LAUSR

Sudden cardiac arrest (SCA) prediction using electrocardiogram (ECG) and heart rate variability (HRV) signals has received the attention of researchers in recent years. Ventricular fibrillation (VF) is one of the most common identifiers for SCA. This work aims to investigate the ECG morphological feature, R peak to T-end (R–Tend), to foresee the imminent SCA 5 min before VF onset. ECG signals for SCA and Normal Controls from The MIT-BIH databases are divided into 1-min duration and is used to predict the onset of VF. Four nonlinear features, the Largest Lyapunov Exponent, Hurst exponent, sample entropy, and approximate entropy were extracted from the R–Tend beats and classified using three classifiers: support vector machine, subtractive fuzzy clustering, and neuro-fuzzy classifier. The performance of the proposed methodology confirms that the sample entropy features efficiently predict the SCA five min before VF onset using SVM classifier and produces the maximum mean classification rate of 100% compared to other classifiers. The proposed algorithm using R–Tend beats predicts the onset of SCA better than HRV signals and is computationally efficient. The proposed marker based on ECG morphological characteristics can be used as a tool to predict SCA for smarter healthcare management.