ECGDetect: Detecting Ischemia via Deep Learning

TL;DR

This paper introduces ECGDetect, a deep learning model that accurately detects myocardial ischemia from ECG data, potentially enabling early diagnosis of acute coronary syndrome, especially in at-risk populations like diabetics.

Contribution

The study presents a novel deep neural network model trained on large ECG datasets to identify ischemic patterns, demonstrating high accuracy and validation on wearable device data.

Findings

Achieved 90.31% ROC-AUC in detecting ischemia.

Attained 89.34% sensitivity and 87.81% specificity.

Validated model performance on wearable ECG data.

Abstract

Coronary artery disease(CAD) is the most common type of heart disease and the leading cause of death worldwide[1]. A progressive state of this disease marked by plaque rupture and clot formation in the coronary arteries, also known as an acute coronary syndrome (ACS), is a condition of the heart associated with sudden, reduced blood flow caused due to partial or full occlusion of coronary vasculature that normally perfuses the myocardium and nerve bundles, compromising the proper functioning of the heart. Often manifesting with pain or tightness in the chest as the second most common cause of emergency department visits in the United States, it is imperative to detect ACS at the earliest. This is particularly relevant to diabetic patients at home, that may not feel classic chest pain symptoms, and are susceptible to silent myocardial injury. In this study, we developed the RCE- ECG-Detect algorithm, a machine learning model to detect the morphological patterns in significant ST change associated with myocardial ischemia. We developed the RCE- ECG-Detect using data from the LTST database which has a sufficiently large sample set to train a reliable model. We validated the predictive performance of the machine learning model on a holdout test set collected using RCE's ECG wearable. Our deep neural network model, equipped with convolution layers, achieves 90.31% ROC-AUC, 89.34% sensitivity, 87.81% specificity.