Analytics for Life Inc.

Shyamlal Ramchandani

Sunny Gupta

Timothy William Fawcett Burton

Mohsen Najafi Yazdi

Derek Exner

Timothy Burton

Ian Shadforth

William Sanders

Don Crawford

Abhinav Doomra

Ali Khosousi

Kristine Canavan

Horace R Gillins

Michael C Garrett

Meng Lei

Paul Grouchy

Matthew Howe-Patterson

Farhad Fathieh

Mehdi Paak

Jonathan James Woodward

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.

Method and system for signal quality assessment and rejection using heart cycle variability

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.

Method and system to assess disease using multi-sensor signals

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.

Methods and systems to configure and use neural networks in characterizing physiological systems

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. 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.

Method and system for automated quantification of signal quality

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.

Method and apparatus for wide-band phase gradient signal acquisition

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.

Non-invasive method and system for measuring myocardial ischemia, stenosis identification, localization and fractional flow reserve estimation

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.

Method and system to assess disease using phase space volumetric objects

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.

Method and system to assess disease 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.

Method and system to assess pulmonary hypertension using phase space tomography and machine learning

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.

Methods and systems using mathematical analysis and machine learning to diagnose disease

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 &#x3bc;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.

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 Poincar&#xe9; map of synchronicity between the cardiac signals and photoplethysmographic signals are evaluated.

Method and system to assess disease using dynamical analysis of cardiac and photoplethysmographic 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.

Growing community of inventors

Kingston, Canada

Shyamlal Ramchandani