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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 曹恆偉(Hen-Wai Tsao),錢膺仁(Ying-Ren Chien) | |
dc.contributor.author | Kai-Chieh Hsu | en |
dc.contributor.author | 許凱傑 | zh_TW |
dc.date.accessioned | 2021-06-17T09:06:41Z | - |
dc.date.available | 2025-01-15 | |
dc.date.copyright | 2020-01-15 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-12-26 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74734 | - |
dc.description.abstract | 近年來智慧醫療領域越來越受重視,許多通/資訊科技包括雲端、物聯網、遠距、大數據分析及人工智慧等,已大量應用於醫療領域中。隨著社會人口年齡層的老化,慢性病的患者逐漸增加,應用於長期病患監測服務、行動照護的需求漸增。
心音圖提供一種非侵入式檢測方法,用來偵測心臟瓣膜異常及輔助心臟病的病因判斷,在長期心音圖檢測而產生龐大資料量情況下,一個有效率的訊號壓縮系統是必要的。目前的醫學檢測大都是在醫療院所完成,不少病患為了檢查及回診,需來回奔波,浪費病患等候看診的時間,也浪費不少社會資源。對此,發展遠距醫療(telemedicine)是台灣現今一個很重要的課題,遠距醫療是利用遠距通訊傳遞醫學資訊的一種新科技,更重要的是,開創了一種新的醫學溝通方式,使醫師與病人間可進行同步與非同步的互動,克服空間與時間的障礙,改善醫療品質、降低社會成本及增加便利性。本論文提出一種利用深度卷積自動編碼器的心音圖壓縮方法,對遠端通訊網路中干擾所造成的錯誤,有一定的容忍度,可應用於遠端醫療上,能改善發展遠距醫療會受到的限制。 本論文首章節為論文簡介,第二章及第三章為背景知識介紹,從第四章開始為本論文主要貢獻,也就是系統架構設計,包含心音訊號切割、心音特徵定義、自動編碼器架構…等,並在第五章探討模擬測試結果,第六章為結語與未來展望,最末章為附錄。 | zh_TW |
dc.description.abstract | In the past few years, the field of smart health has been more and more important. Lots of technology such as cloud computing、internet of things、remote control、big data analysis and artificial intelligence has already applied to medical field. As the population ages, the population of chronic diseases gradually increases. The demand for long-term patient monitoring services and action care is increasing.
The Phonocardiogram(PCG) provides a non-invasive method for detecting heart valve abnormalities and assisting in the diagnosis of heart disease. Due to huge amounts of data generated by long-term PCG monitoring, an efficient signal compression method is necessary. Most of the current medical tests are done in medical institutions. Many patients need to go back and forth in order to check and return to the hospital. This wastes the patient’s time for medical consultation, and also wastes a lot of social resources. Therefore, the development of telemedicine is a very important issue today. Telemedicine is a new technology that uses telematics to transmit medical information. More importantly, it has created a new way of medical communication, enabling synchronized and asynchronous interaction between physicians and patients, overcoming space and time barriers, improving medical quality, reducing social costs and increasing convenience. This paper proposes a PCG compression method using deep convolutional auto-encoder. This method has a certain allowable error rate for interference errors in remote communication, and can be applied to telemedicine, which will slove the problems of developing telemedicine. