Browsing by Author "Boukerma, Billal"

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    ECG features extraction using AC/DCT for biometric
    (IEEE, 2017) Cherifi, Dalila; Adjerid, Chaouki; Boukerma, Billal; Zebbiche, Badreddine; Nait-Ali, Amine
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    Hidden biometrics for identification using ECG and EEG signals
    (2016) Adjerid, Chaouki; Boukerma, Billal; Cherifi, .Dalila (supervisor)
    Security concerns increase as the technology for falsification advances and biometrics provides airtight security by identifying an individual based on the physiological and/or behavioral characteristics. Physiological hidden biometrics represented by ECG and EEG biomedical signals are highly confidential, sensitive, and hard to steal and replicate, and also hold great promise to provide a more secure biometric approach for user identification and authentication. This work proposes the human heartbeat as a characteristic to be used for identity recognition. An ECG-based biometric identification system is developed, a method based on autocorrelation (AC) in conjunction with the discrete cosine transform (DCT) proposed for feature extractions from the pre-processed ECG signal. Also studied is the scenario where the proposed system deals with intruder signals in our database. For this goal, a study is performed to adjust the parameters allowing the system to avoid detection failure of false identification and false rejection scenarios. In addition, human brain activities represented by EEG are studied for biometric system purposes. In this study, an EEG-based biometric system is represented by performing a pre- processing stage on the EEG signals, with the features extractions completed using a wavelet packet decomposition and a classification.
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    Multi-class EEG signal classification for epileptic seizure diagnosis
    (Springer, 2020) Cherifi, Dalila; Afoun, Laid; Iloul, Zakaria; Boukerma, Billal; Adjerid, Chaouki; Boubchir, Larbi; Nait-Ali, Amine
    EEG signal recordings are increasingly replacing the old methods of diagnosis in medical field of many neurological disorders. Our contribution in this article is the study and development of EEG signal classification algorithms for epilepsy diagnosis using one rhythm; for classification, an optimum classifier is proposed with only when used one rhythm so that both execution time and number of features are reduced. We used wavelet packet decomposition (WPD) to extract the five rhythms of brain activity from the public Epilepsy-EEG recordings in order to represent each signal with features vector; then we applied on it the well-known classification methods. A statistical study is done to validate the different algorithms