睡眠在人的一天中約佔據三分之一的時間，然而並非所有人都能擁有良好的睡眠品質，許多人被睡眠問題以及睡眠疾病所困擾。目前臨床睡眠診斷上使用PSG所收錄的腦電訊號、眼動訊號以及肌電訊號作為專家判讀睡眠階段的依據，其中又以腦電訊號最為重要。雖然PSG可記錄多重的生理訊號，但是大量的電極線對使用者也造成睡眠干擾與無法獨立使用等問題。再者，PSG訊號須經由專家判讀，需耗費許多人力以及時間。相較於腦電訊號，眼動訊號(EOG)只需在眼部周圍黏貼電極，並且也能擷取到部分睡眠時的腦電訊號訊號的特徵，透過專門分析技術，就能同時兼顧使用的便利性與睡眠階段判讀，因此我們開發了一套以眼動訊號為基礎的可攜式自動睡眠感測系統。我們收錄了16位健康的成年人的整夜訊號來訓練系統參數與測試系統效能，使用其中八筆進行參數訓練而另外八筆進行測試。我們結合多尺度熵以及自回歸模型開發以眼動訊號為基礎的睡眠階段分析技術。本系統的判讀結果與專家判讀結果的一致性高達84.33%，此效能已達到目前臨床判讀的標準。此外，根據我們先前的研究，最適當的午睡時間長度為睡至淺睡第二期後的10分鐘。然而目前的鬧鐘並無法感測使用者的睡眠階段，而PSG在使用上極不方便也無法自行使用，因此我們利用開發出的自動睡眠判讀系統延伸出根據睡眠階段喚醒使用者的應用。我們收錄了10位健康的成年人午睡訊號來測試即時分析與喚醒系統的效能。睡眠階段的即時分析部份，本系統的準確性為83.08%；而在喚醒時間差異的部份，我們的系統與專家判讀的時間只有0.65分鐘的差異(標準差 0.78分鐘)。證實我們的喚醒系統確實可以在適當的睡眠階段喚醒使用者。未來可將系統與控制睡眠環境的功能做結合，讓使用者擁有更舒適健康的睡眠品質。

Human beings spend approximately one third of their lives sleep including nap and overnight sleep. However, sleep diseases seriously affect patients’ quality of life. For diagnosis, polysomnographic (PSG) recordings including electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) are most commonly taken for sleep stage scoring. Although PSG can record multi-channel signals, the large amount of wires also cause many problems such as sleep interference and it’s not self-applicable. Moreover, PSG signals are visually scored by the well-trained expert which is a time-consuming and subjective process. Compared to PSG, the electrodes of EOG recording is placed around the eyes, and the EOG signals are also coupling some of sleep characteristics of EEG signals. Therefore, we developed an EOG-based automatic stage scoring system to integrate with the eye-mask for sleep monitoring. All-night sleep recordings were obtained from 16 healthy adults, half of the data were used for system training and the others were for testing. The overall agreement between the computer scoring and the manual scoring can reach 84.33%, the result is also in the inter-score agreement. According to our previous studies, human have 10 minutes of S2 nap can achieve better working and memory performance compared to a fixed-length short-term sleep. But the conventional alarm clock cannot sense user’s sleep stage and PSG is not self-applicable and inconvenient. Therefore, we extended our system to be a smart wake-up device. 10 subjects were involved in our experiment to test the performance of our smart wake-up devices. The overall agreement of sleep scoring reaches 83.08%, and the difference of wake-up time between our system and manual scoring is only 0.65 minutes (S.D. 0.78 minutes). The result demonstrates that our system can wake up user in the proper way. In the future, our system can also be applied for sleep environment control to make the user has a healthier and more comfortable sleep quality.

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