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T09:06:41Z (GMT). No. of bitstreams: 1 ntu-108-R06942037-1.pdf: 4079142 bytes, checksum: 1f2970262d715ec06232369be8d56683 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 目錄
摘要 I Abstract II 目錄 III 圖目錄 VI 表目錄 X 第一章 緒論 1 1.1 前言 1 1.2 研究主題及主要貢獻 2 1.3 論文架構 2 第二章 基本背景資訊 3 2.1 心音(Heart Sound) 3 2.1.1. 心音基本介紹 3 2.1.2. 心音波形特徵 6 2.1.3. 心音的採集方式及感測器 10 2.1.4. 典型心音圖 12 2.1.5. 應用:基於心音的輔助診斷 14 2.2 音訊壓縮 15 2.2.1. 無損音訊壓縮(Lossless Compression) 15 2.2.2. 有損音訊壓縮(Lossy Compression) 15 2.2.3. 壓縮評估標準 16 2.2.4. 常見壓縮格式 18 2.3 遠端醫療(Telemedicine) 19 2.4 心音壓縮演算法 21 第三章 深度學習與自動編碼器簡介 25 3.1 監督式學習(Supervised Learning) 26 3.2 深度學習基礎 30 3.2.1. 人工神經網路(Artificial Neural Network) 31 3.2.2. 常見激活函數(Common Activation Function) 33 3.2.3. 訓練過程(Training Process) 33 3.2.4. 過度擬合(Overfitting) 35 3.2.4.1數據標準化(Data Normalization) 35 3.2.4.2正規化(Reaularization) 36 3.2.4.3早停法(Early Stopping) 37 3.3 自動編碼器介紹(Introduction to Autoencoder) 38 3.3.1. 自動編碼器 38 3.3.2. 深度類神經網路(Deep Neural Network,DNN) 42 3.3.3. 卷積類神經網路(Convolution Neural Network) 43 第四章 心音信號深度網路壓縮系統 46 4.1 數據集(Dataset) 47 4.2 數據前處理(Data Pre-processing) 50 4.2.1. 數據歸一化與裁剪 50 4.2.2. 心音分割(Heart Sound Segmentation ) 53 4.3 心音特徵選取 56 4.4 通訊網路之錯誤 58 4.5 壓縮類神經網路架構模型 60 第五章 心音信號深度壓縮系統模擬結果 68 5.1 實驗模擬結果 68 5.2 浮點數轉定點化(floating point to fixed point) 82 5.3 考量通訊網路錯誤結果 86 5.4 模型複雜度討論 88 5.5 應用於小型計算平台 94 5.6 心音壓縮方法比較 96 第六章 結論與未來展望 99 6.1 結論 99 6.2 未來展望 100 附錄 101 參考文獻 113 圖目錄 第二章 圖2- 1心臟的結構及相關血管 3 圖2- 2胸部正面圖 5 圖2- 3正常心音 12 圖2- 4主動脈逆流(主動脈關閉不全) 12 圖2- 5主動脈狹窄 13 圖2- 6二尖瓣逆流(關閉不全,伴有第三心音) 13 圖2- 7肺動脈狹窄 13 圖2- 8伴隨有第三心音 14 第三章 圖3- 1機器學習關係圖 25 圖3- 2機器學習基本架構 26 圖3- 3監督式學習概念圖 28 圖3- 4監督式學習的流程[15] 28 圖3- 5資料集分割圖[16] 29 圖3- 6人類大腦中神經元傳遞訊運作方式[19] 30 圖3- 7人工神經網路 31 圖3- 8單一神經元模型 32 圖3- 9常見的激活函數 33 圖3- 10不同學習率在收斂的影響關係[23] 34 圖3- 11早停發生在訓練集錯誤下降。但驗證集錯誤要上升時[25] 37 圖3- 12一個簡單自動編碼器網路 39 圖3- 13自動編碼器架構與它的變形[49] 40 圖3- 14深度自動編碼器架構[36] 42 圖3- 15一維卷積層運作示意圖[45] 45 第四章 圖4- 1深度壓縮心音系統方塊圖 46 圖4- 2 DLUTHSDB心音資料(a) 編號W00005之心音(b) 編號W00006之心音(c) 編號W00007之心音。 