An effective PSR-based arrhythmia classifier using self-similarity analysis

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 Fibril-lation 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.

Automated summary

A low computational complexity arrhythmia classifier based on Phase Space Reconstruction is proposed in this study, achieving high accuracy in distinguishing various types of cardiac arrhythmias. The algorithm is evaluated and compared using different datasets, showing potential for real-time arrhythmia classification applications.