Chin Electromyogram, an Effectual and Useful Biosignal for the Diagnosis of Obstructive Sleep Apnea
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Abstract
Background and Objective: Obstructive sleep apnea (OSA) is among the critical sleep disorders, and researchers have been investigating its novel diagnostic methods. Polysomnography signals' complexity, difficult visual interpretation, and the need for an efficient algorithm based on simpler signals have made the study of sleep apnea a compelling issue. In this study, the accuracy of chin electromyogram in the diagnosis of OSA was evaluated.
Materials and Methods: The amplitude variation and power spectral density (PSD) of chin electromyograms of 100 patients during apnea and before-after apnea occurrences (non-apnea) periods were compared after complete processing of the raw signal. Two-dimensional (2D) spectrograms related to the specified periods were extracted and fed into the residual neural network (ResNet). The network performance was reported by model evaluation parameters.
Results: The results showed that OSA event influences the patient's chin muscle and increases the amplitude variances and power spectrum of the chin electromyogram. The ResNet-50 deep model classified the dataset of this sleep disorder with about 97% accuracy, which was higher than previous studies in this field.
Conclusion: Chin electromyogram can be introduced as a practical and useful biosignal for accurate OSA diagnosis with a deep classifier without the need for current specialized equipment and multiple vital signals.
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21. Mostafa SS, Mendonca F, Ravelo-Garcia AG, et al. A systematic review of detecting sleep apnea using deep learning. Sensors (Basel) 2019; 19.
22. Nikkonen S, Korkalainen H, Kainulainen S, et al. Estimating daytime sleepiness with previous night electroencephalography, electrooculography, and electromyography spectrograms in patients with suspected sleep apnea using a convolutional neural network. Sleep 2020; 43.
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https://paperswithcode.com/method/resnet
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28. Cen L, Yu ZL, Kluge T, et al. Automatic system for obstructive sleep apnea events detection using convolutional neural network. Annu Int Conf IEEE Eng Med Biol Soc 2018; 2018: 3975-8.
29. McCloskey S, Haidar R, Koprinska I, et al. Detecting hypopnea and obstructive apnea events using convolutional neural networks on wavelet spectrograms of nasal airflow. Cham, Switzerland: Springer International Publishing; 2018 p. 361-72.
30. Arslan TS, Akilotu B, Toraman S. A deep learning-based decision support system for diagnosis of OSAS using PTT signals. Med Hypotheses 2019; 127: 15-22.
31. Haidar R, McCloskey S, Koprinska I, et al. Convolutional neural networks on multiple respiratory channels to detect hypopnea and obstructive apnea events. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN); 2018 Jul 8-13; Rio de Janeiro, Brazil. 2018. p. 1-7.
2. Jayaraj R, Mohan J, Kanagasabai A. A review on detection and treatment methods of sleep apnea. J Clin Diagn Res 2017; 11: VE01-VE03.
3. Ayas NT, White DP, Manson JE, et al. A prospective study of sleep duration and coronary heart disease in women. Arch Intern Med 2003; 163: 205-9.
4. Shneerson JM. Handbook of sleep medicine. Hobo-ken, NJ: Wiley; 2010.
5. Mannarino MR, Di Filippo F, Pirro M. Obstructive sleep apnea syndrome. Eur J Intern Med 2012; 23: 586-93.
6. Krakow B, Melendrez D, Ferreira E, et al. Prevalence of insomnia symptoms in patients with sleep-disordered breathing. Chest 2001; 120: 1923-9.
7. Hassan AR, Bhuiyan M. Automatic sleep scoring using statistical features in the EMD domain and ensemble methods. Biocybern Biomed Eng 2015; 36: 248-55.
8. Li Y, Veasey SC. Neurobiology and neuropathophysiology of obstructive sleep apnea. Neuromolecular Med 2012; 14: 168-79.
9. Rodriguez-Sotelo JL, Osorio-Forero A, Jimenez-Rodriguez A, et al. Automatic sleep stages classification using eeg entropy features and unsupervised pat-tern analysis techniques. Entropy 2014; 16: 6573-89.
10. Hassan AR, Subasi A. A decision support system for automated identification of sleep stages from single-channel EEG signals. Knowl Based Syst 2017; 128: 115-24.
11. Acharya UR, Bhat S, Faust O, et al. Nonlinear dynamics measures for automated eeg-based sleep stage detection. Eur Neurol 2015; 74: 268-87.
12. Biniwale AS. Analysis of human polysomnography (PSG) for automatic sleep event detection using Hid-den Markov Model [MSc Thesis]. San Marcos, TX: Texas State University; 2016.
