William Daniel Sanders

US Patent:

20200205745, Jul 2, 2020

Filed:

Dec 23, 2019

Appl. No.:

16/725430

Inventors:

- Toronto, CA
Timothy William Fawcett Burton - Toronto, CA
Horace Gillins - Toronto, CA
Shyamlal Ramchandani - Kingston, CA
William Sanders - Bethesda MD, US
Ian Shadforth - Morrisville NC, US

International Classification:

A61B 5/00
G06N 3/08
G06N 20/00
G06K 9/62

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.

US Patent:

20180000371, Jan 4, 2018

Filed:

Jun 26, 2017

Appl. No.:

15/633330

Inventors:

- Ganaoque, CA
Shyamlal Ramchandani - Kingston, CA
Timothy Burton - Ottawa, CA
William Sanders - Bethesda MD, US
Ian Shadforth - Morrisville NC, US

International Classification:

A61B 5/04
A61B 5/0408
A61B 5/026
A61B 5/1455
A61B 5/00

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.

Address:

308 Lathrop Ave, Birmingham, AL 35209

VIN:

1GNFC13J77J154132

Make:

CHEVROLET

Model:

TAHOE

Year:

2007