Multi-classification of cardiac diseases utilizing wavelet thresholding and support vector machine

Automatic classification of electrocardiogram (ECG) is importance in cardiac disease diagnosis. Support vector machine (SVM) has drawn more and more attention on pattern recognition, including ECG feature extraction and cardiac disease detection. The most prominent advantage of SVM can be represent as its excellent performance on simplification of inner product operation from high dimensional space to low dimensional space, avoiding calculations in high dimension space. In this study, a multi-classification method is proposed utilizing wavelet multi-resolution analysis (WMRA) and SVM. WMRA is applied to eliminate interference with frequency beyond the frequency interval of ECG signals (0.05∼100Hz). Meanwhile, WMRA provides detail coefficients and approximation coefficients of different decomposition levels, which are the input features fed into SVM for classification. After that, SVM is employed to recognize 6 types of cardiac beats from MIT-BIH arrhythmia database. Besides, different parameters C and γ are discussed and tested. Experimental results indicate that the classification performance gets better as C increases and γ decreases. When C and γ are set to be 1000 and 0.1 respectively, an overall classification accuracy, sensitivity and positive predictivity of 95.23%, 97.42% and 97.71% respectively are achieved.