Detecting Excessive Daytime Sleepiness With CNN And Commercial Grade EEG

  • I Putu Agus Eka Darma Udayana STMIK STIKOM Indonesia
  • Made Sudarma Universitas Udayana
  • Ni Wayan Sri Ariyani Universitas Udayana


Epworth sleepiness scale is a self-assessment method in sleep medicine that has been proven to be a good predictor of obstructive sleep apnea. However, the over-reliance of the method making the process not socially distancing friendly enough in response to a global covid-19 pandemic. A study states that the Epworth sleepiness scale is correlated with the brainwave signal that commercial-grade EEG can capture. This study tried to train a classifier powered by CNN and deep learning that could perform as well as the Epworth with the objectiveness of brainwave signal. We test the classifier using the 20 university student using the Epworth sleepiness test beforehand. Then, we put the participant in 10 minutes EEG session, downsampling the data for normalization purposes and trying to predict the outcome of the ESS in respect of their brainwave state. The AI predict the reaching 65% of accuracy and 81% of sensitivity with just under 100.000 dataset which is excellent considering small dataset although this still have plenty room for improvement.


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[1] H. Nakano, M. Kadowaki, T. Furukawa, and M. Yoshida, "Rise In Nocturnal Respiratory Rate During Cpap May Be An Early Sign of COVID-19 in Patients with Obstructive Sleep Apnea," Journal of Clinical Sleep Medicine, vol. 6, no. 10, pp. 1811–1813, 2020, doi: 10.5664/jcsm.8714.
[2] A. J. El Hangouche et al., "Relationship Between Poor Quality Sleep, Excessive Daytime Sleepiness And Low Academic Performance in Medical Students," Advances in Medical Education and Practice, vol. 9, pp. 631–638, 2018, doi: 10.2147/AMEP.S162350.
[3] C. M. Morin, J. Carrier, C. Bastien, and R. Godbout, "Sleep and Circadian Rhythm In Response To The COVID-19 Pandemic," Canadian Journal of Public Health, vol. 111, no. 5, pp. 654–657, 2020, doi: 10.17269/s41997-020-00382-7.
[4] C. V. Senaratna et al., "Detecting Sleep Apnoea Syndrome in Primary Care With Screening Questionnaires and The Epworth Sleepiness Scale," Medical Journal of Australia, vol. 211, no. 2, pp. 65–70, 2019, doi: 10.5694/mja2.50145.
[5] A. Bener et al., "Internet Addiction, Fatigue, and Sleep Problems Among Adolescent Students: a Large-Scale Study," International Journal of Mental Health and Addiction, vol. 17, no. 4, pp. 959–969, 2019, doi: 10.1007/s11469-018-9937-1.
[6] K. Trimmel et al., “Wanted: A Better Cut-Off Value for The Epworth Sleepiness Scale,” Wiener Klinische Wochenschrift, vol. 130, no. 9–10, pp. 349–355, 2018, doi: 10.1007/s00508-017-1308-6.
[7] M. Sand, J. M. Durán, and K. R. Jongsma, "Responsibility Beyond Design: Physicians' Requirements for Ethical Medical AI," Bioethics, no. October 2020, pp. 1–8, 2021, doi: 10.1111/bioe.12887.
[8] I. G. T. Suryawan and I. P. A. E. D. Udayana, "A Deep Learning Approach For COVID 19 Detection Via X-Ray Image With Image Correction Method," International Journal of Engineering and Emerging Technology, vol. 5, no. 2, pp. 1–5, 2020, doi: 10.24843/IJEET.2020.v05.i02.p018.
[9] C. A. Goldstein et al., "Artificial Intelligence in Sleep Medicine: An American Academy of Sleep Medicine Position Statement," Journal of Clinical Sleep Medicine, vol. 16, no. 4, pp. 605–607, 2020, doi: 10.5664/jcsm.8288.
[10] Z. Manoochehri, M. Rezaei, N. Salari, H. Khazaie, B. K. Paveh, and S. Manoochehri, "The Prediction of Obstructive Sleep Apnea Using Data Mining Approaches," Archives of Iranian Medicine, vol. 21, no. 10, pp. 460–465, 2018,
[11] I. N. Yulita, R. Rosadi, S. Purwani, and M. Suryani, "Multi-Layer Perceptron for Sleep Stage Classification," Journal of Physics, vol. 1028, no. 1, pp. 1–8, 2018, doi: 10.1088/1742-6596/1028/1/012212.
