2015-05-012024-05-17https://scholars.lib.ntu.edu.tw/handle/123456789/682781摘要：本子計劃目標在於研發可有效率並可靠地監控病友睡眠狀態之生理信號處理系統。為了朝向未來對於患有睡眠障礙之病友居家看護的目標,睡眠監測裝置必須具備以下之特性:(1)高節能效率,以支援即時不間斷地生理信號監控與處理;(2)低複雜度,以達到輕巧可攜並不干擾病友睡眠之目標;(3)高可靠度,以即時評估病友之狀況並回報異常狀況。對此,本子計劃將分析目前廣泛運用於睡眠品質評估之 Polysomnography (PSG)資料,包括腦波圖、眼動圖、下顎肌電圖、心電圖,以進一步開發出適用於睡眠監測裝置之節能生理信號處理演算法。所設計之演算法計劃將可降低監控病友睡眠狀態所需傳輸之生理信號資料量及硬體實現複雜度,並同時達到有效即時監控睡眠狀態之目標。子計劃二及子計劃四並將使用本子計劃所提出之演算法處理過後之生理信號,進一步進行大數據分析並改善病友之睡眠品質。 <br> Abstract: The main purpose of this subproject is to investigate a reliable bio-signal processing system that can efficiently monitor patient’s sleep condition. In order to support future home care of patients with sleep disorder, a sleep condition monitor must exhibit: (1) high energy-efficiency, in order to support continuous real-time bio-signal monitoring and processing; (2) low complexity, in order to be highly portable with minimum sleep disturbance; (3) high reliability, in order to evaluate patient’s sleep condition and report any unusual event immediately. Therefore, this research will analyze Polysomnography (PSG) data used for sleep quality evaluation, including EEG, EOG, EMG and ECG, to develop energy-efficient bio-signal processing algorithms for the implementation of portable sleep monitor. The design goal is to minimize the communication data rate of physiological signals and the corresponding implementation complexity, while maintaining reliable real-time monitoring of patient’s sleep condition. The processed physiological data will be used in subproject 4 to further improve patients’ sleep quality, and massively collected in subproject 2 for big data analysis.生理信號處理睡眠狀態監控節能演算法生醫電路系統硬體最佳化Bio-signal processingsleep condition monitoringenergy-efficient algorithmbioelectronicshardware optimization