SUPPORT VECTOR MACHINE FOR HUMAN IDENTIFICATION BASED ON NON-FIDUCIAL FEATURES OF THE ECG

Abstract

The demand for reliable identification systems has grown recently. Using the mean frequency, median frequency, band power, and Welch power spectral density (PSD) of ECG data, we proposed a novel biometric approach in this study. ECG signals are more secure than other traditional biometric modalities because they are impossible to forge and duplicate. Three different support vector machine classifiers—linear SVM, quadratic SVM, and cubic SVM—are employed for the classification. The MIT-BIH arrhythmia database is used to evaluate the suggested method's precision. For the linear SVM, quadratic SVM, and cubic SVM, respectively, test accuracy of 93.6%, 96.4%, and 97.0% was obtained.

Keywords

• biometrics, hidden biometrics, security, identification, ECG, machine learning, SVM