臺灣博碩士論文加值系統

本研究觀察了三十名受試者的睡眠覺醒差異，有沒呼吸障礙年 輕人 (18-24歲)；十名六五歲以上，沒有呼吸障礙的長者 ；十名六五歲以上，沒有呼吸障礙的長者 ；十名六五歲以上，有 呼吸障礙的長者，其阻塞型中止指數介於 15-21。
覺醒指數 (AI)定義為每小時發生的覺醒次數。以年紀來看，輕人在總睡眠中 定義為每小時發生的覺醒次數。以年紀來看，輕人在總睡眠中 的覺醒指數 (1.1) 會低於沒有呼吸障礙的長者 會低於沒有呼吸障礙的長者 (3.5)。以是否有呼 吸障礙來看，沒。以是否有呼 吸障礙來看，沒吸障礙長者在總睡眠中的覺醒指數 (3.5) 會低於有呼吸障礙的長者 會低於有呼吸障礙的長者 (5.2)。在快速眼動 睡眠期 (REM)，有呼吸障礙的長者，其覺醒指數 (11.2)高於沒有呼吸障礙的長者 高於沒有呼吸障礙的長者 (1.9)；除此之外，有呼吸障礙的長者其 ；除此之外，有呼吸障礙的長者其 在快速眼動睡眠期 (REM)的覺醒次數 (10.3)也會高於沒有呼吸障礙的長者 (2.1)。
以覺醒事件的類型來看，在總睡眠時期非快速眼動和淺年輕受試者 EEG頻率大於 16 Hz 的覺醒指數 (AI(H))大於 alpha 頻率的覺醒指數 頻率的覺醒指數 (AI(A))，在兩群 年長者中，無論是 否有呼吸障礙，兩者 EEG頻率大於 16 Hz 的覺醒指數皆小於 alpha 頻率的覺醒指數 (AI(H)/AI(A)比值：總睡眠 總睡眠 ：年輕人 ：4；年長者： 0.21；有呼 吸障礙的長者： 0.04。非快速眼動期： 年輕人4；年長者： 0.22；有呼吸障礙的長 者： 0.05。淺睡期： 年輕人4.5；年長者： 0.19；有呼吸障礙的長者： 0.04)。研究 結果可提供未來開發人工智慧睡眠覺醒偵測系統使用。

This paper discusses differences of EEG arousals in 30 subjects. There were ten young adults without sleep-related breathing-disorder (18-24 years), ten elderly without sleep-related breathing-disorder (more than 65 years), and ten elderly with sleep-related breathing-disorder (more than 65 years) whose obstructive apnea indices were from 15-21.
Arousal indices (AI) were defined as number of arousals per hour of sleep. Regarding age, AI in total sleep time in young adults (1.1) were lower than the elderly without breathing-disorder (3.5). In terms of breathing disorder, AI in total sleep time in the elderly (3.5) were lower than the elderly with breathing-disorder (5.2). In REM sleep, AI of the elderly with breathing-disorder (11.2) were higher than the elderly without breathing-disorder (1.9). Moreover, in REM, the number of arousals in the elderly with breathing-disorder (10.3) were higher than the elderly without breathing-disorder (2.1).
In terms of type of arousals, in total sleep time, NREM, N1+N2, in young adults, AI of frequency greater than 16 Hz (AI(H))were more than alpha frequency (AI(A)) in contrast to the elderly with and without breathing-disorder (AI(H)/AI(A): TST: young adults: 4; elderly: 0.21; elderly with breathing-disorder: 0.04. NREM: young adults: 4; elderly: 0.22; elderly with breathing-disorder: 0.05. (N1+N2): young adults: 4.5; elderly: 0.19; elderly with breathing-disorder: 0.04). This finding can be used for developing a machine learning arousal detection system.

