ECG BASED HUMAN AUTHENTICATION USING WAVELETS AND RANDOM FORESTS


ECG BASED HUMAN AUTHENTICATION USING WAVELETS AND RANDOM FORESTS

Noureddine Belgacem1 , Amine Nait-Ali2 , Régis Fournier2 and Fethi Bereksi-Reguig1 

1Biomedical Engineering Laboratory, Abou Bekr Belkaid University, Tlemcen, Algeria.2 Images Signals and Intelligent Systems Laboratory, UPEC University, France

ABSTRACT 


The electrocardiogram (ECG) is an emerging novel biometric for human identification. It can be combined in a multi-modal biometric identification system or used alone for authentication of subjects. His primary application can be in health care systems where the ECG is used for health measurements. It does furthermore, better than any other biometrics measures, deliver the proof of subject’s being alive as extra information which other biometrics cannot deliver as easily. The main purpose of this study is to present a novel personal authentication approach for human authentication based on their ECG signals. We present a methodology for identity verification that quantifies the minimum number of heartbeats required to authenticate an enrolled individual. The cardiac signals were used to identify a total of 80 individuals obtained from four ECG databases from the Physionet database (MIT-BIH, ST-T, NSR, PTB) and an ECG database collected from 20 student volunteers from Paris Est University. Feature extraction was performed by using Discrete Wavelet Transform (DWT). Wavelets have proved particularly effective for extracting discriminative features in ECG signal classification. The Random Forest was then presented for the ECG signals authentication. 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. 

KEYWORDS 

ECG; human authentication; wavelet decomposition; random forests. 

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