Automated detection of sleep-arousal using multi-scale convolution and self-attention mechanism_Journal of Biomedical Engineering

Authors：

LI Fan ¹ , XU Yan ² ,  ZHANG Bin ² , CONG Fengyu ^1,3,4

1. School of Biomedical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, P. R. China;
2. Department of Psychiatry, Nanfang Hospital, Southern Medical University, Guangzhou 510515, P. R. China;
3. School of Artificial Intelligence, Dalian University of Technology, Dalian, Liaoning 116024, P. R. China;
4. Key Laboratory of Integrated Circuit and Biomedical Electronic System, Liaoning Province. Dalian University of Technology, Dalian, Liaoning 116024, P. R. China;

Corresponding author：

ZHANG Bin, Email: zhang73bin@hotmail.com

Keywords：

Arousal events detection; Electroencephalogram; Multi-scale convolution; Self-attention; Obstructive sleep apnea

DOI：

10.7507/1001-5515.202204052

Video：

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In clinical, manually scoring by technician is the major method for sleep arousal detection. This method is time-consuming and subjective. This study aimed to achieve an end-to-end sleep-arousal events detection by constructing a convolutional neural network based on multi-scale convolutional layers and self-attention mechanism, and using 1 min single-channel electroencephalogram (EEG) signals as its input. Compared with the performance of the baseline model, the results of the proposed method showed that the mean area under the precision-recall curve and area under the receiver operating characteristic were both improved by 7%. Furthermore, we also compared the effects of single modality and multi-modality on the performance of the proposed model. The results revealed the power of single-channel EEG signals in automatic sleep arousal detection. However, the simple combination of multi-modality signals may be counterproductive to the improvement of model performance. Finally, we also explored the scalability of the proposed model and transferred the model into the automated sleep staging task in the same dataset. The average accuracy of 73% also suggested the power of the proposed method in task transferring. This study provides a potential solution for the development of portable sleep monitoring and paves a way for the automatic sleep data analysis using the transfer learning method.

Citation： LI Fan, XU Yan, ZHANG Bin, CONG Fengyu. Automated detection of sleep-arousal using multi-scale convolution and self-attention mechanism. Journal of Biomedical Engineering, 2023, 40(1): 27-34. doi: 10.7507/1001-5515.202204052 Copy

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21.	Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need//Neural Information Processing Systems (NIPS), Long Beach, 2017: 6000–6010. DOI: 10.48550/arXiv.1706.03762.
22.	Cheng Jianpeng, Dong L, Lapata M. Long short-term memory-networks for machine reading// Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP), Austin, 2016: 551–561. DOI: 10.18653/v1/d16-1053.
23.	Li F, Tang H, Shang S, et al. Classification of heart sounds using convolutional neural network. Applied Sciences, 2020, 10(11): 3956.
24.	Bonnet M H, Arand D L. EEG arousal norms by age. Journal of Clinical Sleep Medicine, 2007, 3(3): 271-274.
25.	Fonod R. DeepSleep 2. 0: automated sleep arousal segmentation via deep learning. AI, 2022, 3(1): 164-179.
26.	He R, Wang K, Zhao N, et al. Identification of arousals with deep neural networks (DNNs) using dfferent physiological signals//2018 Computing in Cardiology Conference (CinC), Maastricht: IEEE, 2018. DOI: 10.22489/CinC.2018.060.

