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

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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ORCID for Hanjie Chen: orcid.org/0000-0001-8024-8804

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