نوع مقاله: مقاله کامل پژوهشی

نویسنده

استادیار، دانشکده‌ی مهندسی برق، پزشکی و مکاترونیک، واحد قزوین، دانشگاه آزاد اسلامی، قزوین

10.22041/ijbme.2018.92597.1399

چکیده

خواب، یکی از فرایندهای ضروری برای حفظ و بهبود فعالیت‌های انسان می‌باشد، در حالی که  هنوز بسیاری از جزئیات آن به درستی شناخته نشده است. خواب کاهش یافته یا بریده شده، یکی از ریسک‌های سلامتی به شمار می‌آید که می‌تواند باعث وقوع بیماری‌های قلبی یا دیابت و هم‌چنین انحطاط هوشیاری و شناخت گردد. چند پارگی خواب، به پدیده‌ای گفته می‌شود که در آن محرومیت از خواب به طور کامل اتفاق نمی‌افتد، اما خواب فرد به طور متناوب با انگیختگی‌هایی که منشا آن‌ها عوامل خارجی (نویز) یا عوامل داخلی (آپنه) است، آشفته می‌شود. مدل‌سازی ریاضی یک ابزار مناسب برای شناخت مکانیسم‌های پیچیده‌ی بیولوژیکی است. در این مقاله، مدلی از پدیده‌های اساسی تنظیم چرخه‌ی خواب–بیداری، پیاده‌سازی شده، دینامیک آن مورد بررسی قرار گرفته و سپس عواملی که سبب بروز آپنه‌ی انسدادی خواب می‌شوند، شناسایی و ارزیابی می‌گردند. این مدل شامل دو جمعیت  نورونی اصلی می‌باشد: 1- سیستم تحریک صعودی مغز در ساقه‌ی مغزی، که مسئول ایجاد بیداری است و 2- جمعیت نورونی VLPO از هیپوتالاموس، که خواب را  شکل می‌دهد. این دو منطقه، در مهار متقابل قرار داده شده و انتقال بین دو حالت رفتاری خواب و بیداری را به صورت سوئیچی پدید می‌آورند. نتایج مدل‌سازی نشان می‌دهد که هیسترسیس موجود در انتقال بین خواب و بیداری، تحت تاثیر عواملی که باعث انگیختگی می‌شوند، کوچک و ناپایدار شده و به تبع آن، انتقال‌های سریع پدید می‌آید. مدل ارائه شده در این تحقیق توانسته است برخی از نتایج آزمایشگاهی در خصوص خواب آپنه‌ی انسدادی را بازتولید نماید.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Modeling Obstructive Sleep Apnea using Dynamical Phase Transition

نویسنده [English]

  • Somayeh Raiesdana

Assistant Professor, Faculty of Electrical Department, Biomedical and Mechatronic Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

چکیده [English]

Sleep is an essential process to maintain and improve human activities, while many details related to sleep are still not well understood. Decreased or fragmented sleep is a health risk that might result in heart disease or diabetes on one hand and degradation of consciousness and cognition on the other hand. Sleep fragmentation is a phenomenon in which an individual's sleep is intermittently disrupted by arousal caused by external factors (noise) or internal factors (apnea) although sleep deprivation does not completely occur. Computational modeling is a suitable framework for understanding complex biological mechanisms. In this paper, the fundamental phenomena underlying the sleep-wake transition was reviewed and simulated. The dynamical behavior of model was then investigated and afterwards the factors that might cause obstructive sleep apnea were implemented and evaluated. The model includes two main neuronal populations: the ascending arousal system in the brain stem that is responsible for awakening and a neuronal population in the hypothalamus, called VLPO, which mediates sleep. These populations have mutual inhibition on each other causing a flip-flop or switching behavior between sleep and wake. The results of modeling in this paper showed hysteresis in the sleep-wake cycle, the size of which is affected by factors causing arousal. In OSA, intermittent and unstable transitions as well as the shrinking of bistable zone is expected. The model could reproduce some experimental results related to obstructive apneas.

کلیدواژه‌ها [English]

  • sleep modeling
  • dynamical phase transition
  • obstructive sleep apnea
  • arousal
  • hysteresis

[1]     S. Raiesdana, “Quantifying the dynamic of OSA brain using multifractal formalism: A novel measure for sleep fragmentation,” Technology and Health Care, vol. 25, pp. 265–284, 2017.

[2]     S. Raiesdana, “Automated sleep staging of OSAs based on ICA preprocessing and consolidation of temporal correlations,” Australasian Physical & Engineering Sciences in Medicine, vol. 41, pp. 161–176, March 2018.

[3]     D. W. Beebe, D. Gozal “Obstructive sleep apnea and prefrontal cortex: towards a comprehensive model linking nocturnal upper airway obstruction to daytime cognitive and behavioral deficits,” J Sleep Research, vol. 11, pp. 1-16, 2002.

