Abnormal Rhythm Detection from a Single-Lead ECG via a Recurrent Neural Network

Abstract

Cardiac arrhythmias affect millions of individuals worldwide and can lead to severe complications such as stroke or heart failure. They can be difficult to diagnose with ambulatory electrocardiogram monitors due to their transient nature. We propose a system for long-term ar-rhythmia monitoring that takes single-lead electrocardio-gram and tri-axis acceleration signals as inputs. It is composed of a beat detector to extract interbeat intervals and a classifier to detect arrhythmias. This system is evalu-ated on two datasets including 42 patients and achieves an accuracy of 0.988 for the abnormal class, 0.967 for the normal class, and 0.979 for the tachycardia class.