Automatic ischaemic beats classification using Genetic-based Least Square Support Vector Machine
S. Murugan, S. Radhakrishnan · International Journal of Biomedical Engineering and Technology · 2012
Myocardial ischaemia is the detection of ischaemic episodes in the Electrocardiogram (ECG) recordings can be very supportive to the physicians, relies heavily on the relevant and efficient feature extraction and classification of the ST segment. In this paper, the Genetic Algorithm (GA) and fuzzy logic is combined with PCA and ICA to improve their performance; the algorithms are Fuzzy-Genetic PCA (FGPCA) and Fuzzy-Genetic ICA (FGICA). In the proposed method, the features are classified using a Genetic-based Least Square Support Vector Machine (GLSSVM). The results demonstrated that the GLSSVM with FGICA achieved greater accuracy higher than the other automated diagnostic systems.