A comparison of the use of different wavelet coefficients for the classification of the electrocardiogram

This study compares the classification performance of feature sets formed from the discrete-wavelet-transform coefficients of different mother wavelets. Fifteen feature sets are calculated from three Daubechies wavelets, with the decomposition level varied between 3 and 7. Classification performance is optimised by using automatic feature selection and by combining classifications of multi-beat ECG information. Results show that the over all classification performance of the different feature sets was 71.6-74.2% and that the wavelet order and level had little influence on the over all performance. All quoted results are obtained from 10 runs of 10-fold cross-validation. An analysis of the automatically chosen features reveal that time-frequency bands in the vicinity of the QRS onset and the T-wave are consistently selected