Abstract
The flexible PCB medical device developed at our research lab calculates a single-channel EOG. We develop an infrastructure for our device, including an IoT structure for capturing data. As well as an algorithm that can detect Sleep Stages using EOG data from our device. Previous attempts at classifying sleep always use data from double-channel EOG data. Initially, we used a labelled sleep dataset from the University of Wisconsin to train our neural network. We then apply transfer learning to the sleep classifier with data extracted from our device. Overall, we were able to successfully create a model with data from the medical device and obtain a 81.19% sleep stage classification accuracy.
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References
Debbarma, S., Bhadra, S.: A lightweight flexible wireless electrooculogram monitoring system with printed gold electrodes. IEEE Sens. J. 21(18), 20931–20942 (2021). https://doi.org/10.1109/jsen.2021.3095423
Frishman, L.J.: Electrogenesis of the electroretinogram. In: Retina, pp. 177–201. Elsevier (2013). https://doi.org/10.1016/b978-1-4557-0737-9.00007-2
Malhotra, R.K., Avidan, A.Y.: Sleep stages and scoring technique. In: Atlas of Sleep Medicine, pp. 77–99. Elsevier (2014). https://doi.org/10.1016/b978-1-4557-1267-0.00003-5
Stages of Sleep: REM and Non-REM Sleep Cycles. https://www.webmd.com/sleep-disorders/sleep-101
Dixon, M., Schneider, L., Yu, J., et al.: Sleep-wake detection with a contactless, bedside radar sleep sensing system. Technical report (2021)
Rimminen, H., Amin, A.M., Weadon, T.L., et al.: On-bed differential piezoelectric sensor, February 2021
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition (2015). arXiv: 1512.03385 [cs.CV]
Fan, J., Sun, C., Long, M., et al.: EOGNET: a novel deep learning model for sleep stage classification based on single-channel EOG signal. Front. Neurosci. 15, 573194 (2021)
Zhang, J., Yao, R., Ge, W., et al.: Orthogonal convolutional neural networks for automatic sleep stage classification based on single-channel EEG. Comput. Methods Programs Biomed. 183, 105089 (2020). https://doi.org/10.1016/j.cmpb.2019.105089
Dixon, M., Lee, R.S.: Contactless sleep sensing in Nest Hub, March 2021. https://ai.googleblog.com/2021/03/contactless-sleep-sensing-in-nest-hub.html
Krigolson, O.E., Williams, C.C., Norton, A., et al.: Choosing MUSE: validation of a low-cost, portable EEG system for ERP research. Front. Neurosci. 11 (2017). https://doi.org/10.3389/fnins.2017.00109
Wilkinson, C.M., Burrell, J.I., Kuziek, J.W.P., et al.: Application of the Muse portable EEG system to aid in rapid diagnosis of stroke, June 2020. https://doi.org/10.1101/2020.06.01.20119586
Krigolson, O.E., Williams, C.C., Colino, F.L.: Using portable EEG to assess human visual attention. In: Schmorrow, D.D., Fidopiastis, C.M. (eds.) AC 2017. LNCS (LNAI), vol. 10284, pp. 56–65. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58628-1_5
Berry, R.B., Brooks, R., Gamaldo, C., et al.: AASM scoring manual updates for 2017 (version 2.4). J. Clin. Sleep Med. 13(05), 665–666 (2017). https://doi.org/10.5664/jcsm.6576
Boigne, J., Liyanage, B., Östrem, T.: Recognizing more emotions with less data using self-supervised transfer learning (2020). https://doi.org/10.48550/ARXIV.2011.05585. https://arxiv.org/abs/2011.05585
Kunze, J., Kirsch, L., Kurenkov, I., et al.: Transfer learning for speech recognition on a budget. CoRR abs/1706.00290 (2017). arXiv: 1706.00290
Huh, M., Agrawal, P., Efros, A.A.: What makes ImageNet good for transfer learning? (2016). https://doi.org/10.48550/ARXIV.1608.08614. https://arxiv.org/abs/1608.08614
Morgan, K.K.: What is Polysomnography (PSG)? https://www.webmd.com/sleep-disorders/what-is-polysomnography
Zhang, G.-Q., Cui, L., Mueller, R., et al.: The national sleep research resource: towards a sleep data commons. J. Am. Med. Inf. Assoc. 25(10), 1351–1358 (2018). https://doi.org/10.1093/jamia/ocy064
Young, T., Palta, M., Dempsey, J., et al.: Burden of sleep apnea: rationale, design, and major findings of the Wisconsin Sleep Cohort study. WMJ 108(5), 246–249 (2009)
Virtanen, P., Gommers, R., Oliphant, T.E., et al.: SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020). https://doi.org/10.1038/s41592-019-0686-2
Chawla, N.V., Bowyer, K.W., Hall, L.O., et al.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Welch, P.: The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust. 15(2), 70–73 (1967). https://doi.org/10.1109/tau.1967.1161901
Shanmugam, D., Blalock, D., Balakrishnan, G., et al.: When and why test-time augmentation works. arXiv e-prints, arXiv-2011 (2020)
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Hamza, M., Bhadra, S., Zilic, Z. (2022). Sleep Stage Detection on a Wearable Headband Using Deep Neural Networks. In: González-Vidal, A., Mohamed Abdelgawad, A., Sabir, E., Ziegler, S., Ladid, L. (eds) Internet of Things. GIoTS 2022. Lecture Notes in Computer Science, vol 13533. Springer, Cham. https://doi.org/10.1007/978-3-031-20936-9_15
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