A QRS Detection and Heartbeat Classification Method for New-Generation Wearable ECG Devices

Abstract

In the new-generation wearable Electrocardiogram (ECG) devices signal processing with low power consumption is required to transmit data when detecting dangerous rhythms and to record signals when detecting abnormal rhythms This study proposes a real-time QRS detection and heartbeat classification method with low computational complexity while maintaining a high accuracy The enhancement of QRS segments and restraining of P and T waves are carried out by the proposed ECG signal transformation which also leads to the elimination of baseline wandering In this study the QRS fiducial point is determined based on the detected crests and troughs of the transformed signal Subsequently the R point can be recognized based on four QRS waveform templates and preliminary heartbeat classification can be also achieved at the same time At last the detected heartbeat can be classified using decision rules according to the detected features The performance of the proposed approach is demonstrated using the benchmark of the MIT-BIH Arrhythmia Database where the QRS sensitivity (Se) is 99 82% positive prediction (+P) is 99 81% VEB (ventricular ectopic beat) Se is 89 34% VEB +P is 84 97% respectively The result reveals the approach’s advantage of low computational complexity as well as the feasibility of the real-time application on a mobile phone and an embedded system

Date of Award	2017 Nov 10
Original language	English
Supervisor	Chieh-Li Chen (Supervisor)