Remote monitoring of single-lead electrocardiography enables detection of heart failure status

Abstract

Repeated hospitalization for heart failure (HF) is a strong predictor of mortality among HF patients. While recent cardiac electrical implantable devices (CIEDs) can detect worsening HF through remote monitoring1,2, there is no early detection system for HF progression in patients at home without a CIED. We therefore developed an artificial intelligence-based HF detection system that uses single-lead electrocardiograms (ECGs) recorded at home. Our convolutional neural network (CNN) model calculated a novel HF-index from the estimated NYHA grades as a quantitative indicator of HF severity. Retrospective data revealed a strong correlation between HF-indexes and plasma BNP levels (R=0.91).A prospective clinical study confirmed the accuracy of the HF severity judged from the estimated HF-index using a portable single-lead ECG monitor at home.We have thus successfully constructed a novel, at-home HF monitoring system for a portable single-lead ECG device, which enables early detection and early medical intervention in HF.