title: Hardware-efficient robust biometric identification from 0.58 second template and 12 features of limb (Lead I) ECG signal using logistic regression classifier.
abstract: The electrocardiogram (ECG), widely known as a cardiac diagnostic signal, has recently been proposed for biometric identification of individuals; however reliability and reproducibility are of research interest. In this paper, we propose a template matching technique with 12 features using logistic regression classifier that achieved high reliability and identification accuracy. Non-invasive ECG signals were captured using our custom-built ambulatory EEG/ECG embedded device (NeuroMonitor). ECG data were collected from healthy subjects (10), between 25-35 years, for 10 seconds per trial. The number of trials from each subject was 10. From each trial, only 0.58 seconds of Lead I ECG data were used as template. Hardware-efficient fiducial point detection technique was implemented for feature extraction. To obtain repeated random sub-sampling validation, data were randomly separated into training and testing sets at a ratio of 80:20. Test data were used to find the classification accuracy. ECG template data with 12 extracted features provided the best performance in terms of accuracy (up to 100%) and processing complexity (computation time of 1.2ms). This work shows that a single limb (Lead I) ECG can robustly identify an individual quickly and reliably with minimal contact and data processing using the proposed algorithm.
authors.forename: Md Nazmus
authors.initials: MN
authors.lastname: Sahadat
authors.forename: Eddie L
authors.initials: EL
authors.lastname: Jacobs
authors.forename: Bashir I
authors.initials: BI
authors.lastname: Morshed
mesh_terms: D000328:Adult; D000465:Algorithms; D056667:Biometric Identification / Q000379:methods*; D003201:Computers; D004562:Electrocardiography / Q000379:methods*; D004566:Electrodes; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors