Abstract: 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.

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: 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 (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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

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 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.

Abstract: 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 ?s, 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

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 generally relates to systems and methods and systems of a noninvasive technique for characterizing cardiac chamber size and cardiac mechanical function. A mathematical analysis of three-dimensional (3D) high resolution data may be used to estimate chamber size and cardiac mechanical function. For example, high-resolution mammalian signals are analyzed across multiple leads, as 3D orthogonal (X,Y,Z), or 10-channel data, for 30 to 800 seconds, to derive estimates of cardiac chamber size and cardiac mechanical function. Multiple mathematical approaches may be used to analyze the dynamical and geometrical properties of the data.

Abstract: 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.

Abstract: 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.

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 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 ?s, 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.