1. Vaccaro A, Kaplan Dor Y, Nambara K, et al. Sleep loss can cause death through accumulation of reactive oxygen species in the gut. Cell, 2020, 181(6): 1307-1328.
2. Benjafield A V, Ayas N T, Eastwood P R, et al. Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. The Lancet Respiratory Medicine, 2019, 7(8): 687-698.
3. Senaratna C V, Perret J L, Lodge C J, et al. Prevalence of obstructive sleep apnea in the general population: a systematic review. Sleep Medicine Reviews, 2017, 34(70): 70-81.
4. Berry R B, Gleeson K. Respiratory arousal from sleep: mechanisms and significance. Sleep, 1997, 20(8): 654-675.
5. 董霄松. 2006北京国际睡眠医学论坛//觉醒和呼吸性微觉醒的判读及其临床意义,北京: 中华医学会继续教育部, 2006: 174–176.
6. 曹征涛, 杨军, 朱莹莹, 等. 一种基于微动敏感床垫的识别与呼吸事件相关的微觉醒的新算法. 仪器仪表学报, 2008, 29(4): 378-381.
7. Barbé None, Pericás J, Muñoz A, et al. Automobile accidents in patients with sleep apnea syndrome: an epidemiological and mechanistic study. Am J Respir Crit Care Med, 1998, 158(1): 18-22.
8. Berry R B, Quan S F, Abreu A R, et al. The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications. Version 2.6. Darien: Americal Academy of Sleep Medicine, 2020.
9. Mccormick D A, Bal T. Sleep and arousal: thalamocortical mechanisms. Annual Review of Neuroscience, 1997, 20: 185.
10. 中国医师协会神经内科医师分会睡眠障碍专业委员会, 中国睡眠研究会睡眠障碍专业委员会, 中华医学会神经病学分会睡眠障碍学组. 中国成人多导睡眠监测技术操作规范及临床应用专家共识. 中华医学杂志, 2018, 98(47): 3825-3831.
11. Bonnet M H, Doghramji K, Roehrs T, et al. The scoring of arousal in sleep: reliability, validity, and alternatives. Journal of Clinical Sleep Medicine, 2007, 3(2): 133-145.
12. Li A, Chen S, Quan S F, et al. A deep learning-based algorithm for detection of cortical arousal during sleep. Sleep, 2020, 43(12): zsaa120.
13. Simonnet M, Gourvennec B, BillotI R. Connected heart rate sensors to monitor sleep quality: electrodes, chest belt and smartwatch users acceptability//2016 IEEE 1st International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), Washington: IEEE, 2016: 344–345.
14. Howe-Patterson M, Pourbabaee B, Benard F. Automated detection of sleep arousals from polysomnography data using a dense convolutional neural network//2018 Computing in Cardiology Conference (CinC), Maastricht: IEEE, 2018. DOI: 10.22489/CinC.2018.232.
15. Zabihi M, Rad A B, Kiranyaz S, et al. 1D convolutional neural network models for sleep arousal detection. ArXiv, 2019. DOI: 10.48550/arXiv.1903.01552.
16. Miller D, Ward A, Bambos N. Automatic sleep arousal identification from physiological waveforms using deep learning// 2018 Computing in Cardiology Conference (CinC), Maastricht: IEEE, 2018. DOI: 10.22489/CinC.2018.242.
17. Olesen A N, Jennum P, Mignot E, et al. Deep transfer learning for improving single-EEG arousal detection//42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Montreal: IEEE, 2020: 99-103.
18. Chien Y R, Wu C H, Taso H W. Automatic sleep-arousal detection with single-lead EEG using stacking ensemble learning. Sensors, 2021, 21(18): 6049.
19. Zhang G Q, Cui L, Mueller R, et al. The national sleep research resource: towards a sleep data commons. Journal of the American Medical Informatics Association, 2018, 25(10): 1351-1358.
20. Li F, Yan R, Mahini R, et al. End-to-end sleep staging using convolutional neural network in raw single-channel EEG. Biomedical Signal Processing and Control, 2021. DOI: 10.1016/j.bspc.2020.102203.
21. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need//Neural Information Processing Systems (NIPS), Long Beach, 2017: 6000–6010. DOI: 10.48550/arXiv.1706.03762.
22. Cheng Jianpeng, Dong L, Lapata M. Long short-term memory-networks for machine reading// Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP), Austin, 2016: 551–561. DOI: 10.18653/v1/d16-1053.
23. Li F, Tang H, Shang S, et al. Classification of heart sounds using convolutional neural network. Applied Sciences, 2020, 10(11): 3956.
24. Bonnet M H, Arand D L. EEG arousal norms by age. Journal of Clinical Sleep Medicine, 2007, 3(3): 271-274.
25. Fonod R. DeepSleep 2. 0: automated sleep arousal segmentation via deep learning. AI, 2022, 3(1): 164-179.
26. He R, Wang K, Zhao N, et al. Identification of arousals with deep neural networks (DNNs) using dfferent physiological signals//2018 Computing in Cardiology Conference (CinC), Maastricht: IEEE, 2018. DOI: 10.22489/CinC.2018.060.

Journal of Biomedical Engineering

Automated detection of sleep-arousal using multi-scale convolution and self-attention mechanism

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