研究發現，一些疾病與行為會跟睡眠的品質與長度有關，而一些病症的病理機轉尚未清楚，並且臨床上缺乏有效的治療方式，故需要理想的動物模型以供研究。由於睡眠時的電位訊號與人類睡眠類似，大鼠時常被用來做為模型研究。大鼠與人類的睡眠狀態，都可以被分為兩大類¬：非快速動眼期(NREM)以及快速動眼期(REM)。人類的非快速動眼期由淺至深可在分為淺睡期 1 (s1)、淺睡期 2 (s2)、熟睡期(SWS)。根據1994年Neckelmann等人所提出的規則來判斷，老鼠的非快速動眼期可再分為淺睡期、熟睡期、transition睡眠期。睡眠階段由人工判讀是非常費時費力的。因此，本篇論文的目的為提出一個高準確率並信賴度高的大鼠自動睡眠判讀方法。
本研究根據腦電波 (EEG)以及肌電波 (EMG)的訊號特徵值設計一個自動判讀睡眠周期方法。為了降低特徵值的個體差異，我們會先將特徵值做正規化。在每個十秒的區間，我們根據算出的特徵指標來大致分出清醒、NREM、REM三類。之後再根據每個十秒區間中的五個兩秒區間的特徵指標值和頻帶能量比例來再細分為清醒、淺睡期(NREM 1)、熟睡期 (NREM 2)、transition睡眠期、快速動眼期五類。
本系統使用十六隻大鼠24小時的紀錄並有兩個專家人工判讀來做驗證。在分清醒、淺睡期、熟睡期、transition睡眠期、快速動眼期五類的自動判讀與兩個專家判讀一致的同意度為92.5% (kappa = 0.88)。在分清醒、NREM、REM三類的同自動判讀與兩個專家判讀一致的同意度為95.3% (kappa = 0.91)。這些同意度指出系統的結果準確率以及信賴度是相當高的。

It is found from the research that some disease and behavioural have relation with sleep quality and quantity, but some pathogenesis is not clear and lack of an effective clinical treatment, so requires an ideal animal model for study. Rats are often used as models because they are readily available and display electrical activity during sleep that has similarities with human sleep. Both rat and human, sleep state is divided into two categories: non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep. In human, NREM from light to deep is subdivided as s1, s2 and SWS. In rats, according to Neckelmann et al. proposed rules in 1994, NREM is subdivided into light sleep , deep sleep and transition sleep.Visual scoring is time and energy consuming. Therefore, the aim of thesis is to propose a high accuracy and reliable automatic sleep staging method for rats.
The study is designed to an automated sleep scoring method by using features of the electroencephalogram (EEG) and on electrotromyogram (EMG) activity. For decrease the difference of subjects on the feature, we do normalization. Each 10-sec epoch, we calculate state indices to distinguish waking, NREM and REM state roughly first, and then according to five 2-sec epoch of index value and band power ratio scoring per 10-sec epoch to subdivide into waking, NREM 1, NREM 2 ,transition sleep and REM.
The system is validated with 16 recordings of 24-hour each by comparing with visual scoring for two raters. For scored stage are waking, NREM 1, NREM 2, transition sleep and REM, agreement between scorer consensus and automatic scoring is 92.5% (kappa = 0.88). Global agreement between scorer consensus and automatic scoring is 95.3% (kappa = 0.91) on waking/NREM/REM. This indicated that the performance is high accuracy and reliability.

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