本論文之研究目標，在於開發可攜式長時居家睡眠呼吸監測系統，利用新一代的檢測裝置，搜集這些病患於夜間睡眠時呼吸的相關詳細數據，醫生能夠透過整夜的數據分析報告，提供病患更優質的醫療照護。因此，慢性呼吸系統疾病可以得到更精確的評估。
本研究完成雛形平台的建立，相關軟硬體的整合，其中完成無線系統模組包括：前端感測放大電路、類比數位轉換器以及藍牙模組。感測器端包括：三軸加速器感測以及麥克風。利用三軸加速器量測患者於睡眠時的呼吸狀態將是研究的重點，三軸加速器將用以感測呼吸胸腹腔運動造成重力傾角的改變，使用主成分分析找到向量旋轉得到最大的變異量，以提高訊號雜訊比，對團隊合作於後端開發的演算法能有更精確的應用。透過適當的演算法，呼吸訊號相較於呼吸伸縮帶，可獲得更佳的靈敏度，且利用微機電技術與晶片完成整合達到無干擾的配戴，能提供患者於居家自行重複配戴，達到長時間的檢測。藉由雲端網路技術，大量資訊將經由跨領域團隊合作發展演算法，判讀不同型態的睡眠呼吸中止症狀，實現患者嚴重程度的篩選，降低就醫的負擔。期望藉由醫療團隊，硬體製作，與軟體開發，獲致新形態的居家睡眠照護系統，提升睡眠障礙患者的醫療品質，防範急性病症的發生。

The goal of this thesis is to develop a portable system for long-term monitoring the respiratory disorder effects. With the implemented system, the doctors are able to collect the data about the overnight breathing patterns.
The primary study of this research focuses on building a prototype platform, including the wireless data hub and sensory nodes for sleep apnea symptom (SAS) detections. The wireless hub has amplifiers, filters, analog-to-digital converters (ADC), and a Bluetooth module. The sensors contain microphones and accelerometer senor. In this prototype, the accelerometer senor is used for effort detection measurements. The Principle Component Analysis (PCA) can extract rotation matrix from the data of accelerometers to maximize the signal dynamic and improve the linearity. All the data collected from this invasive detection system will be transmitted to the cloud database. Automatic syndrome extraction and classification algorithms are developing to help the efficiently report the respiratory status of nighttime.

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