GDL

ECG Based Human Authentication using Wavelets and Random Forests

ISSN : 1839-8626
Journal from gdlhub / 2017-08-14 11:52:34
By : Noureddine Belgacem1, Amine Nait-Ali2, Regis Fournier2 and Fethi Bereksi-Reguig1, International Journal On Cryptography And Information Security
Created : 2012-07-04, with 1 files

Keyword : ECG; human authentication; wavelet decomposition; random forests.
Subject : ECG Based Human Authentication using Wavelets and Random Forests
Url : http://airccse.org/journal/ijcis/papers/2212ijcis01.pdf
Document Source : Internet

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.

Description Alternative :

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.

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