臺灣博碩士論文加值系統

人的一生中大約花三分之一的時間在睡眠，良好的品質和充足的睡眠對所有人都至關重要，研究顯示心血管疾病和中風的風險與嚴重的睡眠呼吸中止症有高度相關，因此，正確診斷和治療睡眠呼吸中止症是非常重要的。在過去的幾十年，自動睡眠分期判別演算法幾乎都需要靠人工提取特徵的方式，近幾年來隨著人工智慧的迅速發展，許多新的深度學習相關模型廣泛的應用在醫學領域。臨床睡眠檢查中，睡眠多項生理檢測儀(Polysomnography,PSG)是睡眠品質評估和睡眠呼吸相關疾病診斷的標準設備，分析結果通常由睡眠技師使用電腦輔助睡眠分析軟體來標示呼吸中止/低通氣事件及睡眠分期判別。毫無疑問，這是一項非常耗時的工作，為此開發一套自動睡眠分期和睡眠呼吸中止偵測系統，來協助診斷睡眠障礙是必要的。根據43名成人的資料，我們提出了一個新的模型，該模型由三個平行的卷積神經網路Convolutional Neural Network(CNN)組成，由基本的卷積網路擴展而來，類似於Lenet-5和Alexnet，結果顯示所提出的模型可以達到82.5%自動睡眠分期準確度，並可克服睡眠呼吸中止所帶來的腦波干擾。另外，對於睡眠呼吸中止的檢測，採用了無束縛式光纖感測墊配合自動篩選演算法，首先透過訊號確認呼吸事件的下降率來驗證光纖感測的對於事件的偵測能力，接下來以整體的觀點來計算呼吸振幅下降持續時間的百分比Percentage of the Total Duration of Respiratory Declination (PTDRD)並求得線性回歸函數模型，得到睡眠技師判定呼吸暫停/低通氣指數（Apnea/Hypopnea,AHI）與模型推估的AHI之高一致性。根據63名成年人的資料分析顯示，一、呼吸訊號的下降程度在中樞型睡眠呼吸中止、阻塞型睡眠呼吸中止和正常呼吸之間有統計學差異，二、頭頸和胸背光纖感測墊所得的PTDRDS回歸模型在布蘭德-奧爾特曼圖證明和睡眠技師判定的AHI有很好的一致性，上述結果證明光纖感測墊量測的能力和PTDRD指標測量的有效性，可適用於居家睡眠呼吸中止的篩選評估。

A higher risk of cardiovascular diseases and strokes is seen to be relevance to severe sleep apnea. Therefore, definitive diagnosis and treatment of sleep apnea are very important. In past decades, automatic sleep stage scoring almost depended on feature extraction based methods by human intelligence. In clinical sleep examination, polysomnography (PSG) is considered as gold standard equipment which can provide comprehensive bio-signals for sleep quality assessment and sleep breathing-related disorder diagnosis. Identification of apnea/hypopnea events and sleep stage classification is usually performed by sleep expert with computer-assisted sleep scoring system. Undoubtedly, this is an extremely time consuming work. To develop an automatic sleep stage scoring and sleep apnea detection system to aid physician diagnose sleep disorders is necessary.
In this study, we proposed a new model that was composed of three parallel convolutional neural network(CNN) extended from a deep CNN resembled the LeNet-5 and AlexNet, and achieved overall 82.5% accuracy for automatic sleep stage scoring. For sleep apnea detection, automatic screening approach with an unobtrusive sensor was adopted two approaches: drop degrees from baseline to validate the capability of catching respiratory drops, and linear regression models based on a new global measure, percentage of the total duration of respiratory declination (PTDRD), to estimate the hand-scored apnea/hypopnea index (AHI). The drop degrees derived from respiratory signals exhibited statistical differences among central sleep apnea, obstructive sleep apnea, and normal breathing. The regression models based on the PTDRDs derived from head-neck fiber optic sensor(FOS) and thoracic-dorsal FOS also achieved good agreements with manually scored AHIs in Bland-Altman plots as well as oronasal airflow and thoracic wall movement did. The aforementioned performance demonstrates the capability of the FOS measurement and the efficacy of the PTDRD metrics for sleep apnea assessment.

