Classification of electrocardiogram signals with waveform morphological analysis and support vector machines

Electrocardiogram (ECG) indicates the occurrence of various cardiac diseases, and the accurate classification of ECG signals is important for the automatic diagnosis of arrhythmia. This paper presents a novel classification method based on multiple features by combining waveform morphology and frequency domain statistical analysis, which offer improved classification accuracy and minimise the time spent for classifying signals. A wavelet packet is used to decompose a denoised ECG signal, and the singular value, maximum value, and standard deviation of the decomposed wavelet packet coefficients are calculated to obtain the frequency domain feature space. The slope threshold method is applied to detect R peak and calculate RR intervals, and the first two RR intervals are extracted as time-domain features. The fusion feature space is composed of time and frequency domain features. A combination of support vector machine (SVM) with the help of grid search and waveform morphological analysis is applied to complete nine types of ECG signal classification. Computer simulations show that the accuracy of the proposed algorithm on multiple types of arrhythmia databases can reach 96.67%.