[4]     M. Sedigh-Sarvestani, S. J. Schiff, B. J. Gluckman, “Reconstructing Mammalian Sleep Dynamics with Data Assimilation,” PLOS Computational Biology, vol. 8, issue 11, pp. 1-16, 2012

[5]     E. Bouazizi, R. Naeck, D. D’amore, et al., “Mathematical modelling of sleep fragmentation diagnosis,” Biomedical Signal Processing and Control, vol. 24, pp. 83–92, 2016.

[6]     A. J. K. Phillips, P. A. Robinson, A. Abouei Mehrizi, “A Quantitative Model of Sleep-Wake Dynamics Based on the Physiology of the Brainstem Ascending Arousal System,” J Biol Rhythm, vol. 22, pp. 167-179, 2007.

[7]     P. A. Robinson, A. J. K. Phillips, B.D. Fulcher, M. Puckeridge and J. A. Roberts, A. Abouei Mehrizi, “Quantitative modelling of sleep dynamics,” Phil. Trans. R. Soc. A, vol. 369, pp. 3840-3854, 2018.

[8]     G. Morruzzi, H. Magoun, “Brain stem reticular formation and activation of EEG,” Electroencephalogr Clin Neurophysiol, vol. 1, pp. 455-473, 1949.

[9]     N. Kleitman, “Basic rest-activity cycle—22 years later,” Sleep, vol. 5, pp. 311-317, 1982.

[10] D. Shannahoff-Khalsa, FE. Yates, “Ultradian sleep rhythms of lateral EEG, autonomic and cardiovascular activity are coupled in humans,” Int J Neurosci, vol. 101, pp. 21-43, 2000.

[11] J. Lu, D. Sherman, M. Devor, and C. Saper, “A putative flip-flop switch for control of REM sleep,” Nature, vol. 441, pp. 589-594, 2006.

[12] A. Borbely, P. Achermann, “Sleep homeostasis and models of sleep regulation,” Journal of Biological Rhythms, vol. 14, pp. 559–570, 1999.

[13] P. McCauley, L. Kalachev, A. Smith, et al., “A new mathematical model for the homeostatic effects of sleep loss on neurobehavioral performance,” Journal of Theoretical Biology, vol. 256, pp. 227–239, 2009.

[14] R. W. McCaley, S. G. Massaquoi, A limit cycle mathematical model of the REM sleep oscillator system, Am J Physiol., vol. 251, pp. 1011-29, Dec 1986.

[15] B. Fulcher, A. Phillips, P. Robinson, “Modeling the impact of impulsive stimuli on sleep-wake dynamics,” Phys Rev E, vol. 78, 051920, 2008.

[16] A. Phillips, P. Robinson, D. Kedziora, R. Abeysuriya, “Mammalian sleep dynamics: how diverse features arise from a common physiological framework,” PLoS Comp Biol, vol. 6, e1000826, 2010.

[17] B. Fulcher, A. Phillips, P. Robinson, “Quantitative physiologically based modeling of subjective fatigue during sleep deprivation,” J Theor Biol, vol. 264, pp. 407–419, 2010.

[18] C.G. Diniz Behn, V. Booth, “Simulating microinjection experiments in a novel model of the rat sleep-wake regulatory network,” Journal of Neurophysiology, Vol. 103, pp.1937–1953, 2010.

[19] V. Booth, C.G. Diniz Behn, “A fast-slow analysis of the dynamics of REM sleep,” SIAM Journal of Applied Dynamical Systems,  vol., 11, pp. 212–242, 2012.

[20] V. Booth, C.G. Diniz Behn, “Phsiologically-based modeling of sleep wake regulatory networks,” Mathematical Biosciences, vol. 250, pp. 54–68, 2014.

[21] S.Kaur, N. P. Pedersen, S. Yokota, et al.,  “Glutamatergic Signaling fromthe Parabrachial Nucleus Plays a Critical Role in Hypercapnic Arousal,” The Journal of Neuroscience, vol. 33, no. 18, pp. 7627–7640, 2013.

[22] N. L. Chamberlin, “Brain circuitry mediating arousal from obstructive sleep apnea,” Curr Opin Neurobiol, vol. 23, no. 5, pp. 774–779, October 2013.

[23] A. C. Skeldon, D. J. Dijk, G. Derks, "Mathematical Models for Sleep-Wake Dynamics: Comparison of the Two-Process Model and a Mutual Inhibition Neuronal Model", Plos one. Vol. 9, Issue 8, pp.1-16 e103877, 2014.

[24] R. O. Pasnau, P. Naitoh, S. Stier, E. J. Kollar, “The psychological effects of 205 hours of sleep deprivation,”. Arch. Gen. Psychiat, vol. 18, pp. 496–505, 1968.

[25] M. Puckeridge, B. D. Fulcher, A. J. K. Phillips, P. A. Robinson, “Incorporation of caffeine into a quantitative model of fatigue and sleep,” J. Theor. Biol., vol. 273, pp. 44–54, 2011.