A novel biometric authentication approach using ECG and EMG signals

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.

One-Lead Electrocardiogram for Biometric Authentication using Time Series Analysis and Support Vector Machine

“…Another analysis method in the frequency domain on ECG biometric is Fourier transform as reported in the study [13] informing that the Fast Fourier Transform (FFT) method combined with the nearest neighbour classifier had a good performance for the ECG"s biometric. The FFT method was also used in the simultaneous ECG and electromyogram www.ijacsa.thesai.org (EMG) wave based biometric studies by Belgacem [14] with the Optimum-Path Forest classifier for authentication.…”

Autenticação Contínua e Segura Baseada em Sinais PPG e Comunicação Galvânica

“…A acurácia não foi expressiva em nenhum dos modelos e o posicionamento dos sensores é um obstáculo. O ECG avalia os impulsos elétricos do coração e serve de base para autenticação em vários trabalhos [Belgacem et al 2015, Lourenço et al 2011, Singh and Singh 2012. Entretanto, a coleta do ECG é inflexível quanto a necessidade de medições paralelas em ambos os lados do corpo.…”

“…O uso do ECG (eletrocardiograma) [Blasco et al 2016] é um exemplo de biometria explorada na autenticação contínua. Porém, o procedimento de coleta do sinal ECG pode ser inconveniente, visto que o ECG é coletado por eletrodos conectados à pele do usuário e exige ao menos três pontos de contato em lados diferentes do corpo para coletar a frequência cardíaca [Belgacem et al 2015]. O uso contínuo dos eletrodos traz incômodo ao usuário.…”

An Improved Behavioral Biometric System based on Gait and ECG signals

“…have been developed during past decades in [26,27,28,29]. so far, there are only a few works about a multimodal biometric system that includes behavior with physical traits (M. Derawi [30][31][32][33].…”