Heartbeat classification using different classifiers with non-linear feature extraction
The electrocardiogram (ECG) is an important technique for heart disease diagnosis. This paper proposes a novel method for ECG beat classification. Several important issues exist in the ECG beat classification, which, if suitably addressed, may lead to development of more robust and efficient recognizers. Some of these issues include feature extraction, choice of classification approach and optimization. A new method for non-linear feature extraction of ECG signals based on empirical mode decomposition (EMD), approximate entropy (ApEn) and wavelet packet entropy is presented. The proposed method first uses EMD to decompose ECG signals into a finite number of intrinsic mode functions (IMFs), calculates the ApEn of IMFs as one feature and then obtains the wavelet packet entropy of wavelet packet coefficients as another feature. The two features are regarded as a feature vector. The support vector machine (SVM) and probabilistic neural network (PNN) are used for beat classification. The particle swarm optimization algorithm is used to optimize parameters of the PNN and SVM. The performance of the SVM classifier is slightly superior to that of the PNN classifier with 98.6% accuracy.