Human Authentication System Based on ECG Signal Using FFT and Random Forests

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ECG Based Human Identification Using Random Forests

International audienceSecurity concerns increase as the technology for falsification advances. There are strong evidences that a difficult to falsify biometric trait, the human heartbeat, can be used for identity recognition. Traditional solutions for biometric recognition from electrocardiogram (ECG) signals are limited in its power. They are based on temporal and amplitude distances between detected fiducial points. The current fiducial detection tools are inadequate for this application since the boundaries of waveforms are difficult to detect, locate and define. In this study, the ECG signals were used to identify a total of 120 individuals obtained from four ECG databases obtained from the Physionet database (MIT-BIH, ST-T, NSR, PTB) and an ECG database collected from 40 student volunteers from Paris Est University. Feature extraction from the ECG signals was performed by using Discrete Wavelet Transform (DWT). The Random Forest was then presented for the ECG signals identification. Preliminary experimental results indicate that the system is accurate and can achieve a low false negative rate, low false positive rate and a 100% subject recognition rate for healthy subjects with the reduced set of features

Analysis of ECG Using FFT and random Forests for Human Identification Based on LabVIEW

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A novel biometric authentication approach using ECG and EMG signals

International audienceSecurity 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