamination, and other asynchronous-noise contamination in an acquired signal can be subsequently used for the automated rejection of such asynchronous noise from measurements of biophysical signals.
The exemplified methods and systems facilitate the quantification of cardiac cycle-variability as a metric of signal quality of an acquired signal data set and the rejection, based on that quantification, of said acquired signal data set from one or more subsequent analyses that can predict and/or estimate a metric associated with the presence, non-presence, severity, and/or localization of abnormal cardiovascular conditions or disease, including, for example, but not limited to, coronary artery disease, abnormal left ventricular end-diastolic pressure disease (LVEDP), pulmonary hypertension and subcategories thereof, heart failure (HF), among others as discussed herein. The quantification of levels of cycle-variability assessed noise such as skeletal-muscle-related-signal contamination and muscle-artifact-noise contamination, and other asynchronous-noise contamination in an acquired signal can be subsequently used for the automated rejection of such asynchronous noise from measurements of biophysical signals.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
A61B 5/318 - Heart-related electrical modalities, e.g. electrocardiography [ECG]
A61B 5/02 - Measuring pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography; Heart catheters for measuring blood pressure
A61B 5/0295 - Measuring blood flow using plethysmography, i.e. measuring the variations in the volume of a body part as modified by the circulation of blood therethrough, e.g. impedance plethysmography
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
50.
METHOD AND SYSTEM FOR SIGNAL QUALITY ASSESSMENT AND REJECTION USING HEART CYCLE VARIABILITY
The exemplified methods and systems facilitate the quantification of cardiac cycle-variability as a metric of signal quality of an acquired signal data set and the rejection, based on that quantification, of said acquired signal data set from one or more subsequent analyses that can predict and/or estimate a metric associated with the presence, non-presence, severity, and/or localization of abnormal cardiovascular conditions or disease, including, for example, but not limited to, coronary artery disease, abnormal left ventricular end-diastolic pressure disease (LVEDP), pulmonary hypertension and subcategories thereof, heart failure (HF), among others as discussed herein. The quantification of levels of cycle-variability assessed noise such as skeletal-muscle-related-signal contamination and muscle-artifact-noise contamination, and other asynchronous-noise contamination in an acquired signal can be subsequently used for the automated rejection of such asynchronous noise from measurements of biophysical signals.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/02 - Measuring pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography; Heart catheters for measuring blood pressure
A61B 5/0295 - Measuring blood flow using plethysmography, i.e. measuring the variations in the volume of a body part as modified by the circulation of blood therethrough, e.g. impedance plethysmography
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value
A61B 5/318 - Heart-related electrical modalities, e.g. electrocardiography [ECG]
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
51.
METHOD AND APPARATUS FOR WIDE-BAND PHASE GRADIENT SIGNAL ACQUISITION
The present disclosure facilitates capture of biosignal such as biopotential signals in microvolts, or sub-microvolts, resolutions that are at, or significantly below, the noise-floor of conventional electrocardiographic and biosignal acquisition instruments. In some embodiments, the exemplified system disclosed herein facilitates the acquisition and recording of wide-band phase gradient signals (e.g., wide-band cardiac phase gradient signals, wide-band cerebral phase gradient signals) that are simultaneously sampled, in some embodiments, having a temporal skew less than about 1 μs, and in other embodiments, having a temporal skew not more than about 10 femtoseconds. Notably, the exemplified system minimizes non-linear distortions (e.g., those that can be introduced via certain filters) in the acquired wide-band phase gradient signal so as to not affect the information therein.
A61B 5/1455 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using optical sensors, e.g. spectral photometrical oximeters
52.
Method and system for visualization of heart tissue at risk
Exemplified methods and systems facilitate presentation of data derived from measurements of the heart in a non-invasive procedure (e.g., via phase space tomography analysis). In particular, the exemplified methods and systems facilitate presentation of such measurements in a graphical user interface, or “GUI” (e.g., associated with a healthcare provider web portal to be used by physicians, researchers, or patients, and etc.) and/or in a report for diagnosis of heart pathologies and disease. The presentation facilitates a unified and intuitive visualization that includes three-dimensional visualizations and two-dimensional visualizations that are concurrently presented within a single interactive interface and/or report.
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G06T 19/20 - Editing of 3D images, e.g. changing shapes or colours, aligning objects or positioning parts
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
A61B 5/02 - Measuring pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography; Heart catheters for measuring blood pressure
A61B 6/00 - Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
G16H 30/00 - ICT specially adapted for the handling or processing of medical images
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer software for analyzing patient data for use in the
field of health care. Software as a service (saas) services featuring software for
analyzing patient data for use in the field of health care;
providing a web site featuring temporary use of
non-downloadable software for analyzing patient data for use
in the field of health care; providing temporary use of
on-line non-downloadable software and applications for
analyzing patient data for use in the field of health care.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer software for analyzing patient data for use in the
field of health care. Software as a service (saas) services featuring software for
analyzing patient data for use in the field of health care;
providing a web site featuring temporary use of
non-downloadable software for analyzing patient data for use
in the field of health care; providing temporary use of
on-line non-downloadable software and applications for
analyzing patient data for use in the field of health care.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer software for analyzing patient data for use in the
field of health care. Software as a service (saas) services featuring software for
analyzing patient data for use in the field of health care;
providing a web site featuring temporary use of
non-downloadable software for analyzing patient data for use
in the field of health care; providing temporary use of
on-line non-downloadable software and applications for
analyzing patient data for use in the field of health care.
