A New ECG Signal Classification Based on WPD and ApEn Feature Extraction

Electrocardiogram (ECG) signal classification is an important diagnosis tool wherein feature extraction plays a crucial function. This paper proposes a novel method for the nonlinear feature extraction of ECG signals by combining wavelet packet decomposition (WPD) and approximate entropy (ApEn). The proposed method first uses WPD to decompose ECG signals into different frequency bands and then calculates the ApEn of each wavelet packet coefficient as a feature vector. A support vector machine (SVM) classifier is used for the classification. The particle swarm optimization algorithm is used to optimize the SVM parameters. The proposed method does not require dimensionality reduction, has fast calculation speed, and requires simple computations. The classification of the signals into five beats yields an acceptable accuracy of 97.78 %.