Analytics for Life Inc.

Ian Shadforth

Sunny Gupta

Shyamlal Ramchandani

Timothy Burton

Meng Lei

Timothy William Fawcett Burton

William Sanders

Don Crawford

Paul Douglas Souza

Cody James Wackerman

Andrew Hugh Dubberly

Ali Khosousi

Paul Grouchy

Horace R Gillins

Abhinav Doomra

Jonathan James Woodward

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

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 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

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.

Method and system for visualization of heart tissue at risk

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

Non-invasive method and system for estimating arterial flow characteristics

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.

Discovering genomes to use in machine learning techniques

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.

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Morrisville, NC, United States of America

Ian Shadforth