Automatic classification of the Frank lead ECG using discrete wavelet transform coefficients achieved an overall classification performance of 71.6-74.2% across 7 disease categories.
This study investigates the automatic classification of the Frank lead electrocardiogram (ECG) into different pathophysiological disease categories. Coefficients from the discrete wavelet transform are used to represent the ECG diagnostic information and a comparison of the performance of classifiers processing feature sets generated using different mother wavelets is made. Fifteen feature sets are calculated from three Daubechies wavelets, with the decomposition level varied between 3 and 7. The classification performance of each feature set was optimised using automatic feature selection and by combining classifications of multi-beat ECG information. Throughout the study a database of 500 ECG records with examples from 7 disease categories was used. The classification of each record is known with 100% confidence and is based on ECG independent information. Using multiple runs of 10-fold cross-validation to obtain all results, it was shown that the overall classification performance of the different feature sets was 71.6-74.2%. In addition, the wavelet order and level had little influence on the overall performance. 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.
Chazal et al. (Mon,) conducted a other in 7 pathophysiological disease categories (n=500). Discrete wavelet transform coefficients vs. Different mother wavelets and feature sets was evaluated on Overall classification performance. Automatic classification of the Frank lead ECG using discrete wavelet transform coefficients achieved an overall classification performance of 71.6-74.2% across 7 disease categories.