49 圖4- 3心音原始信號。(a)編號W00002之心音(b)編號W00003之心音 50 圖4- 4心音信號經過歸一化。(a)編號W00002心音 (b)編號W00003心音 51 圖4- 5將心音訊號做切割。(a)、(b)、(c)皆由編號W00002心音信號所切割的片段。 51 圖4- 6資料增強中的時移增強(Time Shift Augmentation) 52 圖4- 7心音圖(PCG)與心電圖(ECG)基本分割圖示[40] 54 圖4- 8心音分割圖 55 圖4- 9輸入特徵與對應的訊號 57 圖4- 10遠端醫療概念圖 58 圖4- 11實驗流程圖 60 圖4- 12池化與升採樣層運作圖 61 圖4- 13深度壓縮心音網路架構圖 62 第五章 圖5- 1壓縮率27,輸入訊號6000點壓縮結果。(a)原始心音片段 (b)重建心音片段 (c)原始心音與壓縮後還原心音訊號 (d)原始心音訊號經編碼端壓縮後的波形 70 圖5- 2不同訊號長度的心音片段 (a) 2000點心音片段 (b) 3000點心音片段 (c) 8000點心音片段 71 圖5- 3壓縮率27,輸入訊號8000點壓縮結果。(a)壓縮後的訊號表示 (b)原始心音與壓縮後還原心音訊號 73 圖5- 4訓練模型階段,損失函數趨勢圖 75 圖5- 5模型壓縮率30實驗結果。(a)原始心音片段 (b)重建心音片段 (c)原始心音與壓縮後還原心音訊號 (d)壓縮後的訊號波形 78 圖5- 6模型壓縮率32實驗結果。(a)原始心音片段 (b)重建心音片段 (c)原始心音與壓縮後還原心音訊號 (d)壓縮後的訊號波形 79 圖5- 7模型壓縮率36實驗結果。(a)原始心音片段 (b)重建心音片段 (c)原始心音與壓縮後還原心音訊號 (d)壓縮後的訊號波形 80 圖5- 8 8位元定點數格式示意圖 82 圖5- 9 Mobile-Net在主流設備下的評估結果 92 圖5- 10小型計算平台流程圖 94 圖5- 11Tensorflow Lite的結構設計 94 第七章 圖7- 1深度壓縮心音網路架構圖(壓縮率27) 101 圖7- 2深度壓縮心音網路架構圖(壓縮率30) 103 圖7- 3深度壓縮心音網路架構圖(壓縮率36) 105 圖7- 4壓縮率27,輸入訊號2000點壓縮結果。(a)原始心音片段 (b)重建心音片段 (c)原始心音與壓縮後還原心音訊號 (d)壓縮後的訊號波形 110 圖7- 5壓縮率27,輸入訊號3000點壓縮結果。(a)原始心音片段 (b)重建心音片段 (c)原始心音與壓縮後還原心音訊號 (d)壓縮後的訊號波形 111 圖7- 6壓縮率27,輸入訊號8000點壓縮結果。(a)原始心音片段 (b)重建心音片段 (c)原始心音與壓縮後還原心音訊號 (d)壓縮後的訊號波形 112 表目錄 第二章 表2- 1心動週期介紹 6 表2- 2心音特徵 8 表2- 3胸部常見聽診區 10 表2- 4常見音訊壓縮技術的壓縮率 18 第四章 表4- 1 PHYSIONET心音數據總集的詳細資料[46] 48 表4- 2編碼端架構詳細參數表 65 表4- 3解碼端架構詳細參數表 66 第五張 表5- 1壓縮率27情況時,不同訊號長度的模型結果表 71 表5- 2各壓縮率下PRD值 81 表5- 3 8位元定點數不同整數位、小數位情形 83 表5- 4架構模型參數範圍表 84 表5- 5定點化模擬結果 85 表5- 6有傳輸網路干擾情況下的模擬結果 86 表5- 7深度模型MACs表 90 表5- 8經典深度類神經模型的運算量 91 表5- 9在Eyeriss運行循環數 93 第七章 表7- 1壓縮率27模型架構詳細參數表 102 表7- 2壓縮率30模型架構詳細參數表 104 表7- 3壓縮率36模型架構詳細參數表 106 表7- 4輸入訊號2000點,壓縮率27模型架構詳細參數表 107 表7- 5輸入訊號3000點,壓縮率27模型架構詳細參數表 108 表7- 6輸入訊號8000點,壓縮率27模型架構詳細參數表 109 | |
dc.language.iso | zh-TW | |
dc.title | 應用於心音訊號壓縮的卷積自動編碼器 | zh_TW |
dc.title | Heart sound Compression based on Convolutional Autoencoder | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 曹昱(Tsao Yu),馬文忠(Wen-Jong Ma),李揚漢(Yang-Han Lee) | |
dc.subject.keyword | 訊號壓縮,心音圖,深度學習,自動編碼器,遠端醫療, | zh_TW |
dc.subject.keyword | Signal compression,PCG,Deep Learning,Autoencoder,Telemedicine, | en |
dc.relation.page | 119 | |
dc.identifier.doi | 10.6342/NTU201904432 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-12-26 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
顯示於系所單位: | 電信工程學研究所 |
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