Heart failure grading using single-lead electrocardiography

Abstract

AbstractConvolutional neural networks (CNNs) applied to electrocardiograms (ECGs) have been showing utility for detecting left ventricular (LV) dysfunction1. Although early detection of reduced LV ejection fraction (rEF) could improve handling of heart failure (HF) with rEF (HFrEF), it is not sufficient to detect HF with preserved EF (HFpEF). Here we developed a CNN algorithm to classify the severity of HF based on single-lead ECG data, irrespective of EF. We trained a CNN using ECG data and the HF classification from 7,865 patients with HF. The CNN achieved an area under the receiver-operating characteristic curve (AUC) of 0.996 for distinguishing patients with HF of various severity from healthy controls. It is anticipated that early detection of HF and therapeutic management of HF patients can be improved by employing this CNN with a single-lead ECG device.