Abstract
Arousal detection is of great importance to measure sleep quality. Traditional diagnosis of arousal that relies on polysomnography (PSG) signals is cumbersome and costly, requiring an overnight stay in a sleep laboratory with multiple sensors attached to the subjects, which limits widespread adoption. Conversely, electrocardiogram (ECG) signal offers a convenient and affordable alternative due to its ease of integration into portable devices. However, most of methods for automatic arousal detection were based on PSG signals. Besides, they ignored the importance of learning neighborhood information of physiological signals. To address these issues, we propose a Multi-Scale Attention Network called MSANet to detect sleep arousal using single-channel ECG. Specifically, it consists of encoder-decoder structure, multi-scale channel attention module and temporal module. The encoder-decoder structure as the foundational framework is to extract ECG signal features across multiple time scales. The multi-scale channel attention module aims to help the model focus on essential features. In the temporal module, we utilize dilated convolutions to capture neighborhood information in the ECG signal. Besides, considering the rarity of sleep arousal events during the sleep period, we apply focal loss to address class imbalance issue. Experiment results on four publicly available sleep datasets show that our proposed model can achieve a competitive performance for arousal detection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Baranwal, N., Phoebe, K.Y., Siegel, N.S.: Sleep physiology, pathophysiology, and sleep hygiene. Prog. Cardiovasc. Dis. 77, 59–69 (2023)
Berry, R.B., et al.: AASM scoring manual updates for 2017 (version 2.4). J. Clin. Sleep Med. 13(5), 665–666 (2017)
Bild, D.E., et al.: Multi-ethnic study of atherosclerosis: objectives and design. Am. J. Epidemiol. 156(9), 871–881 (2002)
De Chazal, P., Sadr, N.: Automatic scoring of non-apnoea arousals using hand-crafted features from the polysomnogram. Physiol. Meas. 40(12), 124001 (2019)
Erdamar, A., Aksahin, M.F.: Quantitative sleep EEG synchronization analysis for automatic arousals detection. Biomed. Signal Process. Control 59, 101895 (2020)
Faust, O., Razaghi, H., Barika, R., Ciaccio, E.J., Acharya, U.R.: A review of automated sleep stage scoring based on physiological signals for the new millennia. Comput. Methods Programs Biomed. 176, 81–91 (2019)
Fonod, R.: DeepSleep 2.0: automated sleep arousal segmentation via deep learning. AI 3(1), 164–179 (2022)
Ghassemi, M.M., et al.: You snooze, you win: the PhysioNet/computing in cardiology challenge 2018. In: 2018 Computing in Cardiology Conference (CinC), vol. 45, pp. 1–4. IEEE (2018)
Li, A., Chen, S., Quan, S.F., Powers, L.S., Roveda, J.M.: A deep learning-based algorithm for detection of cortical arousal during sleep. Sleep 43(12), zsaa120 (2020)
Li, F., Xu, Y., Zhang, B., Cong, F.: Automated detection of sleep-arousal using multi-scale convolution and self-attention mechanism. Chin. J. Biomed. Eng. 40(1), 27–34 (2023)
Li, H., Cao, Q., Zhong, Y., Pan, Y.: Sleep arousal detection using end-to-end deep learning method based on multi-physiological signals. In: 2018 Computing in Cardiology Conference (CinC), vol. 45, pp. 1–4. IEEE (2018)
Li, H., Guan, Y.: DeepSleep convolutional neural network allows accurate and fast detection of sleep arousal. Commun. Biol. 4(1), 18 (2021)
Li, X., Wang, W., Hu, X., Yang, J.: Selective Kernel networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 510–519 (2019)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Liu, J., et al.: Inception convolution with efficient dilation search. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11486–11495 (2021)
Quan, S.F., et al.: The sleep heart health study: design, rationale, and methods. Sleep 20(12), 1077–1085 (1997)
Rao, M.A., Ghosh, P.K., Bhattacharjee, T., Choudhury, A.D.: Trend statistics network and channel invariant EEG network for sleep arousal study. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5716–5722. IEEE (2019)
Su, Y.N., Kuo, C.E.: An automatic sleep arousal detection method by enhancing U-Net with spatial-channel attention. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 5346–5351. IEEE (2022)
Ugur, T.K., Erdamar, A.: An efficient automatic arousals detection algorithm in single channel EEG. Comput. Methods Programs Biomed. 173, 131–138 (2019)
Wen, P., Xu, Q., Yang, Z., He, Y., Huang, Q.: Exploring the algorithm-dependent generalization of AUPRC optimization with list stability. Adv. Neural. Inf. Process. Syst. 35, 28335–28349 (2022)
Yang, S., Jia, B., Chen, Y., Huang, Z.A., Huang, X., Lv, J.: U-Sleep: a deep neural network for automated detection of sleep arousals using multiple PSGs. In: Yang, H., Pasupa, K., Leung, A.C.S., Kwok, J.T., Chan, J.H., King, I. (eds.) Neural Information Processing: 27th International Conference, ICONIP 2020, Bangkok, Thailand, 23–27 November 2020, Proceedings, Part III 27, pp. 629–640. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63836-8_52
Zabihi, M., Rad, A.B., Särkkä, S., Kiranyaz, S., Katsaggelos, A.K., Gabbouj, M.: Automatic sleep arousal detection using state distance analysis in phase space. In: 2018 Computing in Cardiology Conference (CinC), vol. 45, pp. 1–4. IEEE (2018)
Zan, H., Yildiz, A.: Multi-task learning for arousal and sleep stage detection using fully convolutional networks. J. Neural Eng. 20(5), 056034 (2023)
Zhang, G.Q., et al.: The national sleep research resource: towards a sleep data commons. J. Am. Med. Inform. Assoc. 25(10), 1351–1358 (2018)
Zhang, H., Wang, X., Li, H., Mehendale, S., Guan, Y.: Auto-annotating sleep stages based on polysomnographic data. Patterns 3(1), 100371 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Dai, Y., Lin, Y., Ma, W., Fan, X., Li, Y., Yue, H. (2024). A Multi-scale Attention Network for Sleep Arousal Detection with Single-Channel ECG. In: Peng, W., Cai, Z., Skums, P. (eds) Bioinformatics Research and Applications. ISBRA 2024. Lecture Notes in Computer Science(), vol 14955. Springer, Singapore. https://doi.org/10.1007/978-981-97-5131-0_7
Download citation
DOI: https://doi.org/10.1007/978-981-97-5131-0_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-5130-3
Online ISBN: 978-981-97-5131-0
eBook Packages: Computer ScienceComputer Science (R0)