Gender Classification from ECG Signal Analysis using Least Square Support Vector Machine

In this present paper it deals with the Gender Classification from ECG signal using Least Square Support Vector Machine (LS-SVM) and Support Vector Machine (SVM) Techniques. The different features extracted from ECG signal using Heart Rate Variability (HRV) analysis are the input to the LS-SVM and SVM classifier and at the output the classifier, classifies whether the patient corresponding to required ECG is male or female. The least square formulation of support vector machine (SVM) has been derived from statistical learning theory. SVM has already been marked as a novel development by learning from examples based on polynomial function, neural networks, radial basis function, splines and other functions. The performance of each classifier is decided by classification rate (CR). Our result confirms the classification ability of LS-SVM technique is much better to classify gender from ECG signal analysis in terms of classification rate than SVM.