A novel biometric authentication approach using ECG and EMG signals

• Published in: Journal of medical engineering & technology Vol. 39; no. 4; pp. 226 - 238
• Main Authors: Belgacem, Noureddine, Fournier, Régis, Nait-Ali, Amine, Bereksi-Reguig, Fethi
• Format: Journal Article
• Language: English
• Published: England Informa UK Ltd 01.05.2015; Informa Healthcare
• Subjects: Adult; Aged; Aged, 80 and over; Biometric Identification; Databases, Factual; ECG; Electrocardiography; Electromyography; features extraction; Female; Hand - physiology; human authentication; Humans; Male; Middle Aged; OPF classifier; Young Adult; ECG; human authentication; OPF classifier; features extraction
• ISSN: 0309-1902; 1464-522X; 1464-522X
• DOI: 10.3109/03091902.2015.1021429

Abstract Security biometrics is a secure alternative to traditional methods of identity verification of individuals, such as authentication systems based on user name and password. Recently, it has been found that the electrocardiogram (ECG) signal formed by five successive waves (P, Q, R, S and T) is unique to each individual. In fact, better than any other biometrics' measures, it delivers proof of subject's being alive as extra information which other biometrics cannot deliver. The main purpose of this work is to present a low-cost method for online acquisition and processing of ECG signals for person authentication and to study the possibility of providing additional information and retrieve personal data from an electrocardiogram signal to yield a reliable decision. This study explores the effectiveness of a novel biometric system resulting from the fusion of information and knowledge provided by ECG and EMG (Electromyogram) physiological recordings. It is shown that biometrics based on these ECG/EMG signals offers a novel way to robustly authenticate subjects. Five ECG databases (MIT-BIH, ST-T, NSR, PTB and ECG-ID) and several ECG signals collected in-house from volunteers were exploited. A palm-based ECG biometric system was developed where the signals are collected from the palm of the subject through a minimally intrusive one-lead ECG set-up. A total of 3750 ECG beats were used in this work. Feature extraction was performed on ECG signals using Fourier descriptors (spectral coefficients). Optimum-Path Forest classifier was used to calculate the degree of similarity between individuals. The obtained results from the proposed approach look promising for individuals' authentication.