An effective PSR-based arrhythmia classifier using self-similarity analysis. (August 2021)

• Record Type: Journal Article
• Authors: Chen, Hanjie; Das, Saptarshi; Morgan, John; Maharatna, Koushik
• Is Part Of: Biomedical signal processing and control. Volume 69(2021)
• Publication Date: 2021-08
• Subjects: Arrhythmia classification -- Phase space reconstruction -- Box-counting -- Self-similarity; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102851
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Arrhythmia is caused due to cumulative effect of disruptive phase relationship. Heart electrical activities from different parts lead to desynchronised operation. Phase space diagrams based on a 5 s ECG window with different time-delay are used for self-similarity analysis. Features extracted from different parts of the phase space diagram are used to classify four types of arrhythmia. Average computation time for one 5 s window of ECG is 1.9 s, showing potential for developing a real-time system. Abstract: Among different cardiac arrhythmias, Ventricular Arrhythmias (VA) are fatal and life-threatening. Therefore, the detection and classification of VA is crucial task for cardiologists. However, in some cases, the ECG morphologies of two kinds of VA - Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF) are similar and difficult to distinguish by human eyes. In this study, we present a low computational complexity arrhythmia classifier with high accuracy based on Phase Space Reconstruction (PSR). It is used to classify normal electrocardiogram (ECG), atrial fibrillation (AF), VT, VF and VT followed by VF. The Creighton University Ventricular Tachyarrhythmia Database (CUDB), Physikalisch-Technische Bundesanstalt Diagnostic Database (PTBDB), MIT-BIH Atrial Fibrillation Database (MIT-BIH AFDB) from PhysioNet databank and University Hospital Southampton database (UHSDB) are used for evaluation and comparison of the proposed algorithm. Two PSR diagrams were plotted based on a window length of 5 s ECG with two different time delays and the PSR-based features were extracted from them using the box-counting technique. This process was applied on 122 records with more than 5500 windows of ECG signals. The results show an average sensitivity of 98.73%, specificity of 99.71% and accuracy of 99.56%. The average computational time of our proposed method for one 5 s window processing is 1.9 s and therefore has the potential in real-time arrhythmia classification applications.