Hanjie Chen1, Saptarshi Das2, John M Morgan3, Koushik Maharatna4. 1. School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK. Electronic address: hc4y15@soton.ac.uk. 2. Department of Mathematics, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Penryn Campus, Penryn, TR10 9FE, UK; Institute for Data Science and Artificial Intelligence, University of Exeter, North Park Road, Exeter, Devon, EX4 4QE, UK. Electronic address: saptarshi.das@ieee.org. 3. Faculty of Medicine, University of Southampton, Tremona Road, Southampton, SO17 1BJ, UK. Electronic address: jmm@hrclinic.org. 4. School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK. Electronic address: km3@ecs.soton.ac.uk.
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.
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.
Authors: Kogilavani Shanmugavadivel; V E Sathishkumar; M Sandeep Kumar; V Maheshwari; J Prabhu; Shaikh Muhammad Allayear Journal: Comput Math Methods Med Date: 2022-09-12 Impact factor: 2.809