Quantification of electrocardiogram rhythmicity to detect life threatening cardiac arrhythmias using spectral entropy

Changes in the normal rhythmicity of the heart may result in different cardiac arrhythmias, which may be fatal or cause serious damage to the heart if sustained over long periods of time. Ventricular tachycardia or fibrillation (VTVF) as fatal cardiac arrhythmia is the major cause leading to sudden cardiac death. It is crucial for the patient to receive immediate medical intervention when either VT or VF occurs. In this study, we present a novel, and computationally fast method to quantify the rhythmicity of the short-term electrocardiogram (ECG) signals based on spectral entropy feature and there by discriminate between normal sinus rhythm (NSR) and life threatening arrhythmias like, ventricular tachycardia/fibrillation (VT/VF). The receiver operating characteristic curve (ROC) analysis confirms the robustness of this new approach for a window length of 2 s and exhibits an average sensitivity = 99.4% (99.4%), specificity = 98.7% (99.0%), positive predictivity = 98.7% (99.6%), and accuracy = 98.9% (99.2%), to distinguish between normal and VT (VF) subjects. The presented method is simple, highly accurate, computationally efficient, and well suited for real time implementation in automated external defibrillators (AEDs).