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Development of a Hybrid fNIRS-EEG System for a Portable Sleep Pattern Monitoring Device

휴대용 수면 패턴 모니터링을 위한 복합 fNIRS-EEG 시스템 개발

  • Gyoung-Hahn Kim (Technical Research Institute, Hyundai E.D.S.) ;
  • Seong-Woo Woo (Technical Research Institute, Hyundai E.D.S.) ;
  • Sung Hun Ha (Technical Research Institute, Hyundai E.D.S.) ;
  • Jinlong Piao (Technical Research Institute, Hyundai E.D.S.) ;
  • MD Sahin Sarker (Technical Research Institute, Hyundai E.D.S.) ;
  • Baejeong Park (Technical Research Institute, Hyundai E.D.S.) ;
  • Chang-Sei Kim (Department of Mechanical Engineering, Chonnam National University)
  • Received : 2023.10.04
  • Accepted : 2023.11.14
  • Published : 2023.12.31

Abstract

This study presents a new hybrid fNIRS-EEG system to meet the demand for a lightweight and low-cost sleep pattern monitoring device. For multiple-channel configuration, a six-channel electroencephalogram (EEG) and a functional near-infrared spectroscopy (fNIRS) system with eight photodiodes (PD) and four dual-wavelength LEDs are designed. To enhance the convenience of signal measurement, the device is miniaturized into a patch-like form, enabling simultaneous measurement on the forehead. Due to its fully integrated functionality, the developed system is advantageous for performing sleep stage classification with high-temporal and spatial resolution data. This can be realized by utilizing a two-dimensional (2D) brain activation map based on the concentration changes in oxyhemoglobin and deoxyhemoglobin during sleep stage transitions. For the system verification, the phantom model with known optical properties was tested at first, and then the sleep experiment for a human subject was conducted. The experimental results show that the developed system qualifies as a portable hybrid fNIRS-EEG sleep pattern monitoring device.

Keywords

Acknowledgement

본 과제(결과물)는 2023년도 교육부의 재원으로 한국연구재단의 지원을 받아 수행된 지자체-대학 협력기반 지역혁신 사업의 결과입니다(과제관리번호: 2021RIS-002).

