Automated characterization of arrhythmias using nonlinear features from tachycardia ECG beats

Arrhythmias are abnormal heartbeat rhythms, categorized as either harmless or life-threatening. Commonly, elderly people are more vulnerable to life-threatening arrhythmias, namely Atrial Fibrillation (A-Fib), Atrial Flutter (AFL) and Ventricular Fibrillation (V-Fib). Electrocardiogram (ECG) is the primary diagnostic tool that can be used to detect and diagnose cardiac abnormalities including serious arrhythmias. Therefore, using ECG signal beats, we have proposed a computer-aided diagnosis (CAD) system for automated diagnosis of serious arrhythmias. The ECG beats are analyzed using thirteen nonlinear features namely, Shannon entropy, Fuzzy entropy, Tsallis entropy, approximate entropy, Permutation entropy, Modified Multi Scale entropy, Wavelet entropy, Sample entropy, Renyi entropy, Signal Energy, Fractal Dimension, Kolmogorov Sinai entropy and Largest Lyapunov Exponent. Subsequently, the extracted features are ranked using ANOVA and subjected to automated classification using the K-Nearest Neighbor (KNN) and Decision Tree (DT) classifiers. In addition, the extracted features are trained and tested with ten-fold cross validation analysis. DT classifier yielded 96.3% accuracy, 99.3% sensitivity and 84.1% specificity with 14 features and the KNN classifier yielded 93.3% accuracy, 97.5% sensitivity and 75.3% specificity using 12 features. Positively, the proposed CAD system is able to assist the clinical staff in the arrhythmias diagnosis by making the process faster and simpler. Consequently, the necessary treatments can be given expeditiously.