An overview on sleep research based on functional near infrared spectroscopy_Journal of Biomedical Engineering

Authors：

HUANG Mengying ^1,2 , JIAO Xuejun ² , JIANG Jin ² , YANG Jiehong ^1,2 , CHU Hongzuo ^1,2 , PAN Jinjin ² ,  CAO Yong ²

1. Department of Graduate School, Space Engineering University, Beijing 101416, P.R.China;
2. Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Centre, Beijing 100094, P.R.China;

Corresponding author：

Keywords：

functional near-infrared spectroscopy; sleep; cerebral hemodynamic

DOI：

10.7507/1001-5515.202102003

Video：

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Sleep is a complex physiological process of great significance to physical and mental health, and its research scope involves multiple disciplines. At present, the quantitative analysis of sleep mainly relies on the “gold standard” of polysomnography (PSG). However, PSG has great interference to the human body and cannot reflect the hemodynamic status of the brain. Functional near infrared spectroscopy (fNIRS) is used in sleep research, which can not only meet the demand of low interference to human body, but also reflect the hemodynamics of brain. Therefore, this paper has collected and sorted out the related literatures about fNIRS used in sleep research, concluding sleep staging research, clinical sleep monitoring research, fatigue detection research, etc. This paper provides a theoretical reference for scholars who will use fNIRS for fatigue and sleep related research in the future. Moreover, this article concludes the limitation of existing studies and points out the possible development direction of fNIRS for sleep research, in the hope of providing reference for the study of sleep and cerebral hemodynamics.

Citation： HUANG Mengying, JIAO Xuejun, JIANG Jin, YANG Jiehong, CHU Hongzuo, PAN Jinjin, CAO Yong. An overview on sleep research based on functional near infrared spectroscopy. Journal of Biomedical Engineering, 2021, 38(6): 1211-1218. doi: 10.7507/1001-5515.202102003 Copy