摘 要 I
ABSTRACT II
Content V
List of Figures VI
List of Tables VII
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 2
Chapter 2 Materials and Methods 3
2.1 Materials 3
2.2 Method 4
2.3 Statistical analysis 5
Chapter 3 Result 8
3.1 Results regarding age 8
3.1.1 Sleep macrostructure 8
3.1.2 Distribution of arousals throughout stages 10
3.1.3 Arousal indices in the different portions of sleep 11
3.1.4 Arousal types in sleep 13
3.1.5 Mean duration of EEG arousals in sleep 17
3.2 Results regarding breathing-disorder 18
3.2.1 Sleep macrostructure 18
3.2.2 Distribution of arousals throughout stages 20
3.2.3 Arousal indices in the different portions of sleep 21
3.2.4 Arousal types in sleep 23
3.2.5 Mean duration of EEG arousals in sleep 27
Chapter 4 Discussion and conclusion 29
Reference 33

Stepanski, E., et al., Sleep fragmentation and daytime sleepiness. Sleep, 1984. 7(1): p. 18-26.
2. Dewald, J.F., et al., The influence of sleep quality, sleep duration and sleepiness on school performance in children and adolescents: a meta-analytic review. Sleep medicine reviews, 2010. 14(3): p. 179-189.
3. Findley, L., M. Levinson, and R. Bonnie, Driving performance and automobile accidents in patients with sleep apnea. Clinics in chest medicine, 1992. 13(3): p. 427-435.
4. Williamson, A., et al., The link between fatigue and safety. Accident Analysis ＆ Prevention, 2011. 43(2): p. 498-515.
5. Lim, A.S., et al., Sleep fragmentation and the risk of incident Alzheimer's disease and cognitive decline in older persons. Sleep, 2013. 36(7): p. 1027-1032.
6. Rechtschaffen A, K.A., A manual for standardized terminology, techniques and scoring system for sleep stages in human subjects. Brain information service, 1968.
7. Carskadon, M.A., E.D. Brown, and W.C. Dement, Sleep fragmentation in the elderly: relationship to daytime sleep tendency. Neurobiol aging, 1982. 3(4): p. 321-327.
8. Boselli, M., et al., Effect of age on EEG arousals in normal sleep. Sleep, 1998. 21(4): p. 351-357.
9. Goh, D.Y., P. Galster, and C.L. Marcus, Sleep architecture and respiratory disturbances in children with obstructive sleep apnea. American journal of respiratory and critical care medicine, 2000. 162(2): p. 682-686.
10. Bonnet, M., et al., ASDA Report EEG Arousals: Scoring Rules and Examples. Sleep, 1992. 15(2): p. 173-184.
11. Dean, D.A., et al., Scaling up scientific discovery in sleep medicine: the National Sleep Research Resource. Sleep, 2016. 39(5): p. 1151-1164.
12. Zhang, G.-Q., et al., The National Sleep Research Resource: towards a sleep data commons. Journal of the American Medical Informatics Association, 2018.
13. Blank, J.B., et al., Overview of recruitment for the osteoporotic fractures in men study (MrOS). Contemporary clinical trials, 2005. 26(5): p. 557-568.
14. Orwoll, E., et al., Design and baseline characteristics of the osteoporotic fractures in men (MrOS) study—a large observational study of the determinants of fracture in older men. Contemporary clinical trials, 2005. 26(5): p. 569-585.
15. Blackwell, T., et al., Associations between sleep architecture and sleep‐disordered breathing and cognition in older community‐dwelling men: the osteoporotic fractures in men sleep study. Journal of the American Geriatrics Society, 2011. 59(12): p. 2217-2225.
16. Berry, R.B., et al., The AASM manual for the scoring of sleep and associated events. Rules, Terminology and Technical Specifications, Darien, Illinois, American Academy of Sleep Medicine, 2012.
17. Moruzzi, G. and H.W. Magoun, Brain stem reticular formation and activation of the EEG. Electroencephalography and clinical neurophysiology, 1949. 1(1-4): p. 455-473.
18. Johns, M.W., Daytime sleepiness, snoring, and obstructive sleep apnea: the Epworth Sleepiness Scale. Chest, 1993. 103(1): p. 30-36.
19. Sangal, R.B., J.M. Sangal, and C. Belisle, Subjective and objective indices of sleepiness (ESS and MWT) are not equally useful in patients with sleep apnea. Clinical Electroencephalography, 1999. 30(2): p. 73-75.
20. Sangal, R.B., M.M. Mitler, and J.M. Sangal, Subjective sleepiness ratings (Epworth sleepiness scale) do not reflect the same parameter of sleepiness as objective sleepiness (maintenance of wakefulness test) in patients with narcolepsy. Clinical neurophysiology, 1999. 110(12): p. 2131-2135.