Abstract
This research used a classical deep feedforward neural network (DFFNN) for an automatic sleep stage scoring based on a single-channel EEG signal. It used an open-available dataset, randomly selecting one healthy young adult for both training (≈5%) and evaluation (≈95%). The research also augmented the validation by using 5-fold cross validations for the result comparisons. It introduced a new method for inferring the trained network based on a ROM module (memory concept), so it would be faster than directly inferring the trained Deep Neural Network (DNN). The ROM content is filled after the DNN network is trained by the training set and inferred using the testing set. An accuracy of 97% was achieved in inferring the test datasets using ROM when compared to the classic trained DNN inference process.
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Medic, G., Wille, M., Hemels, M.E.: Short-and long-term health consequences of sleep disruption. Nat. Sci. Sleep 9, 151 (2017)
Abbott, S.M., Videnovic, A.: Chronic sleep disturbance and neural injury: links to neurodegenerative disease. Nat. Sci. Sleep 8, 55 (2016)
Wulff, K., Gatti, S., Wettstein, J.G., Foster, R.G.: Sleep and circadian rhythm disruption in psychiatric and neurodegenerative disease. Nat. Rev. Neurosci. 11(8), 589 (2010)
Moser, D., Anderer, P., Gruber, G., Parapatics, S., Loretz, E., Boeck, M., Saletu, B.: Sleep classification according to AASM and Rechtschaffen & Kales: effects on sleep scoring parameters. Sleep 32(2), 139–149 (2009)
Berry, R.B., Brooks, R., Gamaldo, C.E., Harding, S.M., Marcus, C.L., Vaughn, B.V.: The AASM manual for the scoring of sleep and associated events. Rules, Terminology and Technical Specifications, Darien, Illinois, American Academy of Sleep Medicine (2012)
Rosenberg, R.S., Van Hout, S.: The American academy of sleep medicine inter-scorer reliability program: sleep stage scoring. J. Clin. Sleep Med. 9(01), 81–87 (2013)
Lajnef, T., Chaibi, S., Ruby, P., Aguera, P.E., Eichenlaub, J.B., Samet, M., Jerbi, K.: Learning machines and sleeping brains: automatic sleep stage classification using decision-tree multi-class support vector machines. J. Neurosci. Meth. 250, 94–105 (2015)
Huang, C.S., Lin, C.L., Ko, L.W., Liu, S.Y., Su, T.P., Lin, C.T.: Knowledge-based identification of sleep stages based on two forehead electroencephalogram channels. Front. Neurosci. 8, 263 (2014)
Güneş, S., Polat, K., Yosunkaya, Ş.: Efficient sleep stage recognition system based on EEG signal using k-means clustering based feature weighting. Expert Syst. Appl. 37(12), 7922–7928 (2010)
PhysioNet: The Sleep-EDF database Expanded. http://www.physionet.org/physiobank/database/sleep-edfx/
Team, D.: Deeplearning4j: Open-source distributed deep learning for the jvm. Apache Software Foundation License, 2 (2016)
Hsu, Y.L., Yang, Y.T., Wang, J.S., Hsu, C.Y.: Automatic sleep stage recurrent neural classifier using energy features of EEG signals. Neurocomputing 104, 105–114 (2013)
Hillman, D.R., Murphy, A.S., Antic, R., Pezzullo, L.: The economic cost of sleep disorders. Sleep 29(3), 299–305 (2006)
Biswal, S., Kulas, J., Sun, H., Goparaju, B., Westover, M.B., Bianchi, M.T., Sun, J.: SLEEPNET: automated sleep staging system via deep learning. arXiv preprint (2017). arXiv:1707.08262
Hsu, Y.L., Yang, Y.T., Wang, J.S., Hsu, C.Y.: Automatic sleep stage recurrent neural classifier using energy features of EEG signals. Neurocomputing 104, 105–114 (2013)
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. Measur. Control 38(4), 435–451 (2016)
Tsinalis, O., Matthews, P.M., Guo, Y., Zafeiriou, S.: Automatic sleep stage scoring with single-channel EEG using convolutional neural networks. arXiv preprint (2016). arXiv:1610.01683
Tsinalis, O., Matthews, P.M., Guo, Y.: Automatic sleep stage scoring using time-frequency analysis and stacked sparse autoencoders. Ann. Biomed. Eng. 44(5), 1587–1597 (2016)
Yulita, I.N., Fanany, M.I., Arymuthy, A.M.: Bi-directional long short-term memory using quantized data of deep belief networks for sleep stage classification. Procedia Comput. Sci. 116, 530–538 (2017)
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. 25(11), 1998–2008 (2017)
Längkvist, M., Karlsson, L., Loutfi, A.: Sleep stage classification using unsupervised feature learning. Adv. Artif. Neural Syst. 2012, 5 (2012)
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 42, 107–114 (2018)
Paisarnsrisomsuk, S., Sokolovsky, M., Guerrero, F., Ruiz, C., Alvarez, S.A.: Deep Sleep: Convolutional Neural Networks for Predictive Modeling of Human Sleep Time Signals (2018)
Procházka, A., Kuchyňka, J., Vyšata, O., Cejnar, P., Vališ, M., Mařík, V.: Multi-class sleep stage analysis and adaptive pattern recognition. Appl. Sci. 8(5), 2076–3417 (2018)
Stephansen, J.B., Ambati, A., Leary, E.B., Moore, H.E., Carrillo, O., Lin, L., Pizza, F.: The use of neural networks in the analysis of sleep stages and the diagnosis of narcolepsy. arXiv preprint (2017). arXiv:1710.02094
Siddique, N., Adeli, H.: Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing. John Wiley & Sons, New Jersey (2013)
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AlMeer, M.H., Hassen, H., Nawaz, N. (2020). ROM-Based Deep Learning Inference for Sleep Stage Classification. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1037. Springer, Cham. https://doi.org/10.1007/978-3-030-29516-5_66
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