1.	Krueger G P. Sustained work, fatigue, sleep loss and performance: a review of the issues. Work Stress, 1989, 3(2): 129-141.
2.	Dzierzewski J M, Ravyts S G, Dautovich N D, et al. Mental health and sleep disparities in an urban college sample: a longitudinal examination of white and black students. J Clin Psychol, 2020, 76(10): 1972-1983.
3.	Lokhandwala S, Spencer R. Slow wave sleep in naps supports episodic memories in early childhood. Dev Sci, 2021, 24(2): e13035.
4.	Klinzing J G, Niethard N, Born J. Mechanisms of systems memory consolidation during sleep. Nat Neurosci, 2019, 22(10): 1598-1610.
5.	Mcsorley V E, Bin Y S, Lauderdale D S. Sleep characteristics and cognitive function and decline among older adults. Am J Epidemiol, 2019, 188(6): 1066-1075.
6.	Stutz J, Eiholzer R, Spengler C M. Effects of evening exercise on sleep in healthy participants: a systematic review and meta-analysis. Sports Med, 2019, 49(2): 269-287.
7.	Patil S P, Ayappa I A, Caples S M, et al. Treatment of adult obstructive sleep apnea with positive airway pressure: an American academy of sleep medicine clinical practice guideline. J Clin Sleep Med, 2019, 15(2): 335-343.
8.	Irwin M R. Sleep and inflammation: partners in sickness and in health. Nat Rev Immunol, 2019, 19(11): 702-715.
9.	Cellini N, Canale N, Mioni G, et al. Changes in sleep pattern, sense of time and digital media use during COVID-19 lockdown in Italy. J Sleep Res, 2020, 29(4): e13074.
10.	Yildirim O, Baloglu U B, Acharya U R. A deep learning model for automated sleep stages classification using PSG signals. Int J Environ Res Public Health, 2019, 16(4): 599.
11.	Lee X K, Chee N, Ong J L, et al. Validation of a consumer sleep wearable device with actigraphy and polysomnography in adolescents across sleep opportunity manipulations. J Clin Sleep Med, 2019, 15(9): 1337-1346.
12.	Hof Z A, Kellmann M, Kallweit U, et al. Portable PSG for sleep stage monitoring in sports: assessment of SOMNOwatch plus EEG. Eur J Sport Sci, 2020, 20(6): 713-721.
13.	Yi Ruhan, Enayati M, Keller J M, et al. Non-invasive in-home sleep stage classification using a ballistocardiography bed sensor//2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), IEEE, 2019: 1-4.
14.	Azimi H, Liu Haoyang, Bilodeau M, et al. Cloud processing of bed pressure sensor data to detect sleep apnea events//2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA), IEEE, 2020: 1-5.
15.	Stone J D, Rentz L E, Forsey J, et al. Evaluations of commercial sleep technologies for objective monitoring during routine sleeping conditions. Nat Sci Sleep, 2020, 12: 821-842.
16.	Haghayegh S, Khoshnevis S, Smolensky M H, et al. Accuracy of wristband fitbit models in assessing sleep: systematic review and meta-analysis. J Med Internet Res, 2019, 21(11): e16273.
17.	Rundo J V, Downey III R. Polysomnography. Handbook of clinical neurology. Elsevier. 2019: 381-392.
18.	Spiesshoefer J, Linz D, Skobel E, et al. Sleep–the yet underappreciated player in cardiovascular diseases: a clinical review from the German cardiac society working group on sleep disordered breathing. Eur J Prev Cardiol, 2021, 28(2): 189-200.
19.	Lu M, Fang F, Sanderson J E, et al. Validation of a portable monitoring device for the diagnosis of obstructive sleep apnea: electrocardiogram-based cardiopulmonary coupling. Sleep Breath, 2019, 23(4): 1371-1378.
20.	Godino J G, Wing D, de Zambottim, et al. Performance of a commercial multi-sensor wearable (fitbit charge HR) in measuring physical activity and sleep in healthy children. PLoS One, 2020, 15(9): e0237719.
21.	Miller D J, Lastella M, Scanlan A T, et al. A validation study of the WHOOP strap against polysomnography to assess sleep. J Sports Sci, 2020, 38(22): 2631-2636.
22.	Tanveer M A, Khan M J, Qureshi M J, et al. Enhanced drowsiness detection using deep learning: an fNIRS study. IEEE Access, 2019, 7: 137920-137929.
23.	Agbangla N F, Audiffren M, Albinet C T. Use of near-infrared spectroscopy in the investigation of brain activation during cognitive aging: a systematic review of an emerging area of research. Ageing Res Rev, 2017, 38: 52-66.
24.	Rudroff T, Ketelhut N B, Kindred J H. Metabolic imaging in exercise physiology. J Appl Physiol, 2018, 124(2): 497-503.
25.	Chou P H, Lan T H. The role of near-infrared spectroscopy in Alzheimer's disease. Journal of Clinical Gerontology and Geriatrics, 2013, 4(2): 33-36.