56.
METHOD AND SYSTEM TO ASSESS DISEASE USING DYNAMICAL ANALYSIS OF BIOPHYSICAL SIGNALS
The exemplified methods and systems facilitate one or more dynamical analyses that can characterize and identify nonlinear dynamical properties (such as Lyapunov exponent (LE), correlation dimension, entropy (K2), or statistical and/or geometric properties derived from Poincaré maps, etc.) of biophysical signals such as photoplethysmographic signals and/or cardiac signals to predict presence and/or localization of a disease or condition, or indicator of one, including, for example, but not limited to, coronary artery disease, heart failure (including but not limited to elevated or abnormal left ventricular end-diastolic pressure disease) and pulmonary hypertension, among others.
The exemplified methods and systems facilitate one or more dynamical analyses that can characterize and identify synchronicity between the acquired cardiac signals and photoplethysmographic signals to predict/estimate the presence, non-presence, localization, and/or severity of abnormal cardiovascular conditions or disease, including, for example, but not limited to, coronary artery disease, heart failure (including but not limited to indicators of disease or conduction such as abnormal left ventricular end-diastolic pressure disease), and pulmonary hypertension, among others. In some embodiments, statistical properties of the synchronicity between the cardiac signals and photoplethysmographic signals are evaluated. In some embodiments, statistical properties of a histogram of the synchronicity between the cardiac signals and photoplethysmographic signals are evaluated. In some embodiments, statistical and/or geometric properties of a Poincaré map of synchronicity between the cardiac signals and photoplethysmographic signals are evaluated.
A61B 5/1455 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using optical sensors, e.g. spectral photometrical oximeters
A61B 5/021 - Measuring pressure in heart or blood vessels
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/349 - Detecting specific parameters of the electrocardiograph cycle
A61B 5/318 - Heart-related electrical modalities, e.g. electrocardiography [ECG]
A61B 5/0205 - Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
58.
METHOD AND SYSTEM TO ASSESS DISEASE USING DYNAMICAL ANALYSIS OF BIOPHYSICAL SIGNALS
The exemplified methods and systems facilitate one or more dynamical analyses that can characterize and identify nonlinear dynamical properties (such as Lyapunov exponent (LE), correlation dimension, entropy (K2), or statistical and/or geometric properties derived from Poincare maps, etc.) of biophysical signals such as photoplethysmographic signals and/or cardiac signals to predict presence and/or localization of a disease or condition, or indicator of one, including, for example, but not limited to, coronary artery disease, heart failure (including but not limited to elevated or abnormal left ventricular end-diastolic pressure disease) and pulmonary hypertension, among others.
A61B 5/02 - Measuring pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography; Heart catheters for measuring blood pressure
A61B 5/0295 - Measuring blood flow using plethysmography, i.e. measuring the variations in the volume of a body part as modified by the circulation of blood therethrough, e.g. impedance plethysmography
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
59.
METHOD AND SYSTEM TO ASSESS DISEASE USING DYNAMICAL ANALYSIS OF CARDIAC AND PHOTOPLETHYSMOGRAPHIC SIGNALS
The exemplified methods and systems facilitate one or more dynamical analyses that can characterize and identify synchronicity between acquired cardiac signals and photoplethysmographic signals to predict/estimate presence, non-presence, localization, and/or severity of abnormal cardiovascular conditions or disease, including, for example, but not limited to, coronary artery disease, heart failure (including but not limited to indicators of disease or conduction such as abnormal left ventricular end-diastolic pressure disease), and pulmonary hypertension, among others. In some embodiments, statistical properties of the synchronicity between cardiac signals and photoplethysmographic signals are evaluated. In some embodiments, statistical properties of histogram of synchronicity between cardiac signals and photoplethysmographic signals are evaluated. In some embodiments, statistical and/or geometric properties of Poincare map of synchronicity between cardiac signals and photoplethysmographic signals are evaluated.
A61B 5/02 - Measuring pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography; Heart catheters for measuring blood pressure
A61B 5/0295 - Measuring blood flow using plethysmography, i.e. measuring the variations in the volume of a body part as modified by the circulation of blood therethrough, e.g. impedance plethysmography
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
60.