目錄
指導教授推薦書
口試委員會審定書
誌謝 iii
中文摘要 iv
Abstract v
目錄 vi
圖目錄 viii
表目錄 ix
第一章、緒論 1
1.1研究背景與動機 2
1.2睡眠檢測介紹 3
1.2.1多通道生理睡眠檢測裝置 3
1.2.2睡眠分期與睡眠呼吸中止 5
1.3 睡眠檢測方法回顧與自動化分析裝置 8
1.4 論文目的 10
1.5論文架構 10
第二章、深度學習於醫學領域之應用 11
2.1睡眠腦波分期判讀 11
2.2實驗裝置與環境設置 12
2.2.1睡眠分期試驗招募對象與訊號收集 12
2.2.2睡眠腦波訊號處理 13
2.3實驗分析方法 13
2.3.1網路一:單一卷積神經網路 15
2.3.2網路二:多個平行卷積神經網路 20
2.4睡眠分期結果 22
2.4.1網路一:單一卷積神經網路 23
2.4.2網路二:多個平行卷積神經網路 24
2.5 睡眠分期分類討論與結論 27
第三章 睡眠呼吸中止分析 28
3.1主流呼吸感測裝置介紹 29
3.1.1 無束縛式光纖感測裝置 29
3.2實驗裝置與環境設置 31
3.2.1 睡眠呼吸中止試驗招募對象與訊號收集 32
3.3 實驗分析方法與指標 33
3.3.1各種呼吸事件的呼吸振幅下降程度 35
3.3.2呼吸整體下降幅度量化 36
3.3.3基於線性回歸模型的AHI估計 40
3.3.4 Bland-Altman analysis布蘭德-奧爾特曼分析 41
3.4 實驗結果 41
3.4.1各種呼吸事件的呼吸振幅下降程度結果 41
3.4.2基於線性回歸模型的AHI估計結果 43
3.5睡眠呼吸中止偵測討論與結論 45
第四章 結論與展望 46
4.1 引言 46
4.2 自動化睡眠檢測分析方法 46
4.2.1 自動化睡眠分期方法 46
4.2.2 睡眠呼吸中止指標 47
4.3 睡眠檢測裝置 49
4.3.1 傳統穿戴式感測裝置 49
4.3.2 無束縛式感測裝置 49
參考文獻 51


圖目錄
圖一、未被診斷的睡眠呼吸中止所帶來的損失與危害 3
圖二、Philips Alice 6 LDX PSG System睡眠多項生理檢查儀 4
圖三、國際標準10/20腦波電極配置法 6
圖四、睡眠腦波頻率與發生位置一 7
圖五、睡眠腦波頻率與發生位置二 7
圖六、Wake、N1、N2、N3及REM在F3M2和C3M2的腦波表現 11
圖七、標準睡眠檢測電極黏貼配置圖 12
圖八、單一卷積神經網路架構 14
圖九、兩個通道腦波訊號為卷積神經網路輸入訊號(劃分30秒為單位) 15
圖十、卷積層輸入腦波訊號與卷積核(Kernel filter)功能描述 15
圖十一、卷積核(Kernel filter)與特徵圖(Feature map) 16
圖十二、最大值池化層特徵選取與降採樣 16
圖十三、drop out與堆疊層 17
圖十四、三個平行卷積神經網路架構 20
圖十五、留一交叉驗證方式表示圖 22
圖十六、光纖形變與光衰減示意圖 31
圖十七、兩個光纖感測墊放置位置示意圖 32
圖十八、呼吸訊號（藍色）和計算出瞬時呼吸強度IRI（紅色）圖 35
圖十九、呼吸振幅下降總持續時間的百分比計算流程圖 37
圖二十、三個例子來證明局部閥值適應不同的呼吸情況 39
圖二十一、說明檢測呼吸下降的訊號圖 40
圖二十二、回歸模型的Bland-Altman圖 44
圖二十三、正常受測者睡眠分期圖 47
圖二十四、三層光纖結構圖 50
圖二十五、2mm厚度的光纖感測墊實體圖 50

表目錄
表一、腦波睡眠分期招募之受測者資料 13
表二、不同睡眠期別的epoch數量 14
表三、單一卷積神經網路超參數(卷積核長度100) 18
表四、單一卷積神經網路超參數(卷積核長度200) 19
表五、單一卷積神經網路超參數(卷積核長度300) 19
表六、三個平行卷積神經網路超參數 21
表七、單一卷積神經網路混淆矩陣confusion matrix(卷積核長度100) 23
表八、單一卷積神經網路混淆矩陣confusion matrix(卷積核長度200) 24
表九、單一卷積神經網路混淆矩陣confusion matrix(卷積核長度300) 24
表十、平行卷積神經網路confusion matrix(43位全體受測者) 25
表十一、平行卷積神經網路confusion matrix(16位正常受測者) 25
表十二、平行卷積神經網路confusion matrix(15位輕度呼吸中止受測者) 26
表十三、平行卷積神經網路confusion matrix(12位中重度呼吸中止受測者) 26
表十四、睡眠呼吸中止試驗招募之受測者資料 33
表十五、各種呼吸感測器在不同類型呼吸事件發生時的呼吸下降率 42
表十六、呼吸振幅下降總持續時間百分比（PTDRD）估算回歸模型 43

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