ECG classification using ensemble of features

In the literature, countless efforts have been made to analyze and classify electrocardiogram (ECG) signals belonging to various heart problems. In all these efforts, many feature extraction strategies have been used to expose discriminative information from ECG signals. In this paper, the contributions of widely used features to the classification performance and the required processing times to extract those features are comparatively analyzed. The utilized features can be briefly listed as time domain (TD), wavelet transform (WT), and power spectral density (PSD) based features. These feature sets are employed individually and in combination within well-known pattern classifiers, namely decision tree and artificial neural network, to assess classification performance in each case. Later, a wrapper-based feature selection strategy is used to reveal the most discriminative feature subset among the entire feature set containing all the three previously mentioned feature sets. The proposed framework is assessed considering four classes of heart conditions including normal, congestive heart failure, ventricular tachyarrhythmia and atrial fibrillation. The results of the experiments conducted on a large dataset reveal that appropriate subset of TD, WT, and PSD features rather than individual features offer higher classification performance. On the other hand, if the processing time is of concern, TD features come out on top with moderate classification performance.