METHOD AND SYSTEM TO ASSESS DISEASE USING DYNAMICAL ANALYSIS OF CARDIAC AND PHOTOPLETHYSMOGRAPHIC SIGNALS
The exemplified methods and systems facilitate one or more dynamical analyses that can characterize and identify synchronicity between acquired cardiac signals and photoplethysmographic signals to predict/estimate presence, non-presence, localization, and/or severity of abnormal cardiovascular conditions or disease, including, for example, but not limited to, coronary artery disease, heart failure (including but not limited to indicators of disease or conduction such as abnormal left ventricular end-diastolic pressure disease), and pulmonary hypertension, among others. In some embodiments, statistical properties of the synchronicity between cardiac signals and photoplethysmographic signals are evaluated. In some embodiments, statistical properties of histogram of synchronicity between cardiac signals and photoplethysmographic signals are evaluated. In some embodiments, statistical and/or geometric properties of Poincare map of synchronicity between cardiac signals and photoplethysmographic signals are evaluated.
A61B 5/02 - Measuring pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography; Heart catheters for measuring blood pressure
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/0295 - Measuring blood flow using plethysmography, i.e. measuring the variations in the volume of a body part as modified by the circulation of blood therethrough, e.g. impedance plethysmography
61.
METHOD AND SYSTEM TO ASSESS DISEASE USING DYNAMICAL ANALYSIS OF BIOPHYSICAL SIGNALS
The exemplified methods and systems facilitate one or more dynamical analyses that can characterize and identify nonlinear dynamical properties (such as Lyapunov exponent (LE), correlation dimension, entropy (K2), or statistical and/or geometric properties derived from Poincare maps, etc.) of biophysical signals such as photoplethysmographic signals and/or cardiac signals to predict presence and/or localization of a disease or condition, or indicator of one, including, for example, but not limited to, coronary artery disease, heart failure (including but not limited to elevated or abnormal left ventricular end-diastolic pressure disease) and pulmonary hypertension, among others.
A61B 5/02 - Measuring pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography; Heart catheters for measuring blood pressure
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/0295 - Measuring blood flow using plethysmography, i.e. measuring the variations in the volume of a body part as modified by the circulation of blood therethrough, e.g. impedance plethysmography
09 - Scientific and electric apparatus and instruments
10 - Medical apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable and/or recorded computer software for accessing, analyzing and displaying patient data for use in the field of health care; Downloadable and/or recorded computer software for accessing, analyzing and displaying patient data for use in the field of cardiology Medical devices, apparatus and instruments, namely, signal acquisition recorders for acquiring, processing and transmitting intrinsic physiologic signals of a patient, in particular, signals for use in assessing the function and/or anatomical state of the patient's tissue or the health state of the patient; Computer-based platforms embedded in a device or medical apparatus for processing data representative of intrinsic physiologic signals of a patient and for displaying data related to such processed signals for use in assessing the function and/or anatomical state of the patient's tissue or the health state of the patient Software as a service (SAAS) services featuring software for accessing, analyzing and displaying patient health data for use in the field of health care; Software as a service (SAAS) services featuring software for accessing, analyzing and displaying patient data for use in the field of cardiology; Software as a service (SAAS) services featuring software for the collection and processing of resting phase biological signals through algorithms, machine-learning technology, and modeling techniques for diagnostic and predictive medical applications; Providing a web site featuring temporary use of non-downloadable software for accessing, analyzing and displaying patient health data for use in the field of health care; Providing a web site featuring temporary use of non-downloadable software for accessing, analyzing and displaying patient data for use in the field of cardiology; Providing temporary use of on-line non-downloadable software and applications for accessing, analyzing and displaying patient health data for use in the field of health care; Providing temporary use of on- line non-downloadable software and applications for accessing, analyzing and displaying patient data for use in the field of cardiology
63.
Methods and systems using mathematical analysis and machine learning to diagnose disease
Exemplified method and system facilitates monitoring and/or evaluation of disease or physiological state using mathematical analysis and machine learning analysis of a biopotential signal collected from a single electrode. The exemplified method and system creates, from data of a singularly measured biopotential signal, via a mathematical operation (i.e., via numeric fractional derivative calculation of the signal in the frequency domain), one or more mathematically-derived biopotential signals (e.g., virtual biopotential signals) that is used in combination with the measured biopotential signals to generate a multi-dimensional phase-space representation of the body (e.g., the heart). By mathematically modulating (e.g., by expanding or contracting) portions of a given biopotential signal, in the frequency domain, the numeric-based operation gives emphasis or de-emphasis to certain measured frequencies of the biopotential signals, which, when coupled with machine learning, facilitates improved diagnostics of certain pathologies.
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/24 - Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
A61B 5/316 - Modalities, i.e. specific diagnostic methods
A61B 5/02 - Measuring pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography; Heart catheters for measuring blood pressure
The present disclosure facilitates capture of biosignal such as biopotential signals in microvolts, or sub-microvolts, resolutions that are at, or significantly below, the noise-floor of conventional electrocardiographic and biosignal acquisition instruments. In some embodiments, the exemplified system disclosed herein facilitates the acquisition and recording of wide-band phase gradient signals (e.g., wide-band cardiac phase gradient signals, wide-band cerebral phase gradient signals) that are simultaneously sampled, in some embodiments, having a temporal skew less than about 1 μs, and in other embodiments, having a temporal skew not more than about 10 femtoseconds. Notably, the exemplified system minimizes non-linear distortions (e.g., those that can be introduced via certain filters) in the acquired wide-band phase gradient signal so as to not affect the information therein.
