Auto-encoder based ECG signal feature extraction for real-time detection of cardiac arrhythmias

Abstract

In this paper, we proposed to use an auto - encoder for ECG signal feature extraction and a QRS complex shape prediction error as an estimate of signal shape changes, related to arrhythmia. In order to estimate the efficiency of auto - encoder based feature extraction, we performed an experimental investigation using Ventricular Tachycardia (VT) indica ted incidents’ records from Physionet database. The experimental investigation has shown, that the application of auto - encoder makes possible to detect VT incidents in signals, where the features based on signal frame averaging fails. An additional investi gation is needed in order to create a robust algorithm for decision threshold selection, to indicate the MSE value changes, at which the VT incident related alarm should be activated.