Cardiac Arrhythmia Detection from ECG Combining Convolutional and Long Short-Term Memory Networks

A new approach to detect and classify automatically cardiac arrhythmias in electrocardiograms (ECG) recordings using a combination of Convolution Neural Networks and a sequence of Long Short-Term Memory units to improve their accuracy.

Abstract

Objectives: Atrial fibrillation (AF) is a common heart rhythm disorder associated with deadly and debilitating consequences including heart failure, stroke, poor mental health, reduced quality of life and death. Having an automatic system that diagnoses various types of cardiac arrhythmias would assist cardiologists to initiate appropriate preventive measures and to improve the analysis of cardiac disease. To this end, this paper introduces a new approach to detect and classify automatically cardiac arrhythmias in electrocardiograms (ECG) recordings.