A Fusion Based Classification of Normal, Arrhythmia and Congestive Heart Failure in ECG

In healthcare, Electrocardiogram (ECG) signal is considered important to study life-threatening heart diseases that include arrhythmia (ARR), congestive heart failure (CHF). Mostly, atrial arrhythmia leads to CHF. Previous studies on ARR and CHF are focused on the binary classification of each category against normal sinus rhythm (NSR). So, there is a requirement to study the above disease cases together to detect the severity of the situation and take remedial action accordingly. The goal of this study is to analyse and classify these three different classes of ECG (namely ARR, CHF, and NSR) in an efficient way. We used 30 ECG recordings for each of the classes from the publicly available Physionet database. Since the temporal and spectral features by themselves may be insufficient to distinguish the classes, we sought to combine information across both. Accordingly, we considered feature representations from heart rate variability (HRV) of the ECG signal and wavelet-based features together with auto-regressive coefficients. To leverage complementary information across feature types, we employed feature-level fusion. We examined the performance of individual and fused feature types with multiple classifiers. The highest accuracy of 93.33% for three-class classification was obtained after feature fusion using Support Vector Machine (SVM). Although the performance of HRV features is relatively poor compared to wavelet-based features, their fusion improved the classification accuracy.