Hierarchical support vector machine based heartbeat classification using higher order statistics and hermite basis function

The heartbeat class detection of the electrocardiogram is important in cardiac disease diagnosis. For detecting morphological QRS complex, conventional detection algorithm have been designed to detect P, QRS, T wave. However, the detection of the P and T wave is difficult because their amplitudes are relatively low, and occasionally they are included in noise. We applied two morphological feature extraction methods: higher-order statistics and Hermite basis functions. Moreover, we assumed that the QRS complexes of class N and S may have a morphological similarity, and those of class V and F may also have their own similarity. Therefore, we employed a hierarchical classification method using support vector machines, considering those similarities in the architecture. The results showed that our hierarchical classification method gives better performance than the conventional multiclass classification method. In addition, the Hermite basis functions gave more accurate results compared to the higher order statistics.