ECG waveform classification using the neural network and wavelet transform

Two feature extraction methods: Fourier analysis and wavelet analysis for ECG waveform classification are comparatively investigated. Ten different ECG waveforms from MIT/BIH database are classified using a neural network trained by genetic algorithms (NeTGA). One set of feature vectors is formed by using DFT coefficients, and the second set is formed by using wavelet transform (WT) coefficients and their autocorrelation values. Elements of the feature vectors are searched by using dynamic programming (DP) according to the divergence values. Wavelet feature set is found to result in better classification accuracy with less number of nodes. It is observed that with the feature set formed by wavelet analysis, NeTGA gives 99.4% classification performance with 26 nodes after a short training time.