[12] Y. Jiao and B. L. Lu, "Detecting Driver Sleepiness from EEG Alpha Wave during Daytime Driving," in IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2017, vol. 1, no. 61272248, pp. 728–731, doi: 10.1109/BIBM.2017.8217744.
[13] H. Wang, Y. Li, X. Hu, Y. Yang, Z. Meng, and K. M. Chang, "Using EEG to Improve Massive Open Online Courses Feedback Interaction," in CEUR Workshop Proceedings, 2013, vol. 1009, pp. 59–66.
[14] Z. Ni, A. C. Yuksel, X. Ni, M. I. Mandel, and L. Xie, "Disentangling Brain Activity from EEG Data Using Bidirectional LSTM Recurrent Neural Networks Zhaoheng," in Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, 2017, pp. 241–246.
[15] G. Panagopoulos, "Multi-Task Learning for Commercial Brain Computer Interfaces," in IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE), 2017, vol. 1, pp. 86–93.
[16] M. Soufineyestani, D. Dowling, and A. Khan, "Electroencephalography (EEG) Technology Applications and Available Devices," Applied Sciences (Switzerland), vol. 10, no. 21, pp. 1–23, 2020, doi: 10.3390/app10217453.
[17] A. M. Strijkstra, D. G. M. Beersma, B. Drayer, N. Halbesma, and S. Daan, "Subjective Sleepiness Correlates Negatively With Global Alpha (8-12 Hz) and Positively With Central Frontal Theta (4-8 Hz) Frequencies In The Human Resting Awake Electroencephalogram," Neuroscience Letters, vol. 340, no. 1, pp. 17–20, 2003, doi: 10.1016/S0304-3940(03)00033-8.
[18] K. B. E. Böcker, J. A. G. van Avermaete, and M. M. C. van den Berg-Lenssen, "The International 10-20 System Revisited: Cartesian and Spherical Co-Ordinates," Brain Topography, vol. 6, no. 3, pp. 231–235, 1994, doi: 10.1007/BF01187714.
[19] C. Saranya and G. Manikandan, "A Study On Normalization Techniques For Privacy Preserving Data Mining," International Journal of Engineering and Technology, vol. 5, no. 3, pp. 2701–2704, 2013.
[20] C. L. Chen, C. Y. Liao, R. C. Chen, Y. W. Tang, and T. F. Shih, "Bus Drivers Fatigue Measurement Based on Monopolar EEG," in Asian Conference on Intelligent Information and Database Systems, 2017, vol. 10192, pp. 308–317, doi: 10.1007/978-3-319-54430-4_30.
[21] N. Sharma, V. Jain, and A. Mishra, "An Analysis of Convolutional Neural Networks for Image Classification," in International Conference on Computational Intelligence and Data Science (ICCIDS), 2018, vol. 132, no. 132, pp. 377–384, doi: 10.1016/j.procs.2018.05.198.
[22] I. P. A. E. D. U. Udayana and P. G. S. C. Nugraha, “Prediksi Citra Makanan Menggunakan Convolutional Neural Network Untuk Menentukan Besaran Kalori Makanan,” Jurnal Teknologi Informasi dan Komputer, vol. 6, no. 1, pp. 30–38, 2020.
[23] L. I. U. Dong, L. I. Yue, L. I. N. Jianping, L. I. Houqiang, and W. U. Feng, "Deep Learning-Based Video Coding: A Review and a Case Study," ACM Computing Surveys, vol. 53, no. 1, pp. 1–35, 2020, doi: 10.1145/3368405.
[24] N. Srivastava, N. Srivastava, A. Krizhevsky, I. Sutskever, and I. Sutskever, "Dropout: A Simple Way to Prevent Neural Networks from Overfitting," Journal of Machine Learning Research, vol. 299, no. 3–4, pp. 345–350, 2014, doi: 10.1016/0370-2693(93)90272-J.
[25] P. Sargento, V. Perea, V. Ladera, P. Lopes, and J. Oliveira, "The Epworth Sleepiness Scale in Portuguese Adults: From Classical Measurement Theory to Rasch Model Analysis," Sleep and Breathing Springer, vol. 19, no. 2, pp. 693–701, 2015, doi: 10.1007/s11325-014-1078-6.
How to Cite
UDAYANA, I Putu Agus Eka Darma; SUDARMA, Made; ARIYANI, Ni Wayan Sri. Detecting Excessive Daytime Sleepiness With CNN And Commercial Grade EEG. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, [S.l.], v. 12, n. 3, p. 186-195, nov. 2021. ISSN 2541-5832. Available at: <>. Date accessed: 27 may 2022. doi:

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