Automatic sleep stage classification using time–frequency images of CWT and transfer learning using convolution neural network

• Autorzy: Jadhav Pankaj , Rajguru Gaurav , Datta Debabrata , Mukhopadhyay Siddhartha

Identyfikatory

DOI

10.1016/j.bbe.2020.01.010

• Języki publikacji: EN

Abstrakty

EN

For automatic sleep stage classification, the existing methods mostly rely on hand-crafted features selected from polysomnographic records. In this paper, the goal is to develop a deep learning-based method by using single channel electroencephalogram (EEG) that automatically exploits the time–frequency spectrum of EEG signal, removing the need for manual feature extraction. The time–frequency RGB color images for EEG signal are extracted using continuous wavelet transform (CWT). The transfer learning of a pre-trained convolution neural network, squeezenet is employed to classify these CWT images into its sleep stages. The proposed method is evaluated using a publicly available Physionet sleep EDFx dataset using single-channel EEG Fpz-Cz channel. Evaluation results show that this method can achieve near state of the art accuracy even using single channel EEG signal.

Słowa kluczowe

EN

sleep stages; continuous wavelet transform; deep learning; convolution neural network; transfer learning

PL

fazy snu; ciągła transformata falkowa; uczenie głębokie; sieć neuronowa konwolucyjna; transfer nauki

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 1
• Strony: 494--504
• Opis fizyczny: Bibliogr. 57 poz., rys., tab.

Twórcy

autor

Jadhav Pankaj

• Homi Bhabha National Institute, Mumbai, India

autor

Rajguru Gaurav

• Bhabha Atomic Research Centre, Mumbai, India

autor

Datta Debabrata

• Homi Bhabha National Institute, Mumbai, India; Bhabha Atomic Research Centre, Mumbai, India

autor

Mukhopadhyay Siddhartha

• Homi Bhabha National Institute, Mumbai, India; Bhabha Atomic Research Centre, Mumbai, India

