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

Autor:	HATEM ZEHIR, TOUFIK HAFS, SARA DAAS, AMINE NAIT-ALI
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	biometrics hidden biometrics security identification ECG machine learning SVM Technology Engineering (General). Civil engineering (General) TA1-2040
Zdroj:	Journal of Engineering Studies and Research, Vol 29, Iss 1 (2023)
Druh dokumentu:	article
ISSN:	2068-7559 2344-4932
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/8ae4bd9a706a42c3b109b04eb03fd030 Zobrazit plný text záznamu View record in DOAJ