26.	Kim H Y, Seo K, Jeon H J, et al. Application of functional near-infrared spectroscopy to the study of brain function in humans and animal models. Mol Cells, 2017, 40(8): 523.
27.	Quaresima V, Ferrari M M. Functional near-infrared spectroscopy (fNIRS) for assessing cerebral cortex function during human behavior in natural/social situations: a concise review. Organ Res Methods, 2019, 22(1): 46-68.
28.	Mayseless N, Hawthorne G, Reiss A L. Real-life creative problem solving in teams: fNIRS based hyperscanning study. Neuroimage, 2019, 203: 116161.
29.	Fernandez R R, Huang X, Ou K L. A machine learning approach for the identification of a biomarker of human pain using fNIRS. Sci Rep, 2019, 9(1): 5645.
30.	冯静达, 焦学军, 李启杰, 等. 基于心率和呼吸特征结合的睡眠分期研究. 航天医学与医学工程, 2020, 33(2): 152-158.
31.	Oniz A, Inanc G, Taslica S, et al. Sleep is a refreshing process: an fNIRS study. Front Hum Neurosci, 2019, 13: 160.
32.	Shiotsuka S, Atsumi Y, Ogata S, et al. Cerebral blood volume in the sleep measured by near-infrared spectroscopy. Psychiatry Clin Neurosci, 1998, 52(2): 172-173.
33.	Ball H L, Tomori C, Mckenna J J. Toward an integrated anthropology of infant sleep. Am Anthropol, 2019, 121(3): 595-612.
34.	潘津津, 焦学军, 姜国华, 等. 从功能性近红外光谱法的光学信号中提取心动和呼吸特征. 光学学报, 2015, 35(9): 212-218.
35.	Zhang Z, Khatami R, Tromberg B J, et al. Brain and muscle oxygenation monitoring using near-infrared spectroscopy (NIRS) during all-night sleep. International Society for Optics and Photonics, 2013, 8578: 85782P.
36.	Nguyen T, Babawale O, Kim T, et al. Exploring brain functional connectivity in rest and sleep states: a fNIRS study. Sci Rep, 2018, 8(1): 16144.
37.	Taga G, Watanabe H, Homae F. Developmental changes in cortical sensory processing during wakefulness and sleep. Neuroimage, 2018, 178: 519-530.
38.	Lee C W, Blanco B, Dempsey L, et al. Sleep state modulates resting-state functional connectivity in neonates. Front Neurosci, 2020, 14: 347.
39.	Brandi G, Stocchetti N, Pagnamenta A, et al. Cerebral metabolism is not affected by moderate hyperventilation in patients with traumatic brain injury. Crit Care, 2019, 23(1): 45.
40.	Fazekas J F, Albert S N, Alman R W. Influence of chlorpromazine and alcohol on cerebral hemodynamics and metabolism. Am J Med Sci, 1955, 230(2): 128-132.
41.	van Beek A H, Claassen J A, Rikkert M G, et al. Cerebral autoregulation: an overview of current concepts and methodology with special focus on the elderly. J Cereb Blood Flow Metab, 2008, 28(6): 1071-1085.
42.	Zhang Zhongxing, Qi Ming, Hügli G, et al. Predictors of changes in cerebral perfusion and oxygenation during obstructive sleep apnea. MedRxiv, 2020: DOI.
43.	Nishida M, Kikuchi S, Matsumoto K, et al. Sleep complaints are associated with reduced left prefrontal activation during a verbal fluency task in patients with major depression: a multi-channel near-infrared spectroscopy study. J Affect Disord, 2017, 207: 102-109.
44.	Young T, Palta M, Dempsey J, et al. The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med, 1993, 328(17): 1230-1235.
45.	Tabone L, Khirani S, Amaddeo A, et al. Cerebral oxygenation in children with sleep-disordered breathing. Paediatr Respir Rev, 2020, 34: 18-23.
46.	Bu L, Zhang M, Li J, et al. Effects of sleep deprivation on phase synchronization as assessed by wavelet phase coherence analysis of prefrontal tissue oxyhemoglobin signals. PLoS One, 2017, 12(1): e0169279.
47.	黄文杏. 疲劳相关术语的概念及沿革的文献研究. 北京: 北京中医药大学, 2005.
48.	闫文彦, 王新. 中青年人群睡眠质量影响因素与干预措施研究. 人人健康, 2020, 519(10): 93-94.
49.	王坤, 陈长英, 艾建赛, 等. 正念减压疗法对乳腺癌患者化疗期间疲乏及睡眠质量的影响. 中华护理杂志, 2017, 52(5): 518-523.
50.	曹珊珊, 于东东, 苏嵘, 等. 帕金森病病人睡眠障碍发生率及相关影响因素分析. 中西医结合心脑血管病杂志, 2019, 17(8): 1257-1259.
51.	Borragán G, Guerrero-Mosquera C, Guillaume C, et al. Decreased prefrontal connectivity parallels cognitive fatigue-related performance decline after sleep deprivation. An optical imaging study. Biol Psychol, 2019, 144: 115-124.
52.	Chuang C H, Cao Z, King J T, et al. Brain electrodynamic and hemodynamic signatures against fatigue during driving. Front Neurosci, 2018, 12: 181.
53.	Lin C T, King J T, Chuang C H, et al. Exploring the brain responses to driving fatigue through simultaneous EEG and fNIRS measurements. Int J Neural Syst, 2020, 30(1): 1950018.