A61B 5/1455 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using optical sensors, e.g. spectral photometrical oximeters
Systems and methods for the quantification of the quality of an acquired signal are provided for assessment and for gating the acquired signal for subsequent analysis. A signal is acquired, and a determination is made in real-time if there is a problem with the acquisition (e.g., if the acquired signal is acceptable or unacceptable; is of sufficient quality for subsequent assessment). If there is a problem, output is provided via the systems and methods described herein to indicate that signal acquisition needs to be performed again (e.g., if the acquired signal is unacceptable, reject the acquired signal and acquire a new signal).
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/24 - Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
A61B 5/308 - Input circuits therefor specially adapted for particular uses for electrocardiography [ECG]
The exemplified methods and systems employs non-invasively acquired biophysical measurements of a subject in a residue analysis that is structured as a three-dimensional volumetric object to which machine extractable features associated with a geometric associated aspect of the three-dimensional volumetric object may be derived and used for in the training and/or prediction of a disease state. The system generates a residue model from a point-cloud residue generated from an analysis of the plurality of biophysical signal data sets. The system generates a three-dimensional volumetric object from the point-cloud residue from which machine extractable features associated with the point-cloud residue maybe extracted.
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/02 - Measuring pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography; Heart catheters for measuring blood pressure
68.
METHOD AND SYSTEM FOR AUTOMATED QUANTIFICATION OF SIGNAL QUALITY
Systems and methods for the quantification of the quality of an acquired signal are provided for assessment and for gating the acquired signal for subsequent analysis. A signal is acquired, and a determination is made in real-time if there is a problem with the acquisition (e.g., if the acquired signal is acceptable or unacceptable; is of sufficient quality for subsequent assessment). If there is a problem, output is provided via the systems and methods described herein to indicate that signal acquisition needs to be performed again (e.g., if the acquired signal is unacceptable, reject the acquired signal and acquire a new signal).
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
69.
Methods and systems to configure and use neural networks in characterizing physiological systems
The exemplified methods and systems facilitate the configuration and training of a neural network (e.g., a deep neural network, a convolutional neural network (CNN), etc.), or ensemble(s) thereof, with a biophysical signal data set to ascertain estimate for the presence or non-presence of disease or pathology in a subject as well as to assess and/or classify disease or pathology, including for example in some cases the severity of such disease or pathology, in a subject. In the context of the heart, the methods and systems described herein facilitate the configuration and training of a neural network, or ensemble(s) thereof, with a cardiac signal data set to ascertain estimate for the presence or non-presence of coronary artery disease or coronary pathology.
Systems and methods for the quantification of the quality of an acquired signal are provided for assessment and for gating the acquired signal for subsequent analysis. A signal is acquired, and a determination is made in real-time if there is a problem with the acquisition (e.g., if the acquired signal is acceptable or unacceptable; is of sufficient quality for subsequent assessment). If there is a problem, output is provided via the systems and methods described herein to indicate that signal acquisition needs to be performed again (e.g., if the acquired signal is unacceptable, reject the acquired signal and acquire a new signal).
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
71.
METHODS AND SYSTEMS TO CONFIGURE AND USE NEURAL NETWORKS IN CHARACTERIZING PHYSIOLOGICAL SYSTEMS
The exemplified methods and systems facilitate the configuration and training of a neural network (e.g., a deep neural network, a convolutional neural network (CNN), etc.), or ensemble(s) thereof, with a biophysical signal data set to ascertain estimate for the presence or non-presence of disease or pathology in a subject as well as to assess and/or classify disease or pathology, including for example in some cases the severity of such disease or pathology, in a subject. In the context of the heart, the methods and systems described herein facilitate the configuration and training of a neural network, or ensemble(s) thereof, with a cardiac signal data set to ascertain estimate for the presence or non-presence of coronary artery disease or coronary pathology.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/243 - Detecting biomagnetic fields, e.g. magnetic fields produced by bioelectric currents specially adapted for magnetocardiographic [MCG] signals
A61B 5/318 - Heart-related electrical modalities, e.g. electrocardiography [ECG]
A61B 5/364 - Detecting abnormal ECG interval, e.g. extrasystoles or ectopic heartbeats
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
The exemplified methods and systems employs non-invasively acquired biophysical measurements of a subject in a residue analysis that is structured as a three-dimensional volumetric object to which machine extractable features associated with a geometric associated aspect of the three-dimensional volumetric object may be derived and used for in the training and/or prediction of a disease state. The system generates a residue model from a point-cloud residue generated from an analysis of the plurality of biophysical signal data sets. The system generates a three-dimensional volumetric object from the point-cloud residue from which machine extractable features associated with the point-cloud residue maybe extracted.
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
A61B 5/02 - Measuring pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography; Heart catheters for measuring blood pressure
A61B 5/04 - Measuring bioelectric signals of the body or parts thereof
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
73.
