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  • 學位論文

智慧型睡眠期間 PSG 電波自動分階研究

Automatic sleep staging with PSG waves

指導教授 : 郭藍遠

摘要


運動醫學是近年快速發展的一個科學領域,因此在運動傷害防護、增進運動表現、及運動科學方面的研究,蓬勃發展。而睡眠醫學則是另一個快速發展的領域。由於近年來人們的生活壓力倍增,各種環境因素快速惡化,導致許多人因睡眠障礙而失去生活品質。現在藉由對睡眠科學方面的研究發展,讓人們逐漸了解神秘的睡眠醫學知識。傳統上,運動醫學只研究人們醒著時候的運動相關科學議題,對於睡眠時期的跨領域研究文獻非常少,因此借助科技的進步,是否可以發展運動醫學在睡眠領域的應用,例如擴增夢境、睡眠期復健、助眠運動等先進領域的研究,是一件令人期待的事。近年人們已逐漸了解人類睡眠的時期是可以分成許多不同階段的動態過程。因此,睡眠的分階研究被視為是大部分睡眠應用研究的基礎。為了開創運動醫學在睡眠期間的應用研究,本研究提出以人工智慧方法對於睡眠時期的 PSG資料,進行自動分階的技術研發。主要是以類神經網路及模糊數學對PSG 資料的學習與辨認,達成自動分階的任務,以期刊文獻公開的人類PSG 資料庫,驗證本研究提出的數學模式,資料分成 2 部分:30%樣本資料提供模式學習,70%樣本資料用於驗證。目前類神經網路模型判讀準確率可達 72.38%,模糊邏輯法則庫可達 75.27%。

並列摘要


Sports medicine is a scientific field that has developed rapidly in recent years, so researches on sports injury protection, athletic performance, and sports science have flourished. Sleep medicine is another area of rapid development. Due to the increasing pressure on people's lives in recent years, various environmental factors have rapidly deteriorated, causing many people to lose their quality of life due to sleep disorders. Through the research and development of sleep science, people gradually understand the mysterious knowledge in sleep. Traditionally, sports medicine only studies sports-related scientific issues when people are awake. There are very few cross-domain researches on sleep periods. Therefore, with the advancement of technology, it is anticipated to develop cross-domain researches on sleep periods, such as the augmented dreams, sleep rehabilitation, sleep aid exercise, etc. To create a cross-domain application in sleep, this study proposes AI approaches for the automatic sleep staging with PSG in terms of neural network and fuzzy logic. The PSG from literature are employed for model learning (30%) and verification (70%). The accuracy is 72.38% (ANN), and 75.27% (FL).

並列關鍵字

sleep staging PSG neural network fuzzy logic augmented dream

參考文獻


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