Comparison of different wavelet subband features in the classification of ECG beats using 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 effects of two wavelet decomposition structures, the two-stage two-band and the two-stage full binary decomposition structures, in the recognition of ECG beat types are studied. The ECG beat signals are first decomposed into components in different subbands using discrete wavelet transformation. Three statistical features of each decomposed subband signals as well as the AC power and instantaneous RR interval of the original signal are exploited to characterize the ECG signals. A PNN then follows to classify the feature vectors. The result shows that features extracted from the decomposed signals based on the two-stage two-band structure outperform the two-stage full binary structure. A promising accuracy of 99.65%, with equally well recognition rates of over 99% throughout all type of ECG beats, has been achieved using the optimal feature set. Only 11 features are needed to attain this performance. The results demonstrate the effectiveness and efficiency of the proposed method for the computer-aided diagnosis of heart diseases based on ECG signals.