Amit D Shah

20210398683, Dec 23, 2021

Dec 5, 2019

17/295248

- Atlanta GA, US
Amit Shah - Atlanta GA, US
Erik Reinertsen - Atlanta GA, US

EMORY UNIVERSITY - Atlanta GA
GEORGIA TECH RESEARCH CORPORATION - Atlanta GA

G16H 50/30
G16H 50/20
G06K 9/62
G16H 50/70
G16H 10/60
G16H 40/67

Methods and systems for monitoring of sensor data for processing by machine-learning models to generate event predictions to estimate a risk a medical event are provided. An electronic device or wearable smart device may monitor the output of various sensors to collect data related to a person's activity level, location changes, and communications and may use this information as input to a personalized trained machine-learning model to predict a likelihood of an event.

20190328243, Oct 31, 2019

Dec 21, 2017

16/472818

- Atlanta GA, US
Supreeth Prajwal Shashikumar - Atlanta GA, US
Amit Jasvant Shah - Atlanta GA, US
Qiao Li - Atlanta GA, US

A61B 5/0205
A61B 5/00
G16H 10/60
G16H 50/20

The systems and methods can accurately and efficiently determine abnormal cardiac activity from motion data and/or cardiac data using techniques that can be used for long-term monitoring of a patient. In some embodiments, the method for using machine learning to determine abnormal cardiac activity may include receiving one or more may include applying a trained deep learning architecture to each tensor of the one or more periods of time to classify each window and/or each period into one or more classes using at least the one or more signal quality indices for the cardiac data and the motion data and cardiovascular features. The deep learning architecture may include a convolutional neural network, a bidirectional recurrent neural network, and an attention network. The one or more classes may include abnormal cardiac activity and normal cardiac activity.

20190313960, Oct 17, 2019

Nov 1, 2017

16/469001

- Atlanta GA, US
Erik REINERTSEN - Atlanta GA, US
Amit SHAH - Atlanta GA, US
Shamim NEMATI - Atlanta GA, US

A61B 5/16
A61B 5/0456
A61B 5/04
A61B 5/024
A61B 5/00

Systems, methods, and computer-readable media for classifying a PTSD status. In an embodiment, an example method for using a classifier can comprise receiving information from electrocardiography performed on an individual; determining features from the information; comparing the features to the a logistic regression classifier trained using features determined from median quiescent segments of RR interval information from individuals with and without PTSD, wherein the median quiescent segments are non-overlapping time periods of lowest median HR for each individual, and the features include one or more of the following: deceleration capacity (DC), low frequency (LF) power, very low frequency (VLF) power, and standard deviation of all normal RR intervals (SDNN); and determining a posttraumatic stress disorder (PTSD) status of the individual based on the comparison of the features to the classifier, wherein the PTSD status is a severity of PTSD based on a probability of PTSD.