睡眠障礙的準確診斷長期依賴於耗時且資源有限的實驗室睡眠檢查(PSG)，而目前的居家睡眠檢測(HST)則普遍存在功能不同的技術限制：缺乏腦波(EEG)訊號的心肺監測裝置，因無法準確分期而低估呼吸中止指數(AHI)，可能導致疾病嚴重性被誤判並影響後續治療決策；而另一類消費型腦波裝置則無法用於正式的呼吸障礙診斷。為克服此困境，本研究旨在開發並驗證一套完整的模組化居家睡眠監測系統。
此系統的核心為一可穿戴式腦波感測眼罩，其模組化的設計能依據不同使用需求，彈性整合多種感測器。在阻塞型睡眠呼吸中止症(OSA)的評估中，我們將眼罩與市售的ApneaLink Air裝置整合，以同步擷取腦波與呼吸訊號。此方法克服了傳統居家設備的限制，在32筆紀錄中，透過腦波訊號實現了精確的睡眠分期，與黃金標準比對，準確率達89.7%，Kappa值為0.82，並藉由偵測腦波覺醒事件與使用真實睡眠時間進行計算，有效校正了傳統無腦波量測設備對AHI的低估問題，與PSG的結果比較，亦證實本系統的準確性提升，尤其在重度患者的案例中。而在快速動眼期睡眠行為障礙(RBD)的評估中，我們則為眼罩擴充了下巴肌電圖(EMG)並與肢體慣性感測單元(IMU)模組結合，在10筆紀錄中成功捕捉了91.7%作為RBD核心診斷特徵的REM sleep without atonia (RSWA)事件，展現了其高度的應用彈性。
本論文成功驗證了一個能克服現有居家睡眠檢測設備功能限制的整合性平台，透過結合完整量測比對與臨床專家的判讀經驗，為實現準確、可靠且便利的居家睡眠評估提供了有效的解決方案，未來進入臨床應用，將可減少等待床位提早檢測，提高睡眠診斷的品質。

The accurate diagnosis of sleep disorders has long relied on time-consuming and resource-limited laboratory polysomnography (PSG). Existing home sleep tests (HSTs) generally face a technical limitation of functional division: cardiorespiratory monitoring devices lack electroencephalography (EEG) signals for accurate sleep staging, leading to an underestimation of the Apnea-Hypopnea Index (AHI), which can result in the misclassification of disease severity and impact subsequent treatment decisions. Meanwhile, consumer EEG devices cannot be used for formal respiratory diagnosis. To overcome this challenge, this study aims to develop and validate a complete, modular home sleep monitoring system.
The core of this system is a wearable EEG-sensing eyemask with a modular design that allows for the flexible integration of various sensors to meet different application needs. In the assessment of Obstructive Sleep Apnea (OSA), we integrated the eyemask with a commercial ApneaLink Air device to simultaneously acquire EEG and respiratory signals. This method overcomes the limitations of traditional home devices. In 32 recordings, it achieved accurate sleep staging via EEG signals, with an accuracy of 89.7% and a Kappa value of 0.82 when compared against the gold standard. By detecting EEG arousal events and using total sleep time for calculation, it effectively corrects the AHI underestimation common in traditional non-EEG devices. Furthermore, a comparison with PSG results also confirmed an improvement in the system's accuracy, especially in cases of severe patients. In the assessment of REM Sleep Behavior Disorder (RBD), we expanded the eyemask with a chin electromyography (EMG) module and combined it with limb inertial measurement unit (IMU) modules, successfully capturing 91.7% of REM sleep without atonia (RSWA) events—a core diagnostic feature of RBD—in 10 recordings, demonstrating its high application versatility.
This thesis successfully validates an integrated platform that can overcome the functional limitations of existing home sleep test devices. By combining a comprehensive measurement comparison with the interpretive experience of clinical experts, this system provides an effective solution for achieving accurate, reliable, and convenient home sleep assessment. Its potential for future clinical application could improve the quality of sleep diagnostics by reducing wait times and enabling earlier detection.

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