Abstract
Sleep disorder diseases have one of the major health issues across the world. To handle this issue, the primary step taken by most of the sleep experts is the sleep staging classification. In this paper, we proposed an automated deep one-dimensional convolution neural network (1D-CNN) for multi-class sleep stages through polysomnographic signals. The proposed 1D-CNN model comprises eleven layers with learnable parameters: nine convolution layers and two-fully connected layers. The main objective of designing such a 1D-CNN model is to achieve higher classification accuracy for multiple sleep stage classifications with reduced learnable parameters. The proposed network architecture is tested on two different subgroups subject sleep recordings of ISRUC-Sleep datasets, namely ISRUC-Sleep subgroup-I (SG-I) and ISRUC-Sleep subgroup-III (SG-III). The proposed deep 1D-CNN model achieved the highest classification accuracy of 98.44, 99.03, 99.50, and 99.03% using the ISRUC-Sleep SG-I dataset and 98.51, 98.88, 98.76, and 98.67% using SG-III dataset for two to five sleep stage classification, respectively, with single channel of EEG signals. It has been observed that the obtained results from the proposed 1D-CNN model give the best classification accuracy performance on multiple sleep stage classifications incomparable to the existing literature works. The developed 1D-CNN deep learning architecture is ready for clinical usage with high PSG data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Heyat MBB, Lai D, Khan FI, Zhang Y (2019) Sleep bruxism detection using decision tree method by the combination of C4-P4 and C4-A1 channels of scalp EEG. IEEE Access 7:102542–102553. https://doi.org/10.1109/ACCESS.2019.2928020
Chung MH, Kuo TB, Hsu N, Chu H, Chou KR, Yang CC (2009) Sleep and autonomic nervous system changes—enhanced cardiac sympathetic modulations during sleep in permanent night shift nurses. Scand J Work Environ Health 35(3):180–187. https://doi.org/10.5271/sjweh.1324
Aboalayon K, Faezipour M, Almuhammadi W, Moslehpour S (2016) Sleep stage classification using EEG signal analysis: a comprehensive survey and new investigation. Entropy 18(9):272. https://doi.org/10.3390/e18090272
Reynolds CF, O’Hara R (2013) DSM-5 sleep-wake disorders classification: overview for use in clinical practice. Am J Psychiatry 170(10):1099–1101. https://doi.org/10.1176/appi.ajp.2013.13010058
Goel N, Rao H, Durmer J, Dinges D (2009) Neurocognitive consequences of sleep deprivation. Semin Neurol 29(04):320–339. https://doi.org/10.1055/s-0029-1237117
Garcés Correa A, Orosco L, Laciar E (2014) Automatic detection of drowsiness in EEG records based on multimodal analysis. Med Eng Phys 36(2):244–249. https://doi.org/10.1016/j.medengphy.2013.07.011
Kogure T, Shirakawa S, Shimokawa M, Hosokawa Y (2011) Automatic sleep/wake scoring from body motion in bed: validation of a newly developed sensor placed under a mattress. J Physiol Anthropol 30(3):103–109. https://doi.org/10.2114/jpa2.30.103
Rosenberg RS, Van Hout S (2013) The american academy of sleep medicine inter-scorer reliability program: sleep stage scoring. J Clin Sleep Med 9(1):81–87. https://doi.org/10.5664/jcsm.2350
Boashash B, Ouelha S (2016) Automatic signal abnormality detection using time-frequency features and machine learning: a newborn EEG seizure case study. Knowl-Based Syst 106:38–50. https://doi.org/10.1016/j.knosys.2016.05.027
Penzel T, Conradt R (2000) Computer based sleep recording and analysis. Sleep Med Rev 4(2):131–148. https://doi.org/10.1053/smrv.1999.0087 PMID: 12531163
Li Y, Luo M-L, Li K (2016) A multiwavelet-based time-varying model identification approach for time–frequency analysis of EEG signals. Neurocomputing 193:106–114. https://doi.org/10.1016/j.neucom.2016.01.062
Holland JV, Dement WC, Raynal DM (1974) Polysomnography: a response to a need for improved communication. Presented at the 14th Annual Meeting Association Psychophysiology Study Sleep. [Online]
Acharya UR, Bhat S, Faust O, Adeli H, Chua EC-P, Lim WJE, Koh JEW (2015) Nonlinear dynamics measures for automated EEG-based sleep stage detection. Eur Neurol 74(5–6):268–287. https://doi.org/10.1159/000441975
Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H (2018) Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput Biol Med 100:270–278. https://doi.org/10.1016/j.compbiomed.2017.09.017
Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H, Subha DP (2018) Automated EEG-based screening of depression using deep convolutional neural network. Comput Methods Prog Biomed 161:103–113. https://doi.org/10.1016/j.cmpb.2018.04.012
Acharya UR, Vinitha Sree S, Swapna G, Martis RJ, Suri JS (2013) Automated EEG analysis of epilepsy: a review. Knowl Based Syst 45:147–165. https://doi.org/10.1016/j.knosys.2013.02.014
Ahmadlou M, Adeli H, Adeli A (2011) Fractality and a wavelet-chaos-methodology for EEG-based diagnosis of Alzheimer disease. Alzheimer Dis Assoc Disord 25(1):85–92. https://doi.org/10.1097/WAD.0b013e3181ed1160
Tsinalis O, Matthews PM, Guo Y, Zafeiriou S (2016) Automatic sleep stage scoring with single-channel eeg using convolutional neural networks; arXiv preprint arXiv:1610.01683
