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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 闕志達(Tzi-Dar Chiueh) | |
dc.contributor.author | Yu-An Chiao | en |
dc.contributor.author | 喬鈺安 | zh_TW |
dc.date.accessioned | 2021-06-08T02:04:55Z | - |
dc.date.copyright | 2016-03-08 | |
dc.date.issued | 2016 | |
dc.date.submitted | 2016-02-13 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/19550 | - |
dc.description.abstract | 對睡眠呼吸中止症的患者而言,連續陽壓呼吸器為目前最建議的治療方法。但是由於到睡眠中心進行陽壓呼吸器調整皆須花費許多時間、金錢,以及人力資源。因此若能讓呼吸器自身偵測患者當下的呼吸狀態,自行調整壓力,這樣便能達到節省各項成本,亦能增加患者使用呼吸器來治療的舒適度。
本論文提出一個陽壓呼吸器治療氣壓自動控制演算法,並使用睡眠中心所提供的陽壓呼吸器病患的臨床資料進行驗證。此項全新的演算法僅利用藉由呼吸器氣流訊號,藉由信號處理分析氣流訊號進行呼吸中止與淺呼吸事件偵測;並從氣流訊號中產生時域、頻域、多尺度熵相關的特徵值,進行特徵值篩選後使用k-means分群演算法去除極端值,將特徵值以深度學習類神經網路產生分類器,使用交叉驗證的方式驗證,以實現打呼事件偵測;最後也利用一簡單的方式分析氣流訊號來判斷中樞型呼吸中止。藉由以上這些偵測出來的呼吸事件來決定應當打出多大的治療氣壓。 | zh_TW |
dc.description.abstract | For patients suffering from sleep apnea, continuous positive airway pressure (CPAP) is the most recommended therapy currently. However, manual CPAP titration at sleep center is time consumption and high-cost. Therefore, PAP machine (APAP) which can detect the breathing event and further automatically adjust the therapeutic pressure has been demonstrated to lower therapeutic pressure than fixed-pressure CPAP. Also, patients prefer APAP than fixed-pressure CPAP though the compliance was similar between two devices.
In this thesis, an automated CPAP titration algorithm is proposed. We verified our algorithm with database of overnight CPAP titration in the sleep center of NTUH. This novel algorithm only used PAP flow signal. Apnea and hypopnea detection can be realized by signal processing of several signals. Besides, we extracted several features from PAP flow signal, do feature selection, put them into deep-learning neural network to generate a classifier for snore detection, and use cross-validation method to do verification. A simple recheck method was also introduced to do CSA detection. Finally, the therapeutic pressure of CPAP was determined with algorithm according to the aforementioned event detections. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T02:04:55Z (GMT). No. of bitstreams: 1 ntu-105-R02943017-1.pdf: 3879013 bytes, checksum: 399de457c190a1ca1bd25484aa6da23e (MD5) Previous issue date: 2016 | en |
dc.description.tableofcontents | 誌謝 i
摘要 iii Abstract v 目錄 vii 圖目錄 xi 表目錄 xv 第一章 緒論 1 1.1 研究動機 1 1.2 呼吸中止症介紹 3 1.3 論文組織 4 第二章 自動型陽壓呼吸器系統 5 2.1 現有之呼吸器環境介紹 5 2.2 系統架構 8 2.3 臨床資料收集 9 2.4 呼吸器氣流訊號介紹 10 2.5 信號前處理 11 2.5.1呼吸器漏氣問題 11 2.5.2經驗模態分解法 13 2.5.3集成經驗模態分解法 17 2.6 呼吸事件偵測 19 2.7 治療氣壓決策 20 第三章 呼吸中止與淺呼吸偵測 21 3.1呼吸中止與淺呼吸介紹 21 3.2呼吸中止自動判讀之系統架構 23 3.2.1 雜訊去除 23 3.2.2 計算區域參考值 24 3.2.3偵測可能事件 25 3.2.4再檢查機制 26 3.3 更改臨界值 27 3.3.1 受試者工作特徵曲線介紹 28 3.3.2呼吸中止之受試者工作特徵曲線 30 3.3.3淺呼吸之受試者工作特徵曲線 33 3.3.3.1使用參考值為五分鐘的結果 33 3.3.3.2使用參考值為兩分鐘的結果與比較 35 3.3.3.3加入潮氣量做為參數的結果與比較 37 3.4 本論文結果與過往研究比較 39 第四章 打呼事件偵測與呼吸中止判斷 43 4.1本論文所用之打呼事件偵測方法 43 4.2 特徵值擷取(Feature Extraction) 44 4.3多尺度熵分析 47 4.3.1多尺度熵介紹 47 4.3.2多尺度熵的降維 49 4.4特徵值篩選(Feature Selection) 50 4.4.1 Weka介紹 50 4.4.2特徵篩選方法 51 4.5 K-means分群演算法 53 4.5.1 K-means分群演算法介紹 53 4.5.2 K-means分群演算法流程 54 4.6類神經網路 55 4.6.1倒傳遞類神經網路 56 4.6.2深層學習類神經網路 59 4.6.2.1架構與演算法 60 4.6.2.2延伸型限制性波茲曼機演算法 60 4.7 K次交叉驗證(K-Fold Cross-Validation) 62 4.8臨床實驗結果 63 4.8.1 打呼事件偵測之實驗結果 63 4.8.2本論文結果與過往研究比較 64 4.8.3 以打呼事件減少淺呼吸之過估 64 4.9中樞型呼吸中止判斷 67 4.9.1呼吸器治療之中樞型呼吸中止成因 68 4.9.2中樞型呼吸中止判斷演算法與臨床實驗結果 69 第五章 治療氣壓自動控制系統 71 5.1 呼吸器調整檢查作業程序介紹 71 5.2 治療氣壓自動控制演算法 74 5.3 演算法結果 75 第六章 結論與展望 81 參考文獻 85 | |
dc.language.iso | zh-TW | |
dc.title | 以自動調壓連續陽壓呼吸器治療阻塞型睡眠呼吸中止症之演算法開發 | zh_TW |
dc.title | Development of Algorithm for Autotitrating CPAP for Treating Obstructive Sleep Apnea | en |
dc.type | Thesis | |
dc.date.schoolyear | 104-1 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 李佩玲(Pei-Lin Lee) | |
dc.contributor.oralexamcommittee | 劉宗德(Tsung-Te Liu),楊家驤(Chia-Hsiang Yang) | |
dc.subject.keyword | 呼吸中止症,自動型陽壓呼吸器,呼吸事件偵測,類神經網路,治療氣壓自動調整, | zh_TW |
dc.subject.keyword | sleep apnea,automatically-adjusting positive airway pressure,breathing event detection,neural network,therapeutic pressure autotitration, | en |
dc.relation.page | 91 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2016-02-14 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電子工程學研究所 | zh_TW |
顯示於系所單位: | 電子工程學研究所 |
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