13. Sezgin N. EMG classification in obstructive sleep apnea syndrome and periodic limb movement syndrome patients by using wavelet packet transform and extreme learning machine. Turk J Elec Eng Comp Sci 2015; 23: 873-84.
14. Ferri R, Franceschini C, Zucconi M, et al. Searching for a marker of REM sleep behavior disorder: Submentalis muscle EMG amplitude analysis during sleep in patients with narcolepsy/cataplexy. Sleep 2008; 31: 1409-17.
15. Senny F, Maury G, Cambron L, et al. The sleep/wake state scoring from mandible movement signal. Sleep Breath 2012; 16: 535-42.
16. Al-Angari H. Evaluation of chin EMG activity at sleep onset and termination in obstructive sleep apnea syndrome. Proceedings of the 35th Computers in Cardiology; 2008 Sep 14-17; Bologna, Italy. 35 ed. IEE; 2008.
17. Gouveris H, Bahr K, Schmitt E, et al. Cortico-peripheral neuromuscular disconnection in obstructive sleep apnoea. Brain Commun 2020; 2: fcaa056.
18. Azim MR, Haque SA, Amin MS, et al. Analysis of EEG and EMG signals for detection of sleep disordered breathing events. Proceedings of the 6th International Conference on Electrical and Computer Engineering (ICECE 2010); 2010 Dec 18-20; Dhaka, Bangladesh: IEE; 2010. p. 646-9.
19. Moridani MK, Heydar M, Jabbari Behnam SS. A Reliable algorithm based on combination of EMG, ECG and EEG signals for sleep apnea detection: A reliable algorithm for sleep apnea detection. Proceedings of the 5th Conference on Knowledge Based Engineering and Innovation (KBEI); 2019 Feb 28- Mar 1; Tehran, Iran. 2019 p. 256-62.
20. Shokrollahi M, Krishnan S, Kumar D, et al. Chin EMG analysis for REM sleep behavior disorders. Proceedings of the 2012 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC); 2012 Jan 9-11; Manaus, Brazil. p. 1-4.
21. Mostafa SS, Mendonca F, Ravelo-Garcia AG, et al. A systematic review of detecting sleep apnea using deep learning. Sensors (Basel) 2019; 19.
22. Nikkonen S, Korkalainen H, Kainulainen S, et al. Estimating daytime sleepiness with previous night electroencephalography, electrooculography, and electromyography spectrograms in patients with suspected sleep apnea using a convolutional neural network. Sleep 2020; 43.
23. Patil SP. What every clinician should know about polysomnography. Respir Care 2010; 55: 1179-95.
24. Alaskar H. Deep learning of EMG time-frequency representations for identifying normal and aggressive actions. International Journal of Computer Science and Network Security 2018; 18: 16-25.
25. Deep learning [Online]. [cited 2021 Oct 17]; Avail-able from: URL: http://www.deeplearning.ir
26. Residual Network [Online]. [cited 2021 Oct 20]; Available from: URL:
https://paperswithcode.com/method/resnet
27. Bazi Y, Al Rahhal MM, Alhichri H, et al. Simple yet effective fine-tuning of deep CNNs using an auxiliary classification loss for remote sensing scene classification. Remote Sens 2019; 11: 2908.
28. Cen L, Yu ZL, Kluge T, et al. Automatic system for obstructive sleep apnea events detection using convolutional neural network. Annu Int Conf IEEE Eng Med Biol Soc 2018; 2018: 3975-8.
29. McCloskey S, Haidar R, Koprinska I, et al. Detecting hypopnea and obstructive apnea events using convolutional neural networks on wavelet spectrograms of nasal airflow. Cham, Switzerland: Springer International Publishing; 2018 p. 361-72.
30. Arslan TS, Akilotu B, Toraman S. A deep learning-based decision support system for diagnosis of OSAS using PTT signals. Med Hypotheses 2019; 127: 15-22.
31. Haidar R, McCloskey S, Koprinska I, et al. Convolutional neural networks on multiple respiratory channels to detect hypopnea and obstructive apnea events. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN); 2018 Jul 8-13; Rio de Janeiro, Brazil. 2018. p. 1-7.
| Files | ||
| Issue | Vol 6 No 1-2 (2021): Winter-Spring | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/jss.v6i(1-2).9292 | |
| Keywords | ||
| Obstructive sleep apnea Deep learning Polysomnography Sleep-disordered breathing Neural network models Chin Electromyogram | ||
| Rights and permissions | |
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Moradhasel B, Sheikhani A, Aloosh O, Jafarnia Dabanloo N. Chin Electromyogram, an Effectual and Useful Biosignal for the Diagnosis of Obstructive Sleep Apnea. J Sleep Sci. 2022;6(1-2):32-40.
