Single Lead ECG-based Ventricular Repolarization Classification for Early Identification of Unexpected Ventricular Fibrillation

Malignant ventricular arrhythmia (especially ventricular fibrillation (VF)) is the main reason which causes sudden cardiac death (SCD). This paper presents an automatic SCD-patient classifier we developed to identify patients with unexpected VF using 60-minutes continuous single-lead electrocardiograms (ECG) signals before that. Patients are classified as having SCD if the majority of their recorded ventricular repolarization (VR) is recognized as characteristic of unexpected VF. Thus, the classifier's underlying task is to recognize individual VR delineated from single-lead ECG signals as SCD VR, where VR from non-SCD patients are used as controls. With the reported clinical practices of SCD, we extracted five morphological and temporal features (both commonly used and newly developed ones) from ECG signals for VR classification. To evaluate classification performance, we trained and tested k nearest neighbor classifier, a decision tree classifier, and a Naïve Bayes classifier using five-fold cross validation on 36 one-hour ECG signals (18 from patients at risk of SCD and 18 from control people). We compared the performance of these three classifiers, and the patient-classification sensitivity is approximately 98.02-99.51%. Moreover, the k nearest neighbor with a higher accuracy (98.89%) and specificity (98.27%) performed better than the other two. Importantly, the results show obvious superiorities of performance over that in the same duration and of usefulness over several minutes given by related works.Clinical Relevance- This could be integrated into a real-time, long-term out-of-hospital SCD predictor to improve the warning veracity and bring forward the warning time, especially for patients with implantable cardiac defibrillators or pacemakers, etc..