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

Abstract

In this present paper it deals with the Gender Classification fro m ECG signal using Least Square Support Vector Machine (LS-SVM ) and Support Vector Machine (SVM ) Techniques. The different features extracted fro m 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 fro m statistical learn ing theory. SVM has already been marked as a novel development by learning fro m examples based on polynomial function, neural networks, radial basis function, splines and other functions. The performance of each classifier is decided by classificat ion rate (CR). Ou r result confirms the classification ability of LS-SVM technique is much better to classify gender fro m ECG signal analysis in terms of classification rate than SVM .