METHODS AND SYSTEMS TO CONFIGURE AND USE NEURAL NETWORKS IN CHARACTERIZING PHYSIOLOGICAL SYSTEMS
The exemplified methods and systems facilitate the configuration and training of a neural network (e.g., a deep neural network, a convolutional neural network (CNN), etc.), or ensemble(s) thereof, with a biophysical signal data set to ascertain estimate for the presence or non-presence of disease or pathology in a subject as well as to assess and/or classify disease or pathology, including for example in some cases the severity of such disease or pathology, in a subject. In the context of the heart, the methods and systems described herein facilitate the configuration and training of a neural network, or ensemble(s) thereof, with a cardiac signal data set to ascertain estimate for the presence or non-presence of coronary artery disease or coronary pathology.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/04 - Measuring bioelectric signals of the body or parts thereof
The present disclosure facilitates the evaluation of wide-band phase gradient information of the heart tissue to assess, e.g., the presence of heart ischemic heart disease. Notably, the present disclosure provides an improved and efficient method to identify and risk stratify coronary stenosis of the heart using a high resolution and wide-band cardiac gradient obtained from the patient. The patient data are derived from the cardiac gradient waveforms across one or more leads, in some embodiments, resulting in high-dimensional data and long cardiac gradient records that exhibit complex nonlinear variability. Space-time analysis, via numeric wavelet operators, is used to study the morphology of the cardiac gradient data as a phase space dataset by extracting dynamical and geometrical properties from the phase space dataset.
A61B 5/316 - Modalities, i.e. specific diagnostic methods
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/02 - Measuring pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography; Heart catheters for measuring blood pressure
A61B 5/1455 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using optical sensors, e.g. spectral photometrical oximeters
76.
SYSTEM FOR CHARACTERIZING CARDIOVASCULAR SYSTEMS FROM SINGLE CHANNEL DATA
Systems to identify and risk stratify disease states, cardiac structural defects, functional cardiac deficiencies induced by teratogens and other toxic agents, pathological substrates, conduction delays and defects, and ejection fraction using single channel biological data obtained from the subject. A modified Matching Pursuit (MP) algorithm may be used to find a noiseless model of the data that is sparse and does not assume periodicity of the signal. After the model is derived, various metrics and subspaces are extracted to characterize the cardiac system. In another system, space-time domain is divided into a number of regions (which is largely determined by the signal length), the density of the signal is computed in each region and input to a learning algorithm to associate them to the desired cardiac dysfunction indicator target.
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/02 - Measuring pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography; Heart catheters for measuring blood pressure
A61B 5/04 - Measuring bioelectric signals of the body or parts thereof
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
The exemplified methods and systems described herein facilitate the quantification and/or removal of asynchronous noise, such as muscle artifact noise contamination, to more accurately assess complex nonlinear variabilities in quasi-periodic biophysical-signal systems such as those in acquired cardiac signals, brain signals, etc.
A61B 5/316 - Modalities, i.e. specific diagnostic methods
A61B 5/242 - Detecting biomagnetic fields, e.g. magnetic fields produced by bioelectric currents
A61B 5/02 - Measuring pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography; Heart catheters for measuring blood pressure
78.
METHODS AND SYSTEMS TO QUANTIFY AND REMOVE ASYNCHRONOUS NOISE IN BIOPHYSICAL SIGNALS
The exemplified methods and systems described herein facilitate the quantification and/or removal of asynchronous noise, such as muscle artifact noise contamination, to more accurately assess complex nonlinear variabilities in quasi-periodic biophysical-signal systems such as those in acquired cardiac signals, brain signals, etc.
The exemplified methods and systems described herein facilitate the quantification and/or removal of asynchronous noise, such as muscle artifact noise contamination, to more accurately assess complex nonlinear variabilities in quasi-periodic biophysical-signal systems such as those in acquired cardiac signals, brain signals, etc.
Phase space tomography methods and systems to facilitate the analysis and evaluation of complex, quasi-periodic system by generating computed phase-space tomographic images and mathematical features as a representation of the dynamics of the quasi-periodic cardiac systems. The computed phase-space tomographic images can be presented to a physician to assist in the assessment of presence or non-presence of disease. In some implementations, the phase space tomographic images are used as input to a trained neural network classifier configured to assess for presence or non-presence of pulmonary hypertension, including pulmonary arterial hypertension.
G06T 3/40 - Scaling of a whole image or part thereof
A61B 5/1455 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using optical sensors, e.g. spectral photometrical oximeters
A61B 5/0536 - Impedance imaging, e.g. by tomography
The present disclosure uses physiological data, ECG signals as an example, to evaluate cardiac structure and function in mammals. Two approaches are presented, e.g., a model-based analysis and a space-time analysis. The first method uses a modified Matching Pursuit (MMP) algorithm to find a noiseless model of the ECG data that is sparse and does not assume periodicity of the signal. After the model is derived, various metrics and subspaces are extracted to image and characterize cardiovascular tissues using complex-sub-harmonic-frequencies (CSF) quasi-periodic and other mathematical methods. In the second method, space-time domain is divided into a number of regions, the density of the ECG signal is computed in each region and inputted into a learning algorithm to image and characterize the tissues.
