睡眠是影響生活品質不可或缺的一環，人類一天會花上三分之一的時間在睡眠上，而現今多數研究更認為睡眠更是在能量的儲存以及學習記憶中扮演了極為重要的角色，但並不是所有人都能擁有良好的睡眠品質。因此在臨床上會使用多通道睡眠生理紀錄儀 (Polysomnography, PSG) 進行檢查，由專家對於收錄的生理訊號進行人工判讀並由專家或醫生進行診斷。由於人工判讀是一件十分主觀且耗時的工作，故有許多睡眠自動判讀演算法被提出。雖然這些演算法在準確率上皆有不錯的表現，但卻都僅在單一族群上。而真正需要被診斷的是有睡眠疾病的而非正常人。
本研究提出一套基於睡眠生理訊號的特徵分析。一般作自動睡眠判讀的方法只用到最具代表性的特徵作判斷且只考慮到一種情況，例如:判斷睡眠中的清醒期的時候只有用到阿法波，而且，當阿法波出現超過一定比例後就判定為清醒期。在這篇論文中，除了最具代表性的特徵外，還會參照AASM規則，使用其他有被提到的輔助性的特徵，藉由加入這些考量後，可以使判讀的準確率得到明顯的提升，並且包含正常人與失眠患者倆的族群，且經由對訊號和頻譜的分析，提出了一個高準確率(85.5%, 84.8%)且可靠的自動睡眠判讀方法。 並且提出一個能夠針對不同的群體來自動選擇適合的自動睡眠判讀決策樹且選擇適合的結果。系統與專家整體的準確率可達85.2%，在分類的結果也可達到高準確率的正確分類。將來，可以將此方法運用在有睡眠障礙的病人的診斷和治療上，例如:失眠病人的診斷和治療。也可以運用在記憶學習等學術研究上。

Sleep is very important for human beings. People spend one third of time on sleep one day. Many studies today indicate that sleep play a significance role on energy storage and learning. However, there’s not everyone who can acquire good sleep quality. Therefore, the clinical use Polysomnography (PSG) to recording sleep physiological signals. The collection of physiological signals will be manual scoring by expert for diagnosis. Since manual scoring is a very subjective and time-consuming work, so there are many sleep automatic scoring methods have been proposed. Although the agreement of these methods on a good performance, the method usually applied on the single group, the subject that really need to be diagnosed is who has the sleep disorder, not the normal.
. In this study, we propose a sleep scoring system which based on the analysis of the signal and the spectrum. Generally, the methods of automatic sleep staging only use the most typical feature for staging and only consider one case. For example, the decision whether the sleep is staged as wake or not is only by using the alpha wave. And when the alpha wave appears lasting for more than a proportion, the sleep is classified as the wake stage. In this paper, besides the most typical feature, we use other additional features referencing the rules by AASM. By adding the more consideration, the agreement of the staging is improved apparently. We propose a high accuracy (85.5%, 84.8%) and reliable automatic sleep staging method. And we propose the automatic selecting method that could choose the proper decision tree to the different group to do the sleep scoring and choose the proper result. The agreement between the expert and the system is 85.2%, and also we could classify the subjects correctly. In the future, the method can be used in the diagnosis and cure of the patients with sleep obsession, ex. the diagnosis and cure of the patients with sleep insomnia. This method can be used in the memory learning or other academic researches.

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