無線多重生理狀態監控系統-應用於居家睡眠監控

Abstract

目前要確立睡眠呼吸中止症的檢查方法要靠「睡眠多項生理監測儀(polysomnography-PSG)」，但睡眠呼吸中止症的檢查，必須在睡眠檢查中心進行整夜檢查。然而，數量有限的睡眠中心和床位往往導致診斷和治療的漫長等待;再加上睡眠在陌生的環境，及次數不多的測量，可能影響受測者的睡眠情況，造成無法完整診斷病症。所以，在家檢查睡眠情況會提供一個比在實驗室中設置更切合實際及效率的評估方式。 本論文提出一個以電池供應電源、微小化及便攜式的無線多重生理參數介面，並以JAVA開發監控及紀錄的軟體，將受測者睡眠時的生理參數存入接收的電腦中，專家可依儲存的資料，判讀及診斷使用者的睡眠情況。於真實的環境中，居家睡眠監控系統必須是個便利的大小、堅固耐用、重量輕且低功率消耗來達到可穿戴便利與持久性的需求。為了證明我們提出系統的可行性,我們於台北榮民總醫院的睡眠中心在健康的受測者上同時錄製標準的PSG系統- Alice 5□ Diagnostic Sleep System與本論文提出的系統。而兩套系統在時域及睡眠階段上的比較分析,均達到很好的相似程度，故我們提出的系統可以在居家睡眠檢查的長期追蹤研究上實際運用。

The polysomnography (PSG) is considered as the gold standard for the diagnosis of Obstructive Sleep Apnea (OSA). The examination of OSA requires an overnight sleep experiment in a laboratory. However, due to the limitation for number of labs and beds, patients often have to wait for a long time before being diagnosed and eventually treated. In addition, the unfamiliar environment and restricted action when operating PSG may disturb the sleep of patients, and then cause an incomplete or corrupted test. Therefore, a PSG conducted at the patient’s home would be more reliable and more convenient. In this study, we implemented wireless polysomnography system for sleep monitoring at home, which involves with a battery-powered, miniature, wireless, portable, and multipurpose recorder. A java-based PSG recording program in the personal computer was designed to save several bio-signals and transfer them into EDF format. And then, these PSG records can be used to determine patient’s sleep stages and diagnose OSA by specialists. This system is portable, lightweight, and low power-consumption. To demonstrate the feasibility of our proposed PSG system, the comparison between the standard PSG-Alice 5□ Diagnostic Sleep System and our system was examined. Here, several healthy volunteer patients simultaneously performed PSG experiment with standard PSG-Alice 5□ Diagnostic Sleep System and our system under the supervision of specialists at the Sleep Laboratory of Taipei Veteran General Hospital. The results for comparison of time-domain waveform and sleep stage between the above two systems showed that our system was reliable and can be applied in practice. Our system can serve for a long-term tracing and research of personal sleep monitoring at home.