Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
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)
Khushaba, R.N., Kodagoda, S., Lal, S., Dissanayake, G.: Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm. IEEE Trans. Biomed. Eng. 58(1), 121–131 (2011)
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)
Redmond, S.J., Heneghan, C.: Cardio respiratory-Based Sleep Staging in Subjects With Obstructive Sleep Apnea. IEEE Trans. Biomed. Eng. 53(3), 485–496 (2006)
Yilmaz, B., Asyali, M.H., Arikan, E., Yetkin, S., Özgen, F.: Sleep stage and obstructive apneaic epoch classification using single-lead ECG. Biomed. Eng. Online 9, 39 (2010)
Eiseman, N.A., Westover, M.B., Mietus, J.E., Thomas, R.J., Bianchi, M.T.: Classification algorithms for predicting sleepiness and sleep apnea severity. J. Sleep Res. 21, 101–112 (2010)
Khandoker, A.H., Palaniswami, M., Karmakar, C.K.: Support vector machines for automated recognition of obstructive sleep apnea syndrome from ECG recordings. IEEE Trans. Inf Technol. Biomed. 13(1), 37–48 (2009)
Abraham, A.: Artificial neural networks. In: Sydenham, P.H. (ed.) Handbook of Measuring System Design. Wiley, New York (2005)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Trishita, Bhatia, S.K., Kumar, G., Swetapadma, A. (2019). Distinction Between Phases of Human Sleep Cycle Using Neural Networks Based on Bio-signals. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 755. Springer, Singapore. https://doi.org/10.1007/978-981-13-1951-8_12
Download citation
DOI: https://doi.org/10.1007/978-981-13-1951-8_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1950-1
Online ISBN: 978-981-13-1951-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)