Feature extraction for improving the support vector machine biomedical data classifier performance

A support vector machine (SVM) is a relatively novel classifier based on the statistical learning theory. To increase the performance of classification, presented study focuses on the mixed domain (time&frequency) feature extraction preliminary to SVM application. Time and frequency domain selected features and discrete fast wavelet transform coefficients parameters including energy and entropy measures were the component of new feature vector. SVM classifier structure were adjusted by the selection of optimal for analysed application its kernel functions:both polynomial and radial basis functions. System was positively verified on the set of clinically classified ECG signals for control and atrial fibrillation (AF) disease patients taken from MITBIH data base. The measures of specificity and sensitivity computed for the set of 20 AF and 20 patients from control group divided into learning and verifying subsets were used to evaluate presented pattern recognition structure. Different types of wavelet basic function for feature extraction stage were tested to find the best system structure. Obtained results showed, that the ability of generalization for enriched feature extraction (FE)-SVM based system increased, due to selectively choosing only the most representative features for analyzed AF detection problem.