Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.