Automatic diagnosis of septal defects based on tunable-Q wavelet transform of cardiac sound signals
We propose a new method for diagnosis of septal defects using TQWT. New feature set based on SAMDF derived from TQWT has been proposed. The effects of Q and decomposition levels on classification performance have been evaluated. Results have been evaluated with classification performance evaluation parameters and ROC graphs. Performance has been compared with existing TQWT based method with same datasets.
septal defects cardiac sound signals classification segmentation heart beat cycles tunable-q wavelet transform (tqwt) sum of average magnitude difference function (samdf) least squares support vector machine (ls-svm)