Analysis of Pan-Tompkins Algorithm Performance with Noisy ECG Signals

Abstract

Abstract The Pan-Tompkins Algorithm is the most widely used QRS complex detector for the monitoring of many cardiac diseases including in arrhythmia detection. This method could provide good detection performance with high-quality clinical ECG signal data. However, the numerous types of noise and artefacts that exist in an ECG signal will produce low-quality ECG signal data. Because of this, the performance of Pan-Tompkins-based QRS detection methods using low-quality ECG signals should be further investigated. In this paper, the performance of the Pan-Tompkins algorithm was analysed in extracting the QRS complex from standard ECG data that includes noise-stressed ECG signals. The algorithm’s QRS detection reliability was tested using MIT-BIH Noise Stress Test data and MIT-BIH Arrhythmia data. The performance of the algorithm was then analysed and presented. This paper shows the capability of the Pan-Tompkins algorithms in handling noisy ECG signals.