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Prediction and classification of ventricular arrhythmia based on phase-space reconstruction and fuzzy c-means clustering

Prediction and classification of ventricular arrhythmia based on phase-space reconstruction and fuzzy c-means clustering
Prediction and classification of ventricular arrhythmia based on phase-space reconstruction and fuzzy c-means clustering
Background and objective: Prediction and classification of Ventricular Arrhythmias (VA) may allow clinicians sufficient time to intervene for stopping its escalation to Sudden Cardiac Death (SCD). This paper proposes a novel method for predicting VA and classifying its type, in particular, the fatal VA even before the event occurs. Methods: A statistical index J based on the combination of phase-space reconstruction (PSR) and box counting has been used to predict VA. The fuzzy c-means (FCM) clustering technique is applied for the classification of impending VA. Results: 32 healthy and 32 arrhythmic subjects from two open databases - PTB Diagnostic database (PTBDB) and CU Ventricular Tachyarrhythmia (CUDB) database respectively; were used to validate our proposed method. Our method showed average prediction time of approximately 5 min (4.97 min) for impending VA in the tested dataset while classifying four types of VA (VA without ventricular premature beats (VPBs), ventricular fibrilla-tion (VF), ventricular tachycardia (VT), and VT followed by VF) with an average 4 min (approximately) before the VA onset, i.e., after 1 min of the prediction time point with average accuracy of 98.4%, a sensitivity of 97.5% and specificity of 99.1%. Conclusions: The results obtained can be used in clinical practice after rigorous clinical trial to advance tech-nologies such as implantable cardioverter defibrillator (ICD) that can help to preempt the occurrence of fatal ventricular arrhythmia - a main cause of SCD.
Fuzzy C-means clustering, Phase space reconstruction, Prediction and classification, Ventricular arrhythmia
0010-4825
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 (2022) Prediction and classification of ventricular arrhythmia based on phase-space reconstruction and fuzzy c-means clustering. Computers in Biology & Medicine, 142, [105180]. (doi:10.1016/j.compbiomed.2021.105180).

Record type: Article

Abstract

Background and objective: Prediction and classification of Ventricular Arrhythmias (VA) may allow clinicians sufficient time to intervene for stopping its escalation to Sudden Cardiac Death (SCD). This paper proposes a novel method for predicting VA and classifying its type, in particular, the fatal VA even before the event occurs. Methods: A statistical index J based on the combination of phase-space reconstruction (PSR) and box counting has been used to predict VA. The fuzzy c-means (FCM) clustering technique is applied for the classification of impending VA. Results: 32 healthy and 32 arrhythmic subjects from two open databases - PTB Diagnostic database (PTBDB) and CU Ventricular Tachyarrhythmia (CUDB) database respectively; were used to validate our proposed method. Our method showed average prediction time of approximately 5 min (4.97 min) for impending VA in the tested dataset while classifying four types of VA (VA without ventricular premature beats (VPBs), ventricular fibrilla-tion (VF), ventricular tachycardia (VT), and VT followed by VF) with an average 4 min (approximately) before the VA onset, i.e., after 1 min of the prediction time point with average accuracy of 98.4%, a sensitivity of 97.5% and specificity of 99.1%. Conclusions: The results obtained can be used in clinical practice after rigorous clinical trial to advance tech-nologies such as implantable cardioverter defibrillator (ICD) that can help to preempt the occurrence of fatal ventricular arrhythmia - a main cause of SCD.

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Accepted/In Press date: 24 December 2021
e-pub ahead of print date: 29 December 2021
Published date: March 2022
Keywords: Fuzzy C-means clustering, Phase space reconstruction, Prediction and classification, Ventricular arrhythmia

Identifiers

Local EPrints ID: 455602
URI: http://eprints.soton.ac.uk/id/eprint/455602
ISSN: 0010-4825
PURE UUID: 93e345b6-970a-4dfa-8447-1d5ee7b95719
ORCID for Hanjie Chen: ORCID iD orcid.org/0000-0001-8024-8804

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Date deposited: 28 Mar 2022 16:49
Last modified: 29 Mar 2022 01:50

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Contributors

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

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