Fuzzy-genetic Based Pca and Ica for Feature Extraction in Ischemic Beat Classification

PCA and ICA are two powerful techniques for feature extraction. In addition, fuzzy c-means clustering (FCM) is among considerable techniques for data reduction. In other words, the aim of using FCM is to decrease the number of segments by grouping similar segments in training data. In this work, an improved version of PCA and ICA is proposed for feature extraction to classify the ischemic beats from ECG signal. The Fuzzy C-Means (FCM) and Genetic Algorithm (GA) are combined with PCA and ICA to extract more relevant features; the proposed methods are named as Fuzzy-Genetic based PCA (FGPCA) and Fuzzy-Genetic based ICA (FGICA). Least Square Support Vector Machine (LSSVM) is used to classify the beats into ischemic or non-ischemic, with the features from the FGPCA and FGICA.