Bibliografia

• [1] Bajaj V, Pachori RB. Automatic classification of sleep stages based on the time–frequency image of eeg signals. Comput Methods Programs Biomed 2013;112:320–8.
• [2] Berry RB, Brooks R, Gamaldo CE, Harding SM, Marcus C, Vaughn B. The aasm manual for the scoring of sleep and associated events.. Rules, Terminology and Technical Specifications, Darien, Illinois. American Academy of Sleep Medicine; 2012.
• [3] Boostani R, Karimzadeh F, Nami M. A comparative review on sleep stage classification methods in patients and healthy individuals. Comput Methods Programs Biomed 2017;140:77–91.
• [4] Chambon S, Galtier MN, Arnal PJ, Wainrib G, Gramfort A. A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series. IEEE Trans Neural Syst Rehabil Eng 2018.
• [5] Diykh M, Li Y. Complex networks approach for EEG signal sleep stages classification. Expert Syst Appl 2016;63:241–8.
• [6] Ebrahimi F, Mikaeili M, Estrada E, Nazeran H. Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients. In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.. 2008. pp. 1151–4.
• [7] 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 Programs Biomed 2012;108:10–9.
• [8] Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. Physiobank, physiotoolkit, and physionet. Circulation 2000;101:e215–20.
• [9] Goodfellow I, Bengio Y, Courville A, Bengio Y. Deep learning volume 1. MIT press Cambridge; 2016.
• [10] Gramfort A, Luessi M, Larson E, Engemann DA, Strohmeier D, Brodbeck C, Goj R, Jas M, Brooks T, Parkkonen L, et al. Meg and eeg data analysis with mne-python. Front Neurosci 2013;7:267.
• [11] Hassan AR. A comparative study of various classifiers for automated sleep apnea screening based on single-lead electrocardiogram. In 2015 International Conference on Electrical & Electronic Engineering (ICEEE). IEEE; 2015. p. 45–8.
• [12] Hassan AR. Computer-aided obstructive sleep apnea detection using normal inverse gaussian parameters and adaptive boosting. Biomed Signal Process Control 2016;29:22–30.
• [13] Hassan AR, Bashar SK, Bhuiyan MIH. Automatic classification of sleep stages from single-channel electroencephalogram. In 2015 Annual IEEE India Conference (INDICON).. 2015. pp. 1–6.
• [14] Hassan AR, Bashar SK, Bhuiyan MIH. 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). IEEE; 2015. p. 2238–43.
• [15] Hassan AR, Bhuiyan MIH. Automatic sleep stage classification. In 2015 2nd International Conference on Electrical Information and Communication Technologies (EICT). IEEE; 2015. p. 211–6.
• [16] Hassan AR, Bhuiyan MIH. Dual tree complex wavelet transform for sleep state identification from single channel electroencephalogram. In 2015 IEEE International Conference on Telecommunications and Photonics (ICTP). 2015. pp. 1–5.
• [17] Hassan AR, Bhuiyan MIH. Automatic sleep scoring using statistical features in the emd domain and ensemble methods. Biocybernet Biomed Eng 2016;36:248–55.
• [18] Hassan AR, Bhuiyan MIH. Computer-aided sleep staging using complete ensemble empirical mode decomposition with adaptive noise and bootstrap aggregating. Biomed Signal Process Control 2016;24:1–10.
• [19] Hassan AR, Bhuiyan MIH. A decision support system for automatic sleep staging from eeg signals using tunable q-factor wavelet transform and spectral features. J Neurosci Methods 2016;271:107–18.
• [20] Hassan AR, Bhuiyan MIH. Automated identification of sleep states from eeg signals by means of ensemble empirical mode decomposition and random under sampling boosting. Comput Methods Programs Biomed 2017;140:201–10.
• [21] Hassan AR, Bhuiyan MIH. An automated method for sleep staging from eeg signals using normal inverse gaussian parameters and adaptive boosting. Neurocomputing 2017;219:76–87.
• [22] Hassan AR, Haque MA. Computer-aided sleep apnea diagnosis from single-lead electrocardiogram using dual tree complex wavelet transform and spectral features. In 2015 International Conference on Electrical & Electronic Engineering (ICEEE). IEEE; 2015. p. 49–52.
• [23] Hassan AR, Haque MA. Epilepsy and seizure detection using statistical features in the complete ensemble empirical mode decomposition domain. In TENCON 2015-2015 IEEE Region 10 Conference. IEEE; 2015. p. 1–6.
• [24] Hassan AR, Haque MA. Computer-aided obstructive sleep apnea identification using statistical features in the emd domain and extreme learning machine. Biomed Phys Eng Express 2016;2:035003.
• [25] Hassan AR, Haque MA. Computer-aided obstructive sleep apnea screening from single-lead electrocardiogram using statistical and spectral features and bootstrap aggregating. Biocybernet Biomed Eng 2016;36:256–66.
• [26] Hassan AR, Haque MA. Identification of sleep apnea from single-lead electrocardiogram. In 2016 IEEE Intl Conference on Computational Science and Engineering (CSE) and IEEE Intl Conference on Embedded and Ubiquitous Computing (EUC) and 15th Intl Symposium on Distributed Computing and Applications for Business Engineering (DCABES). IEEE; 2016. p. 355–60.
