Articles and methods for detection of hidden cardiovascular disease from portable electrocardiographic signal data using deep learning

Abstract

Provided herein are methods of training a model to detect cardiovascular disease in a subject from portable electrocardiogram (ECG) signal data and computer-implemented methods of detecting cardiovascular disease in a subject. The methods of training a model includes selecting an ECG dataset corresponding to a cardiovascular disease, the ECG dataset including multiple distinct ECGs, forming a training dataset from the ECG dataset, training the nodes of a deep neural network on the training dataset, and including random gaussian noise with each ECG of the ECG dataset during the training of the nodes. The computer-implemented methods of detecting cardiovascular disease in a subject include applying a deep neural network to ECG data for a subject, the deep neural network being trained according to the training methods disclosed herein.

Dependencies are inferred from the claim text.