Detection of Congestive Heart Failure with Nature-Inspired Optimization and Classifiers Performance Amelioration from ECG Signals

Abstract

This paper explores the earlier detection of CHF, and its allied Cardio Vascular Diseases (CVD) like Atrial Fibrillation (Afib) from nature-inspired optimization techniques using cardiac signals. The Beth Israel Deaconess Medical Centre (BIDMC) CHF, MIT-BIH-Afib, and Normal Sinus Rhythm (NSR) databases from the Physionet repository are utilized in this research. The Flower Pollination Algorithm (FPA), Binary Flower Pollination Algorithm (BFPA), and BFPA with Greedy Crossover approaches are applied to extract the features of CHF, Afib, and NSR cardiac classes ECG signals. Then extracted features are given into appropriate classifiers such as the SVM (Linear), SVM (Polynomial), SVM (RBF), WKNN, and NBC classifiers. The classifiers’ performance is assessed and compared by the standard performance benchmarks, such as Accuracy, F1 Score, Geometric Mean, Kappa Coefficient and Error Rate. The SVM (RBF) classifier with BFPA features achieved 97.71% overall average accuracy along with a 0.9540 kappa coefficient in {CHF} vs. {NSR} cardiac abnormality detection. The SVM (RBF) classifier with BFPA features reached 89.59% overall average accuracy along with a 0.7935 kappa coefficient in {Afib} vs. {NSR} cardiac class detection. As a result, the SVM (RBF) classifier with BFPA features outclasses all other classification techniques in detecting {CHF} vs. {NSR} cardiac abnormality classes.