References

  1. The AASM Manual for the Scoring of Sleep and Associated Events, https://aasm.org/clinical-resources/scoring-manual. 
  2. Berry RB, Brooks R, Gamaldo C, Harding SM, Lloyd RM, Quan SF, Troester MT, Vaughn BV. AASM Scoring Manual Updates for 2017 (Version 2.4), Journal of clinical sleep medicine: JCSM: official publication of the American Academy of Sleep Medicine, 2017;13(5):665-666.  https://doi.org/10.5664/jcsm.6576
  3. Aboalayon KAI, Faezipour M, Almuhammadi WS, Moslehpour S. Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation, Entropy 2016, 2016;18:272. 
  4. Phan H, Andreotti F, Cooray N, Chen OY, De Vos M. SeqSleepNet: end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019;27(3):400-410.  https://doi.org/10.1109/TNSRE.2019.2896659
  5. Tsinalis O, Matthews PM, Guo Y. Automatic sleep stage scoring using time-frequency analysis and stacked sparse autoencoders. Annals of biomedical engineering, 2016;44:1587-1597.  https://doi.org/10.1007/s10439-015-1444-y
  6. Cui Z, Zheng X, Shao X, Cui L. Automatic sleep stage classification based on convolutional neural network and fine-grained segments, Complexity, 2018; 2018, Article ID 9248410:1-13.  https://doi.org/10.1155/2018/9248410
  7. Tzimourta KD, Tsilimbaris A, Tzioukalia K, Tzallas AT, Tsipouras MG, Astrakas LG, Giannakeas N. EEG-Based Automatic Sleep Stage Classification. Biomedical Journal of Scientific & Technical Research, 2018;7(4):1-16. 
  8. Li C, Qi Y, Ding X, Zhao J, Sang T, Lee M. A Deep Learning Method Approach for Sleep Stage Classification with EEG Spectrogram, International Journal of Environmental Research and Public Health, 2022;19(10):6322,1-17.  https://doi.org/10.3390/ijerph19106322
  9. Mousavi S, Afghah F, Acharya UR. Sleep EEG Net: Automated sleep stage scoring with sequence to sequence deep learning approach. PLoS one, 2019;14(5):e0216456, 1-15.  https://doi.org/10.1371/journal.pone.0216456
  10. Supratak A, Dong H, Wu C, Guo Y. DeepSleepNet: A model for automatic sleep stage scoring based on raw single-channel EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017;25(11):1998-2008.  https://doi.org/10.1109/TNSRE.2017.2721116
  11. Wu W. Sleep Quality Detection Based on EEG Signals Using Transfer Support Vector Machine Algorithm, Journal of Frontiers in Neuroscience, 2021;15:670745, 1-9.  https://doi.org/10.3389/fnins.2021.670745
  12. Park CH, Woo SW, Kim N, Jang H, Kim HH, Kim YJ, Lee YM, Hong KS, Kim CS. Simultaneous Discrimination of Multiple Chromophores with Frequency Division Multiplexed Four-Color Functional Near-Infrared Spectroscopy, IEEE Transactions on Instrumentation and Measurement, 2023;72:4504313, 1-13.  https://doi.org/10.1109/TIM.2023.3279877
  13. Scholkmann F, Kleiser S, Metz AJ, Zimmermann R, Mata Pavia J, Wolf U, Wolf M. A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology, NeuroImage, 2014;85(Pt 1):6-27.  https://doi.org/10.1016/j.neuroimage.2013.05.004
  14. Bonilauri A, Intra SF, Baselli G, Baglio F. Assessment of fNIRS Signal Processing Pipelines: Towards Clinical Applications, Applied Sciences, 2022;12(1):316, 1-25.  https://doi.org/10.3390/app12010316
  15. Li R, Yang D, Fang F, Hong KS, Reiss AL, Zhang Y. Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review, Sensors (Basel, Switzerland), 2022;22(15):5865, 1-22.  https://doi.org/10.3390/s22155865
  16. Roy B, Sahib AK, Kang D, Aysola RS, Kumar R. Brain tissue integrity mapping in adults with obstructive sleep apnea using T1-weighted and T2-weighted images. Therapeutic Advances in Neurological Disorders, 2022;15(1):17562864221137505. 
  17. Shoaib Z, Akbar A, Kim ES, Kamran MA, Kim JH, Jeong MY. Utilizing EEG and fNIRS for the detection of sleep-deprivation-induced fatigue and its inhibition using colored light stimulation. Scientific Reports, 2023;13(1):6465, 1-18.  https://doi.org/10.1038/s41598-023-33426-2
  18. Nguyen T, Babawale O, Kim T, Jo HJ, Liu H, Kim JG. Exploring brain functional connectivity in rest and sleep states: a fNIRS study, Scientific reports, 2018;8(1):16144, 1-10.  https://doi.org/10.1038/s41598-018-33439-2
  19. Arif S, Khan MJ, Naseer N, Hong KS, Sajid H, Ayaz Y. Vector Phase Analysis Approach for Sleep Stage Classification: A Functional Near-Infrared Spectroscopy-Based Passive Brain-Computer Interface, Front. Hum. Neurosci., 2021;15:658444, 1-15.  https://doi.org/10.3389/fnhum.2021.658444
  20. Kim SK, Yoo SK. Multimodal Bio-signal Measurement System for Sleep Analysis, Journal of Korea Multimedia Society, 2018;21(5):609-616.  https://doi.org/10.9717/KMMS.2018.21.5.609
  21. Rojas GM, Alvarez C, Montoya CE, de la Iglesia-Vaya M, Cisternas JE, Galvez M. Study of Resting-State Functional Connectivity Networks Using EEG Electrodes Position As Seed, Frontiers in neuroscience, 2018;12:235:1-12.  https://doi.org/10.3389/fnins.2018.00235
  22. Onton JA, Kang DY, Coleman TP. Visualization of Whole-Night Sleep EEG From 2-Channel Mobile Recording Device Reveals Distinct Deep Sleep Stages with Differential Electrodermal Activity. Front. Hum. Neurosci., 2016;10:605, 1-12.  https://doi.org/10.3389/fnhum.2016.00605
  23. Al-salman W, Li Y, Wen P, Diykh M. An efficient approach for EEG sleep spindles detection based on fractal dimension coupled with time frequency image, Biomed. Signal Process. Control., 2018;41:210-221.  https://doi.org/10.1016/j.bspc.2017.11.019
  24. Duman F, Erdamar A, Erogul O, Telatar Z, Yetkin S. Efficient sleep spindle detection algorithm with decision tree, Expert Syst. Appl., 2009;36(6):9980-9985.  https://doi.org/10.1016/j.eswa.2009.01.061
  25. Nasi T, Virtanen J, Noponen T, Toppila J, Salmi T, Ilmoniemi RJ. Spontaneous hemodynamic oscillations during human sleep and sleep stage transitions characterized with near-infrared spectroscopy. PLoS One, 2011;6(10):e25415, 1-9. https://doi.org/10.1371/journal.pone.0025415