Assessment of QRS and Q-T segments efficacy as non-invasive biomarkers for sudden cardiac death forecasting

Abstract

Sudden cardiac death (SCD) is a critical event occurring within an hour of sudden cardiac arrest (SCA). SCA often arises from disruptions in cardiac electrical signals, leading to fatality by hindering blood circulation. SCD, a significant contributor to cardiovascular-related deaths, impacts millions people globally. Most studies in the literature focus on heart rate variability (HRV) as a biomarker for predicting SCD while marginalizing other ECG morphological features. This study strives to assess and compare the QRS and Q-T efficacy as non-invasive biomarkers to predict SCD. The study aims to examine the QRS and Q-T segments of the ECG signal as potential biomarkers for predicting SCD effectively. The process involves selecting ECG segments from international databases, followed by preprocessing, delineation, empirical mode decomposition (EMD), and median frequency (MDF) feature extraction. Machine learning classifiers, namely support vector machine (SVM) and random forest (RF), are employed to classify SCD and normal sinus rhythm (NSR) classes based on the extracted features. The results underscore the superiority of the Q-T segment, with SVM achieving the best classification performance (accuracy = 83.88%, sensitivity = 90%, specificity = 77.77%). This suggests that the Q-T segment holds the potential to predict SCD better than the QRS segment.