Sors A, Bonnet S, Mirek S, Vercueil L, Payen J-F (2018) A convolutional neural network for sleep stage scoring from raw single-channel eeg. Biomed Signal Process Control 42:107–114. https://doi.org/10.1016/j.bspc.2017.12.001
Chambon S, Galtier MN, Arnal PJ, Wainrib G, Gramfort A (2018) A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series. IEEE Trans Neural Syst Rehabil Eng 26(4):758–769. https://doi.org/10.1109/TNSRE.2018.2813138
Fernández-Varela I, Hernández-Pereira E, Moret-Bonillo V (2018) A convolutional network for the classification of sleep stages. Proceedings 2(18):1174. https://doi.org/10.3390/proceedings2181174
Tripathy RK, Rajendra Acharya U (2018) Use of features from RR-time series and EEG signals for automated classification of sleep stages in deep neural network framework. Biocybern Biomed Eng. https://doi.org/10.1016/j.bbe.2018.05.005
Cui Z, Zheng X, Shao X, Cui L (2018) Automatic sleep stage classification based on convolutional neural network and finegrained segments. Hindawi Complex 9248410. https://doi.org/10.1155/2018/9248410
Supratak A, Dong H, Wu C, Guo Y (2017) DeepSleepNet: a model for automatic sleep stage scoring based on raw single-channel EEG. IEEE Trans Neural Syst Rehabil Eng 25(11):1998–2008. https://doi.org/10.1109/TNSRE.2017.2721116
Khalighi S, Sousa T, Santos JM, Nunes U (2016) (2016) ISRUC-Sleep: a comprehensive public dataset for sleep researchers. Comput Methods Programs Biomed 124:180–192. https://doi.org/10.1016/j.cmpb.2015.10.013
Bajaj V, Pachori RB (2013) Automatic classification of sleep stages based on the time-frequency image of EEG signals. Comput Methods Programs Biomed 112(3):320–328. https://doi.org/10.1016/j.cmpb.2013.07.006
Yıldız A, Akin M, Poyraz M, Kirbas G (2009) Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction. Expert Syst Appl 36:7390–7399. https://doi.org/10.1016/j.eswa.2008.09.003
Sanders TH, McCurry M, Clements MA (2014) Sleep stage classification with cross frequency coupling. Annu Int Conf IEEE Eng Med Biol Soc 2014:4579–4582. https://doi.org/10.1109/EMBC.2014.6944643
Powers D, Ailab (2011) Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J Mach Learn Technol 2:2229–3981. https://doi.org/10.9735/2229-3981
Yildirim O, Baloglu U, Acharya U (2019) A deep learning model for automated sleep stages classification using PSG signals. Int J Environ Res Public Health 16(4):599. https://doi.org/10.3390/ijerph16040599
Fernandez-Blanco E, Rivero D, Pazos A (2019) Convolutional neural networks for sleep stage scoring on a two-channel EEG signal. Soft Comput. https://doi.org/10.1007/s00500-019-04174-1
Ieracitano C, Mammone N, Bramanti A, Hussain A, Morabito F (2018) A convolutional neural network approach for classification of Dementia stages based on 2D-spectral representation of EEG recordings. Neurocomputing 323. https://doi.org/10.1016/j.neucom.2018.09.071
Nagabushanam P, Thomas George S, Radha S (2019) EEG signal classification using LSTM and improved neural network algorithms. Soft Comput. https://doi.org/10.1007/s00500-019-04515-0
Michielli N, Acharya UR, Molinari F (2019) Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals. Comput Biol Med 106:71–81. https://doi.org/10.1016/j.compbiomed.2019.01.013
Li X, La R, Wang Y, Niu J, Zeng S, Sun S, Zhu J (2019) EEG-based mild depression recognition using convolutional neural network. Med Biol Eng Comput. https://doi.org/10.1007/s11517-019-01959-2
Banluesombatkul N, Ouppaphan P, Leelaarporn P, Lakhan P, Chaitusaney B, Jaimchariyatam N, Chuangsuwanich E, Chen W, Phan H, Dilokthanakul N, Wilaiprasitporn T (2020) MetaSleepLearner: a pilot study on fast adaptation of bio-signals-based sleep stage classifier to new individual subject using meta-learning
Mousavi Z, Yousefi Rezaii T, Sheykhivand S, Farzamnia A, Razavi SN (2019) Deep convolutional neural network for classification of sleep stages from single-channel EEG signals. J Neurosci Methods 108312. https://doi.org/10.1016/j.jneumeth.2019.108312
Zhang X, Xu M, Li Y, Su M, Xu Z, Wang C, et al, (2020) Automated multi-model deep neural network for sleep stage scoring with unfiltered clinical data. Sleep Breath. https://doi.org/10.1007/s11325-019-02008-w
Nakamura T, Adjei T, Alqurashi Y, Looney D, Morrell MJ, Mandic DP (2017) Complexity science for sleep stage classification from EEG. In: 2017 international joint conference on neural networks (IJCNN). https://doi.org/10.1109/ijcnn.2017.796641
Hassan AR, Bhuiyan MIH (2017) Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting. Comput Methods Programs Biomed 140:201–210. https://doi.org/10.1016/j.cmpb.2016.12.015
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Satapathy, S.K., Sharathkumar, S., Loganathan, D. (2021). Automated Sleep Staging Using Convolution Neural Network Based on Single-Channel EEG Signal. In: Sharma, H., Gupta, M.K., Tomar, G.S., Lipo, W. (eds) Communication and Intelligent Systems. Lecture Notes in Networks and Systems, vol 204. Springer, Singapore. https://doi.org/10.1007/978-981-16-1089-9_51
Download citation
DOI: https://doi.org/10.1007/978-981-16-1089-9_51
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-1088-2
Online ISBN: 978-981-16-1089-9
eBook Packages: EngineeringEngineering (R0)