Biometric Identification via ECG Signal using Machine Learning based approach

dc.contributor.author	REHMAN, UBAID UR
dc.date.accessioned	2023-08-10T11:05:13Z
dc.date.available	2023-08-10T11:05:13Z
dc.date.issued	2019
dc.identifier.other	00000119934
dc.identifier.uri	http://10.250.8.41:8080/xmlui/handle/123456789/36262
dc.description	Supervisor: Brig. Dr. Javaid Iqbal	en_US
dc.description.abstract	Automatic identification of individuals using biometric features is an area that has gained high importance nowadays. This study presents a novel approach for biometric identification through ECG signal using hybridization of different features and Radial Basis Function Neural Network (RBF-NN). Three different features, namely ARIMA, Wavelet Entropy, and Sample Entropy, are extracted from an ECG dataset. The features are then fed to an RBF-NN to identify different individuals. In the past, these features were used individually for person identification. This paper presents an approach for person identification by hybridization of the features mentioned above. The proposed approach shows promising results with an accuracy of 99.50% to identify 55 individuals correctly	en_US
dc.language.iso	en	en_US
dc.publisher	College of Electrical & Mechanical Engineering (CEME), NUST	en_US
dc.subject	Key Words: Biometric identification; Electrocardiography; Sample entropy; Wavelet entropy; Radial basis function neural network.	en_US
dc.title	Biometric Identification via ECG Signal using Machine Learning based approach	en_US
dc.type	Thesis	en_US