A61B 5/0205 - Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
A61B 5/04 - Measuring bioelectric signals of the body or parts thereof
A61B 5/1455 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using optical sensors, e.g. spectral photometrical oximeters
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/021 - Measuring pressure in heart or blood vessels
82.
Display screen with transitional graphical user interface
Exemplified methods and systems facilitate, on a cloud platform, analysis and presentation of data derived from measurements of the heart acquired in a non-invasive procedure. The cloud platform includes a data store service, an analysis service, and a data exchange service configured to determine the presence or non-presence of significant coronary artery disease.
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G06T 19/20 - Editing of 3D images, e.g. changing shapes or colours, aligning objects or positioning parts
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
A61B 5/02 - Measuring pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography; Heart catheters for measuring blood pressure
A61B 6/00 - Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
G16H 30/00 - ICT specially adapted for the handling or processing of medical images
The exemplified methods and systems provide a phase space volumetric object in which the dynamics of a complex, quasi-periodic system, such as the electrical conduction patterns of the heart, or other biophysical-acquired signals of other organs, are represented as an image of a three dimensional volume having both a volumetric structure (e.g., a three dimensional structure) and a color map to which features can be extracted that are indicative the presence and/or absence of pathologies, e.g., ischemia relating to significant coronary arterial disease (CAD). In some embodiments, the phase space volumetric object can be assessed to extract topographic and geometric parameters that are used in models that determine indications of presence or non-presence of significant coronary artery disease.
A61B 5/02 - Measuring pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography; Heart catheters for measuring blood pressure
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/0265 - Measuring blood flow using electromagnetic means, e.g. electromagnetic flow meter
86.
Method and system to assess disease using phase space tomography and machine learning
The exemplified intrinsic phase space tomography methods and systems facilitate the analysis and evaluation of complex, quasi-periodic system by generating computed phase-space tomographic images as a representation of the dynamics of the quasi-periodic cardiac systems. The computed phase-space tomographic images can be presented to a physician to assist in the assessment of presence or non-presence of disease. In some embodiments, the phase space tomographic images are used as input to a trained neural network classifier configured to assess for presence or non-presence of significant coronary artery disease.
The exemplified methods and systems provide a phase space volumetric object in which the dynamics of a complex, quasi-periodic system, such as the electrical conduction patterns of the heart, or other biophysical-acquired signals of other organs, are represented as an image of a three dimensional volume having both a volumetric structure (e.g., a three dimensional structure) and a color map to which features can be extracted that are indicative the presence and/or absence of pathologies, e.g., ischemia relating to significant coronary arterial disease (CAD). In some embodiments, the phase space volumetric object can be assessed to extract topographic and geometric parameters that are used in models that determine indications of presence or non-presence of significant coronary artery disease.
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
The exemplified methods and systems provide a phase space volumetric object in which the dynamics of a complex, quasi-periodic system, such as the electrical conduction patterns of the heart, or other biophysical-acquired signals of other organs, are represented as an image of a three dimensional volume having both a volumetric structure (e.g., a three dimensional structure) and a color map to which features can be extracted that are indicative the presence and/or absence of pathologies, e.g., ischemia relating to significant coronary arterial disease (CAD). In some embodiments, the phase space volumetric object can be assessed to extract topographic and geometric parameters that are used in models that determine indications of presence or non-presence of significant coronary artery disease.
A61B 5/053 - Measuring electrical impedance or conductance of a portion of the body
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
89.
METHOD AND SYSTEM TO ASSESS DISEASE USING PHASE SPACE TOMOGRAPHY AND MACHINE LEARNING
The exemplified intrinsic phase space tomography methods and systems facilitate the analysis and evaluation of complex, quasi-periodic system by generating computed phase-space tomographic images as a representation of the dynamics of the quasi-periodic cardiac systems. The computed phase-space tomographic images can be presented to a physician to assist in the assessment of presence or non-presence of disease. In some embodiments, the phase space tomographic images are used as input to a trained neural network classifier configured to assess for presence or non-presence of significant coronary artery disease.
The exemplified technology facilitates de-noising of magnetic field-sensed signal data (e.g., of an electrophysiological event) using signal reconstruction processes that fuse the magnetic field-sensed signal data with another sensed signal data (e.g., voltage gradient signal data) captured simultaneously with the magnetic field-sensed signal data. To this end, the purely algorithmic processing technique beneficially facilitates removal and/or filtering of noise from a sensor lead of a noisy captured source and rebuilds the signal for that lead from information simultaneously obtained from other leads of a different source. In some embodiments, a data are fused via a sparse approximation operation that uses candidate terms based on Van der Pol differential equations.
