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 fibrillation (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 technologies 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
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
March 2022
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).
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 fibrillation (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 technologies 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
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Date deposited: 28 Mar 2022 16:49
Last modified: 17 Mar 2024 07:03
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Contributors
Author:
Hanjie Chen
Author:
Saptarshi Das
Author:
Koushik Maharatna
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