Abstract
Diagnosis of sleep disorders is still a challenging issue for a large number of nerve diseases. In this sense, EEG is a powerful tool due to its non-invasive and real-time characteristics. This modality is being more and more used for diagnosis such as for epilepsy. It is also becoming widely used for many Predictive, Preventive and Personalized Medicine (PPPM) applications.
To understand sleep disorders, we propose a method to classify EEG signals in order to detect abnormal behaviours that could reflect a specific modification of the sleep pattern. Our method consists of extracting the characteristics based on temporal and spectral analyses with different descriptors. A classification is then performed based on these features. Validation on a public available database show promizing results with high accuracy levels.
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References
Alickovic, E., Subasi, A.: Ensemble SVM method for automatic sleep stage classification. IEEE Trans. Instrum. Meas. 67(6), 1258–1265 (2018)
Fraiwan, L., Lweesy, K., Khasawneh, N., Wenz, H., Dickhaus, H.: Automated sleep stage identification system based on time–frequency analysis of a single EEG channel and random forest classifier. Comput. Methods Prog. Biomed. 108(1), 10–19 (2012)
Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)
Hassan, A.R., Bashar, S.K., Bhuiyan, M.I.H.: On the classification of sleep states by means of statistical and spectral features from single channel electroencephalogram. In: 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 2238–2243 (2015)
Kayikcioglu, T., Maleki, M., Eroglu, K.: Fast and accurate pls-based classification of EEG sleep using single channel data. Expert Syst. Appl. 42(21), 7825–7830 (2015)
Lajnef, T., Chaibi, S., Ruby, P., Aguera, P.E., Eichenlaub, J.B., Samet, M., Kachouri, A., Jerbi, K.: Learning machines and sleeping brains: automatic sleep stage classification using decision-tree multi-class support vector machines. J. Neurosci. Methods 250, 94–105 (2015)
Li, Y., Yingle, F., Gu, L., Qinye, T.: Sleep stage classification based on EEG Hilbert-Huang transform. In: 2009 4th IEEE Conference on Industrial Electronics and Applications, pp. 3676–3681 (2009)
Sharma, R., Pachori, R.B., Upadhyay, A.: Automatic sleep stages classification based on iterative filtering of electroencephalogram signals. Neural Comput. Appl. 28(10), 2959–2978 (2017)
Tagluk, M.E., Sezgin, N., Akin, M.: Estimation of sleep stages by an artificial neural network employing EEG, EMG and EOG. J. Med. Syst. 34(4), 717–725 (2010)
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Abichou, Y., Chaabene, S., Chaari, L. (2020). A Sleep Monitoring Method with EEG Signals. In: Chaari, L. (eds) Digital Health in Focus of Predictive, Preventive and Personalised Medicine. Advances in Predictive, Preventive and Personalised Medicine, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-030-49815-3_4
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DOI: https://doi.org/10.1007/978-3-030-49815-3_4
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