Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network
In this paper, an electrocardiogram (ECG) beat classification system based on wavelet transformation and probabilistic neural network (PNN) is proposed to discriminate six ECG beat types. The ECG beat signals are first decomposed into components in different subbands using discrete wavelet transformation. Three sets of statistical features of the decomposed signals as well as the AC power and the instantaneous RR interval of the original signal are exploited to characterize the ECG signals. A PNN follows to classify the feature vectors. The result shows a promising accuracy of 99.65%, with equally well recognition rates of over 99% throughout all type of ECG beats. Only 11 features are required to attain this high accuracy, which is substantially smaller in quantity than that in other methods. These observations prove the effectiveness and efficiency of the proposed method for computer-aided diagnosis of heart diseases based on ECG signals.
electrocardiogram probabilistic neural network discrete wavelet transform rr interval pattern classification