1. Krueger G P. Sustained work, fatigue, sleep loss and performance: a review of the issues. Work Stress, 1989, 3(2): 129-141.
2. Dzierzewski J M, Ravyts S G, Dautovich N D, et al. Mental health and sleep disparities in an urban college sample: a longitudinal examination of white and black students. J Clin Psychol, 2020, 76(10): 1972-1983.
3. Lokhandwala S, Spencer R. Slow wave sleep in naps supports episodic memories in early childhood. Dev Sci, 2021, 24(2): e13035.
4. Klinzing J G, Niethard N, Born J. Mechanisms of systems memory consolidation during sleep. Nat Neurosci, 2019, 22(10): 1598-1610.
5. Mcsorley V E, Bin Y S, Lauderdale D S. Sleep characteristics and cognitive function and decline among older adults. Am J Epidemiol, 2019, 188(6): 1066-1075.
6. Stutz J, Eiholzer R, Spengler C M. Effects of evening exercise on sleep in healthy participants: a systematic review and meta-analysis. Sports Med, 2019, 49(2): 269-287.
7. Patil S P, Ayappa I A, Caples S M, et al. Treatment of adult obstructive sleep apnea with positive airway pressure: an American academy of sleep medicine clinical practice guideline. J Clin Sleep Med, 2019, 15(2): 335-343.
8. Irwin M R. Sleep and inflammation: partners in sickness and in health. Nat Rev Immunol, 2019, 19(11): 702-715.
9. Cellini N, Canale N, Mioni G, et al. Changes in sleep pattern, sense of time and digital media use during COVID-19 lockdown in Italy. J Sleep Res, 2020, 29(4): e13074.
10. Yildirim O, Baloglu U B, Acharya U R. A deep learning model for automated sleep stages classification using PSG signals. Int J Environ Res Public Health, 2019, 16(4): 599.
11. Lee X K, Chee N, Ong J L, et al. Validation of a consumer sleep wearable device with actigraphy and polysomnography in adolescents across sleep opportunity manipulations. J Clin Sleep Med, 2019, 15(9): 1337-1346.
12. Hof Z A, Kellmann M, Kallweit U, et al. Portable PSG for sleep stage monitoring in sports: assessment of SOMNOwatch plus EEG. Eur J Sport Sci, 2020, 20(6): 713-721.
13. Yi Ruhan, Enayati M, Keller J M, et al. Non-invasive in-home sleep stage classification using a ballistocardiography bed sensor//2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), IEEE, 2019: 1-4.
14. Azimi H, Liu Haoyang, Bilodeau M, et al. Cloud processing of bed pressure sensor data to detect sleep apnea events//2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA), IEEE, 2020: 1-5.
15. Stone J D, Rentz L E, Forsey J, et al. Evaluations of commercial sleep technologies for objective monitoring during routine sleeping conditions. Nat Sci Sleep, 2020, 12: 821-842.
16. Haghayegh S, Khoshnevis S, Smolensky M H, et al. Accuracy of wristband fitbit models in assessing sleep: systematic review and meta-analysis. J Med Internet Res, 2019, 21(11): e16273.
17. Rundo J V, Downey III R. Polysomnography. Handbook of clinical neurology. Elsevier. 2019: 381-392.
18. Spiesshoefer J, Linz D, Skobel E, et al. Sleep–the yet underappreciated player in cardiovascular diseases: a clinical review from the German cardiac society working group on sleep disordered breathing. Eur J Prev Cardiol, 2021, 28(2): 189-200.
19. Lu M, Fang F, Sanderson J E, et al. Validation of a portable monitoring device for the diagnosis of obstructive sleep apnea: electrocardiogram-based cardiopulmonary coupling. Sleep Breath, 2019, 23(4): 1371-1378.
20. Godino J G, Wing D, de Zambottim, et al. Performance of a commercial multi-sensor wearable (fitbit charge HR) in measuring physical activity and sleep in healthy children. PLoS One, 2020, 15(9): e0237719.
21. Miller D J, Lastella M, Scanlan A T, et al. A validation study of the WHOOP strap against polysomnography to assess sleep. J Sports Sci, 2020, 38(22): 2631-2636.
22. Tanveer M A, Khan M J, Qureshi M J, et al. Enhanced drowsiness detection using deep learning: an fNIRS study. IEEE Access, 2019, 7: 137920-137929.
23. Agbangla N F, Audiffren M, Albinet C T. Use of near-infrared spectroscopy in the investigation of brain activation during cognitive aging: a systematic review of an emerging area of research. Ageing Res Rev, 2017, 38: 52-66.
24. Rudroff T, Ketelhut N B, Kindred J H. Metabolic imaging in exercise physiology. J Appl Physiol, 2018, 124(2): 497-503.
25. Chou P H, Lan T H. The role of near-infrared spectroscopy in Alzheimer's disease. Journal of Clinical Gerontology and Geriatrics, 2013, 4(2): 33-36.
