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

An effective PSR-based arrhythmia classifier using self-similarity analysis
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.
Arrhythmia classification, Phase space reconstruction, Box-counting, Self-similarity
1746-8094
Chen, Hanjie
7a5b6697-7e34-4787-9254-7995f013e94a
Das, Saptarshi
e06f2eb0-1e3e-453c-ba78-82eed18ceac9
Morgan, John
7bd04ada-ca61-4a2c-b1cf-1750ffa9d89c
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Chen, Hanjie
7a5b6697-7e34-4787-9254-7995f013e94a
Das, Saptarshi
e06f2eb0-1e3e-453c-ba78-82eed18ceac9
Morgan, John
7bd04ada-ca61-4a2c-b1cf-1750ffa9d89c
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd

Chen, Hanjie, Das, Saptarshi, Morgan, John and Maharatna, Koushik (2021) An effective PSR-based arrhythmia classifier using self-similarity analysis. Biomedical Signal Processing and Control, 69 (102851), [102851]. (doi:10.1016/j.bspc.2021.102851).

Record type: Article

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 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.

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More information

Published date: 15 June 2021
Keywords: Arrhythmia classification, Phase space reconstruction, Box-counting, Self-similarity

Identifiers

Local EPrints ID: 449813
URI: http://eprints.soton.ac.uk/id/eprint/449813
ISSN: 1746-8094
PURE UUID: eef661b8-810a-47d4-8a6f-00e166fab093
ORCID for Hanjie Chen: ORCID iD orcid.org/0000-0001-8024-8804

Catalogue record

Date deposited: 18 Jun 2021 16:30
Last modified: 19 Jun 2021 01:53

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Contributors

Author: Hanjie Chen ORCID iD
Author: Saptarshi Das
Author: John Morgan
Author: Koushik Maharatna

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