Distinction Between Phases of Human Sleep Cycle Using Neural Networks Based on Bio-signals
Sleep disorders can be monitored by analyzing the various stages of sleep. The stages of human sleep cycle can be broadly classified into three types Awake, rapid eye movement (REM) sleep and non-REM sleep stages. In this work a neural network based method is proposed to distinguish between Awake, REM sleep and non-REM sleep stages. Various types of bio-signals such as electro-occulogram (EOG), electromyogram (EMG), and electroencephalogram (EEG) are used as input to the neural network based method. Accuracy of the proposed neural network based method is found to be 100%. The results of the method are promising, hence can be used to monitor sleep disorders.
- 1.Swetapadma, A., Swain, B.R.: A data mining approach for sleep wave and sleep stage classification. In: IEEE International Conference on Inventive Computation Technologies Coimbatore, pp. 1–6 (2016)Google Scholar
- 2.Huang, C.S., Lin, L., Ko, W., Liu, S.Y., Sua, T.P., Lin, C.T.: A hierarchical classification system for sleep stage scoring via forehead EEG signals. In: IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, Singapore, 1–5 (2013)Google Scholar
- 4.Phan, H., Do, Q., Do, T.L., Vu, D.L.: Metric learning for automatic sleep stage classification. In: 35th Annual International Conference of the IEEE EMBS, pp. 5025–5028 (2013)Google Scholar
- 9.Abraham, A.: Artificial neural networks. In: Sydenham, P.H. (ed.) Handbook of Measuring System Design. Wiley, New York (2005)Google Scholar
- 10.Goldberger, A.L., Amaral, L.A.N., 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), 215–220 (2000)CrossRefGoogle Scholar