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

Rajesh Ku Tripathy,Ashutosh Acharya,Sumit Kumar Choudhary

doi:10.5923/j.ajsp.20120205.08

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 .

Highlights

Gender is almost its most salient feature, and gender classification according to ECG is one of the most Challenging problems in person identification in Bio metrics[1]
Co mpared with other research topics in Bio metrics, the academic researches on gender classification is less
Fro m last two decades It was observed that a variety of pred ict ion models have been p rop osed in the mach ine learning that include time series models, regression models, adaptive neuro-fuzzy inference systems (ANFIS), artificial neural network (ANN) models and Support Vector Machine (SVM) models[2]

Summary

Introduction

Gender is almost its most salient feature, and gender classification according to ECG is one of the most Challenging problems in person identification in Bio metrics[1]. Due to the effectiveness and smoothness of ANN model, it is widely used in various fields like pattern recognition, regression and classificat ion. SVM have remarkab le generalizat ion performan ce and many mo re adv ant ag es ov er oth er metho ds , an d SVM has attracted attention and gained e xtensive application. Suykens and his group[6] have proposed the use of LS-SVM for simp lification of traditional of SVM. Apart fro m its use in classification in various areas of pattern recognition, it has been extensively used in handling regression problems successfully[7, 8]. In LS-SVM , a set of only linear equation (Linear programming) is solved which is much easier and computationally more simple which made it advantageous than SVM

Methods

Discussion

Conclusion