A61B 5/243 - Detecting biomagnetic fields, e.g. magnetic fields produced by bioelectric currents specially adapted for magnetocardiographic [MCG] signals
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/245 - Detecting biomagnetic fields, e.g. magnetic fields produced by bioelectric currents specially adapted for magnetoencephalographic [MEG] signals
A61B 5/327 - Generation of artificial ECG signals based on measured signals, e.g. to compensate for missing leads
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/0205 - Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
A61B 5/053 - Measuring electrical impedance or conductance of a portion of the body
91.
METHODS AND SYSTEMS OF DE-NOISING MAGNETIC-FIELD BASED SENSOR DATA OF ELECTROPHYSIOLOGICAL SIGNALS
The exemplified technology facilitates de-noising of magnetic field-sensed signal data (e.g., of an electrophysiological event) using signal reconstruction processes that fuse the magnetic field-sensed signal data with another sensed signal data (e.g., voltage gradient signal data) captured simultaneously with the magnetic field-sensed signal data. To this end, the purely algorithmic processing technique beneficially facilitates removal and/or filtering of noise from a sensor lead of a noisy captured source and rebuilds the signal for that lead from information simultaneously obtained from other leads of a different source. In some embodiments, a data are fused via a sparse approximation operation that uses candidate terms based on Van der Pol differential equations.
A61B 5/05 - Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
A facility providing systems and methods for discovering novel features to use in machine learning techniques. The facility receives, for a number of subjects, one or more sets of data representative of some output or condition of the subject over a period of time or capturing some physical aspect of the subject. The facility then extracts or computes values from the data and applies one or more feature generators to the extracted values. Based on the outputs of the feature generators, the facility identifies novel feature generators for use in at least one machine learning process and further mutates the novel feature generators, which can then be applied to the received data to identify additional novel feature generators.
G06N 99/00 - Subject matter not provided for in other groups of this subclass
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
G06F 16/56 - Information retrieval; Database structures therefor; File system structures therefor of still image data having vectorial format
G16B 40/00 - ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
G06N 3/04 - Architecture, e.g. interconnection topology
G06F 17/00 - Digital computing or data processing equipment or methods, specially adapted for specific functions
G06F 19/00 - Digital computing or data processing equipment or methods, specially adapted for specific applications (specially adapted for specific functions G06F 17/00;data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes G06Q;healthcare informatics G16H)
93.
Discovering genomes to use in machine learning techniques
A facility for identifying combinations of feature and machine learning algorithm parameters, where each combination can be combined with one or more machine learning algorithms to train a model, is disclosed. The facility evaluates each genome based on the ability of a model trained using that genome and a machine learning algorithm to produce accurate results when applied to a validation data set by, for example, generating a fitness or validation score for the trained model and the corresponding genome used to train the model. Genomes that produce fitness scores that exceed a fitness threshold are selected for mutation, mutated, and the process is repeated. These trained models can then be applied to new data to generate predictions for the underlying subject matter.
G16B 40/00 - ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
G16B 5/00 - ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
G06N 3/12 - Computing arrangements based on biological models using genetic models
G06F 16/00 - Information retrieval; Database structures therefor; File system structures therefor
G01N 33/50 - Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
G06N 20/10 - Machine learning using kernel methods, e.g. support vector machines [SVM]
G06N 5/00 - Computing arrangements using knowledge-based models
G06F 19/00 - Digital computing or data processing equipment or methods, specially adapted for specific applications (specially adapted for specific functions G06F 17/00;data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes G06Q;healthcare informatics G16H)
94.
DISCOVERING NOVEL FEATURES TO USE IN MACHINE LEARNING TECHNIQUES, SUCH AS MACHINE LEARNING TECHNIQUES FOR DIAGNOSING MEDICAL CONDITIONS
A facility providing systems and methods for discovering novel features to use in machine learning techniques. The facility receives, for a number of subjects, one or more sets of data representative of some output or condition of the subject over a period of time or capturing some physical aspect of the subject. The facility then extracts or computes values from the data and applies one or more feature generators to the extracted values. Based on the outputs of the feature generators, the facility identifies novel feature generators for use in at least one machine learning process and further mutates the novel feature generators, which can then be applied to the received data to identify additional novel feature generators.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
G06F 15/18 - in which a program is changed according to experience gained by the computer itself during a complete run; Learning machines (adaptive control systems G05B 13/00;artificial intelligence G06N)
95.
DISCOVERING GENOMES TO USE IN MACHINE LEARNING TECHNIQUES
A facility for identifying combinations of feature and machine learning algorithm parameters, where each combination can be combined with one or more machine learning algorithms to train a model, is disclosed. The facility evaluates each genome based on the ability of a model trained using that genome and a machine learning algorithm to produce accurate results when applied to a validation data set by, for example, generating a fitness or validation score for the trained model and the corresponding genome used to train the model. Genomes that produce fitness scores that exceed a fitness threshold are selected for mutation, mutated, and the process is repeated. These trained models can then be applied to new data to generate predictions for the underlying subject matter.
G16B 40/00 - ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
G16B 50/00 - ICT programming tools or database systems specially adapted for bioinformatics
96.