• [27] Hassan AR, Haque MA. An expert system for automated identification of obstructive sleep apnea from single-lead ecg using random under sampling boosting. Neurocomputing 2017;235:122–30.
• [28] Hassan AR, Siuly S, Zhang Y. Epileptic seizure detection in eeg signals using tunable-q factor wavelet transform and bootstrap aggregating. Comput Methods Programs Biomed 2016;137:247–59.
• [29] Hassan AR, Subasi A. A decision support system for automated identification of sleep stages from single- channel eeg signals. Knowledge-Based Syst 2017;128:115–24.
• [30] Iandola FN, Han S., Moskewicz MW, Ashraf K, Dally WJ, Keutzer K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. arXiv e-prints,. arXiv:1602.07360.
• [31] Imtiaz SA, Rodriguez-Villegas E. An open-source toolbox for standardized use of physionet sleep edf expanded database. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2015. pp. 6014–7.
• [32] Kemp B, Zwinderman AH, Tuk B, Kamphuisen HA, Oberye JJ. Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the eeg. IEEE Trans Biomed Eng 2000;47:1185–94.
• [33] Khalighi S, Sousa T, Pires G, Nunes U. Automatic sleep staging: A computer assisted approach for optimal combination of features and polysomnographic channels. Expert Syst Appl 2013;40:7046–59.
• [34] Koley B, Dey D. An ensemble system for automatic sleep stage classification using single channel eeg signal. Comput Biol Med 2012;42:1186–95.
• [35] Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems.. 2012. pp. 1097–105.
• [36] LeCun Y, Kavukcuoglu K, Farabet C, et al. Convolutional networks and applications in vision. In ISCAS volume 2010. 2010. pp. 253–6.
• [37] 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. IEEE; 2009. p. 3676–81.
• [38] Mallat SG. Multiresolution approximations and wavelet orthonormal bases of L2r ðÞ. Trans Am Math Soc 1989;315:69–87.
• [39] Nair V, Hinton GE. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on machine learning (ICML-10).; 2010. p. 807–14.
• [40] Patanaik A, Ong JL, Gooley JJ, Ancoli-Israel S, Chee MW. An end-to-end framework for real-time automatic sleep stage classification. Sleep 2018;41:zsy041.
• [41] Rahman MM, Bhuiyan MIH, Hassan AR. Sleep stage classification using single-channel eog. Comput Biol Med 2018;102:211–20.
• [42] Rechtschaffen A. A manual for standardized terminology, techniques and scoring system for sleep stages in human subjects. Brain Inform Service 1968.
• [43] Sanders TH, McCurry M, Clements MA. Sleep stage classification with cross frequency coupling. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.. 2014. pp. 4579–82.
• [44] Sharma M, Goyal D, Achuth P, Acharya UR. An accurate sleep stages classification system using a new class of optimally time–frequency localized three-band wavelet filter bank. Comput Biol Med 2018;98:58–75.
• [45] Shi J, Liu X, Li Y, Zhang Q, Li Y, Ying S. Multi-channel eeg-based sleep stage classification with joint collaborative representation and multiple kernel learning. J Neurosci Methods 2015;254:94–101.
• [46] Silva I, Moody GB. An open-source toolbox for analysing and processing physionet databases in matlab and octave. J Open Res Softw 2014;2.
• [47] Sim oes H, Pires G, Nunes U, Silva V. Feature extraction and selection for automatic sleep staging using eeg. In ICINCO (3). 2010. pp. 128–33.
• [48] Sors A, Bonnet S, Mirek S, Vercueil L, Payen J-F. A convolutional neural network for sleep stage scoring from raw single-channel eeg. Biomed Signal Process Control 2018;42:107–14.
• [49] Stochholm A, Mikkelsen K, Kidmose P. Automatic sleep stage classification using ear-eeg. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2016. pp. 4751–4.
• [50] Supratak A, Dong H, Wu C, Guo Y. Deepsleepnet: A model for automatic sleep stage scoring based on raw single-channel eeg. IEEE Trans Neural Syst Rehabil Eng 2017;25:1998–2008.
• [51] Torrence C, Compo GP. A practical guide to wavelet analysis. Bull Am Meteorol Soc 1998;79:61–78.
• [52] Tsinalis O, Matthews PM, Guo Y, Zafeiriou S. Automatic sleep stage scoring with single-channel eeg using convolutional neural networks; 2016, arXiv preprint arXiv:1610.01683.
• [53] Tzimourta KD, Tsilimbaris A, Tzioukalia K, Tzallas AT, Tsipouras MG, Astrakas LG, Giannakeas N. Eeg-based automatic sleep stage classification. Biomed J 2018;1:6.
• [54] Van Sweden B, Kemp B, Kamphuisen H, Van der Velde E. Alternative electrode placement in (automatic) sleep scoring (f pz-cz/p z-oz versus c4-at). Sleep 1990;13:279–83.
• [55] Wang Z, Oates T. Encoding time series as images for visual inspection and classification using tiled convolutional neural networks. In Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence. volume 1; 2015.
• [56] Zhang J, Wu Y. Complex-valued unsupervised convolutional neural networks for sleep stage classification. Comput Methods Programs Biomed 2018;164:181–91.
• [57] Zhang J, Wu Y, Bai J, Chen F. Automatic sleep stage classification based on sparse deep belief net and combination of multiple classifiers. Trans Inst Measure Control 2016;38:435–51.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).