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 monitoring 1,2 , 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.