26. Kim H Y, Seo K, Jeon H J, et al. Application of functional near-infrared spectroscopy to the study of brain function in humans and animal models. Mol Cells, 2017, 40(8): 523.
27. Quaresima V, Ferrari M M. Functional near-infrared spectroscopy (fNIRS) for assessing cerebral cortex function during human behavior in natural/social situations: a concise review. Organ Res Methods, 2019, 22(1): 46-68.
28. Mayseless N, Hawthorne G, Reiss A L. Real-life creative problem solving in teams: fNIRS based hyperscanning study. Neuroimage, 2019, 203: 116161.
29. Fernandez R R, Huang X, Ou K L. A machine learning approach for the identification of a biomarker of human pain using fNIRS. Sci Rep, 2019, 9(1): 5645.
30. 冯静达, 焦学军, 李启杰, 等. 基于心率和呼吸特征结合的睡眠分期研究. 航天医学与医学工程, 2020, 33(2): 152-158.
31. Oniz A, Inanc G, Taslica S, et al. Sleep is a refreshing process: an fNIRS study. Front Hum Neurosci, 2019, 13: 160.
32. Shiotsuka S, Atsumi Y, Ogata S, et al. Cerebral blood volume in the sleep measured by near-infrared spectroscopy. Psychiatry Clin Neurosci, 1998, 52(2): 172-173.
33. Ball H L, Tomori C, Mckenna J J. Toward an integrated anthropology of infant sleep. Am Anthropol, 2019, 121(3): 595-612.
34. 潘津津, 焦学军, 姜国华, 等. 从功能性近红外光谱法的光学信号中提取心动和呼吸特征. 光学学报, 2015, 35(9): 212-218.
35. Zhang Z, Khatami R, Tromberg B J, et al. Brain and muscle oxygenation monitoring using near-infrared spectroscopy (NIRS) during all-night sleep. International Society for Optics and Photonics, 2013, 8578: 85782P.
36. Nguyen T, Babawale O, Kim T, et al. Exploring brain functional connectivity in rest and sleep states: a fNIRS study. Sci Rep, 2018, 8(1): 16144.
37. Taga G, Watanabe H, Homae F. Developmental changes in cortical sensory processing during wakefulness and sleep. Neuroimage, 2018, 178: 519-530.
38. Lee C W, Blanco B, Dempsey L, et al. Sleep state modulates resting-state functional connectivity in neonates. Front Neurosci, 2020, 14: 347.
39. Brandi G, Stocchetti N, Pagnamenta A, et al. Cerebral metabolism is not affected by moderate hyperventilation in patients with traumatic brain injury. Crit Care, 2019, 23(1): 45.
40. Fazekas J F, Albert S N, Alman R W. Influence of chlorpromazine and alcohol on cerebral hemodynamics and metabolism. Am J Med Sci, 1955, 230(2): 128-132.
41. van Beek A H, Claassen J A, Rikkert M G, et al. Cerebral autoregulation: an overview of current concepts and methodology with special focus on the elderly. J Cereb Blood Flow Metab, 2008, 28(6): 1071-1085.
42. Zhang Zhongxing, Qi Ming, Hügli G, et al. Predictors of changes in cerebral perfusion and oxygenation during obstructive sleep apnea. MedRxiv, 2020: DOI.
43. Nishida M, Kikuchi S, Matsumoto K, et al. Sleep complaints are associated with reduced left prefrontal activation during a verbal fluency task in patients with major depression: a multi-channel near-infrared spectroscopy study. J Affect Disord, 2017, 207: 102-109.
44. Young T, Palta M, Dempsey J, et al. The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med, 1993, 328(17): 1230-1235.
45. Tabone L, Khirani S, Amaddeo A, et al. Cerebral oxygenation in children with sleep-disordered breathing. Paediatr Respir Rev, 2020, 34: 18-23.
46. Bu L, Zhang M, Li J, et al. Effects of sleep deprivation on phase synchronization as assessed by wavelet phase coherence analysis of prefrontal tissue oxyhemoglobin signals. PLoS One, 2017, 12(1): e0169279.
47. 黄文杏. 疲劳相关术语的概念及沿革的文献研究. 北京: 北京中医药大学, 2005.
48. 闫文彦, 王新. 中青年人群睡眠质量影响因素与干预措施研究. 人人健康, 2020, 519(10): 93-94.
49. 王坤, 陈长英, 艾建赛, 等. 正念减压疗法对乳腺癌患者化疗期间疲乏及睡眠质量的影响. 中华护理杂志, 2017, 52(5): 518-523.
50. 曹珊珊, 于东东, 苏嵘, 等. 帕金森病病人睡眠障碍发生率及相关影响因素分析. 中西医结合心脑血管病杂志, 2019, 17(8): 1257-1259.
51. Borragán G, Guerrero-Mosquera C, Guillaume C, et al. Decreased prefrontal connectivity parallels cognitive fatigue-related performance decline after sleep deprivation. An optical imaging study. Biol Psychol, 2019, 144: 115-124.
52. Chuang C H, Cao Z, King J T, et al. Brain electrodynamic and hemodynamic signatures against fatigue during driving. Front Neurosci, 2018, 12: 181.
53. Lin C T, King J T, Chuang C H, et al. Exploring the brain responses to driving fatigue through simultaneous EEG and fNIRS measurements. Int J Neural Syst, 2020, 30(1): 1950018.

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An overview on sleep research based on functional near infrared spectroscopy

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