DISCOVERING NOVEL FEATURES TO USE IN MACHINE LEARNING TECHNIQUES, SUCH AS MACHINE LEARNING TECHNIQUES FOR DIAGNOSING MEDICAL CONDITIONS
A facility providing systems and methods for discovering novel features to use in machine learning techniques. The facility receives, for a number of subjects, one or more sets of data representative of some output or condition of the subject over a period of time or capturing some physical aspect of the subject. The facility then extracts or computes values from the data and applies one or more feature generators to the extracted values. Based on the outputs of the feature generators, the facility identifies novel feature generators for use in at least one machine learning process and further mutates the novel feature generators, which can then be applied to the received data to identify additional novel feature generators.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
97.
DISCOVERING GENOMES TO USE IN MACHINE LEARNING TECHNIQUES
A facility for identifying combinations of feature and machine learning algorithm parameters, where each combination can be combined with one or more machine learning algorithms to train a model, is disclosed. The facility evaluates each genome based on the ability of a model trained using that genome and a machine learning algorithm to produce accurate results when applied to a validation data set by, for example, generating a fitness or validation score for the trained model and the corresponding genome used to train the model. Genomes that produce fitness scores that exceed a fitness threshold are selected for mutation, mutated, and the process is repeated. These trained models can then be applied to new data to generate predictions for the underlying subject matter.
G06F 15/18 - in which a program is changed according to experience gained by the computer itself during a complete run; Learning machines (adaptive control systems G05B 13/00;artificial intelligence G06N)
G06N 3/12 - Computing arrangements based on biological models using genetic models
98.
Non-invasive method and system for characterizing cardiovascular systems for all-cause mortality and sudden cardiac death risk
Methods and systems for evaluating the electrical activity of the heart to identify novel ECG patterns closely linked to the subsequent development of serious heart rhythm disturbances and fatal cardiac events. Two approaches are describe, for example a model-based analysis and space-time analysis, which are used to study the dynamical and geometrical properties of the ECG data. In the first a model is derived using a modified Matching Pursuit (MMP) algorithm. Various metrics and subspaces are extracted to characterize the risk for serious heart rhythm disturbances, sudden cardiac death, other modes of death, and all-cause mortality linked to different electrical abnormalities of the heart. In the second method, space-time domain is divided into a number of regions (e.g., 12 regions), the density of the ECG signal is computed in each region and input to a learning algorithm to associate them with these events.
A61B 5/1455 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using optical sensors, e.g. spectral photometrical oximeters
A61B 5/0452 - Detecting specific parameters of the electrocardiograph cycle
A61B 5/08 - Measuring devices for evaluating the respiratory organs
G06F 19/00 - Digital computing or data processing equipment or methods, specially adapted for specific applications (specially adapted for specific functions G06F 17/00;data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes G06Q;healthcare informatics G16H)
99.
Non-invasive method and system for characterizing cardiovascular systems
The present disclosure uses physiological data, ECG signals as an example, to evaluate cardiac structure and function in mammals. Two approaches are presented, e.g., a model-based analysis and a space-time analysis. The first method uses a modified Matching Pursuit (MMP) algorithm to find a noiseless model of the ECG data that is sparse and does not assume periodicity of the signal. After the model is derived, various metrics and subspaces are extracted to image and characterize cardiovascular tissues using complex-sub-harmonic-frequencies (CSF) quasi-periodic and other mathematical methods. In the second method, space-time domain is divided into a number of regions, the density of the ECG signal is computed in each region and inputted into a learning algorithm to image and characterize the tissues.
A61B 5/0205 - Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
A61B 5/04 - Measuring bioelectric signals of the body or parts thereof
A61B 5/1455 - Measuring characteristics of blood in vivo, e.g. gas concentration, pH-value using optical sensors, e.g. spectral photometrical oximeters
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
A61B 5/021 - Measuring pressure in heart or blood vessels
100.
Methods and systems using mathematical analysis and machine learning to diagnose disease
Exemplified method and system facilitates monitoring and/or evaluation of disease or physiological state using mathematical analysis and machine learning analysis of a biopotential signal collected from a single electrode. The exemplified method and system creates, from data of a singularly measured biopotential signal, via a mathematical operation (i.e., via numeric fractional derivative calculation of the signal in the frequency domain), one or more mathematically-derived biopotential signals (e.g., virtual biopotential signals) that is used in combination with the measured biopotential signals to generate a multi-dimensional phase-space representation of the body (e.g., the heart). By mathematically modulating (e.g., by expanding or contracting) portions of a given biopotential signal, in the frequency domain, the numeric-based operation gives emphasis or de-emphasis to certain measured frequencies of the biopotential signals, which, when coupled with machine learning, facilitates improved diagnostics of certain pathologies.
A61B 5/00 - Measuring for diagnostic purposes ; Identification of persons
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/04 - Measuring bioelectric signals of the body or parts thereof
A61B 5/042 - Electrodes specially adapted therefor for introducing into the body
A61B 5/02 - Measuring pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography; Heart catheters for measuring blood pressure
