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研究生:胡鎧麟
研究生(外文):Kai-Lin Hu
論文名稱:以PPG脈搏訊號提取時頻特徵做心血管疾病診斷的神經網路
指導教授:陳奇夆
學位類別:碩士
校院名稱:國立中央大學
系所名稱:光機電工程研究所
學門:工程學門
學類:機械工程學類
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:96
中文關鍵詞:小波轉換總體經驗模態分解光體積變化描計圖法倒傳遞神經網路卷積神經網路
外文關鍵詞:Wavelet transformEnsemble empirical mode decompositionPhotoplethysmographyBackpropagation neural networkConvolution neural network
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摘要
本論文主旨在探討有關脈搏訊號研究的方法並結合現今主流的幾套時頻分析理論,以自行開發的一套特徵提取演算法分別依據倒傳遞神經網路及卷積神經網路的訓練需求,提取以光體積變化描計圖法量測到的脈搏訊號在時域或時頻域上的特徵,以監督式學習的方法訓練由資料庫中提供的脈搏訊號樣本,透過倒傳遞神經網路以及卷積神經網路的測試準確度來探討是否能根據時域以及時頻域的特徵成功地辨識被歸類在不同心血管疾病的人;並利用另一組資料庫驗證特徵提取以及神經路設定的合理性。最後討論加入主成分分析法及Dropout的概念優化倒傳遞神經網路並檢驗訓練結果的精準度是否有所上升。
實驗結果證明,倒傳遞神經網路在給定適當的收斂條件下的測試精確度能夠提升到60%至71%之間;而利用卷積神經網路進行訓練得到的測試準確度大約在61%至67%左右。由於卷積神經網路得到的結果較不理想,因此我們利用相同的設定對第二組資料庫進行訓練,最後發現對該筆資料庫訓練後的測試結果準確度能到達74%至78%左右,因此辨識不同健康狀況者所提出的幾種不同提取特徵的方式具有一定合理性與功能性,但因為第一個資料庫提供的樣本過少且訊號量測品質不穩導致最後的測試準確度並不如第二個資料庫的訓練結果來的高。
最後以主成分分析法對對原特徵群做降維的處理後配合Dropout的概念重新對倒傳遞神經網路進行訓練後發現測試準確度可以穩定地維持在70%至72%之間,可以視為一個收斂且可靠的結果。

關鍵字: 小波轉換、總體經驗模態分解、光體積變化描計圖法、倒傳遞神經網路、卷積神經網路
Abstract
The aim of this thesis is to discuss the details and algorithms of signal processing techniques such as continuous wavelet transform, discrete wavelet transform and ensemble empirical mode decomposition; and how they can be applied to human health condition classification by analyzing human pulse with aforementioned methods. By applying these methods and a new algorithm proposed in this thesis on human pulse dataset measured by Photoplethysmography(PPG), the features in time domain and time-frequency domain of PPG pulse signal can be extracted successfully as the inputs to two kinds of artificial neural network: Backpropagation Neural Network(BPNN) and Convolution Neural Network(CNN) to examine whether the 88 samples could be clustered into three different groups according to the cardiovascular conditions described in the datasets.
The experiment shows that the testing accuracy of BPNN with inputs vector composed by six features in time domain, eight features in time-frequency domain and proper stopping criterions to avoid over-fitting is about 67% to 73%. On the other hand, the testing accuracy of CNN with inputs are time-frequency mapped with frequency range between 2Hz to 10Hz and time interval is one second can achieve about 61% to 64%.
Comparing the testing accuracy of two datasets gives us a conclusion that the algorithm used in this paper to extract features affects the classification result is much less than the quality of PPG pulse signal acquired in different circumstances and samples of dataset for analysis.
Keyword: Wavelet transform, Ensemble empirical mode decomposition, Photoplethysmography, Backpropagation neural network, Convolution neural network
目錄
摘要 i
Abstract ii
致謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章、緒論 1
1-1研究背景 1
1-2文獻回顧 1
第二章、基礎理論 6
2-1時頻分析的測不準原理 6
2-2傅立葉轉換 7
2-3短時間傅立葉轉換 8
2-4連續小波轉換 10
2-4-1小波轉換的簡介: 10
2-4-2尺度(Scale) 11
2-4-3通用型摩斯小波(Generalized Morse Wavelet) 15
2-5離散小波轉換(Discrete Wavelet Transform) 17
2-5-1基本演算法 17
2-5-2多貝西小波 18
2-6總體經驗模態分解(Ensemble Empirical Mode Decomposition) 22
2-7光體積變化描計圖法(Photoplethysmography) 25
2-7-1光體積變化描計圖法簡介 25
2-7-2以NI ELVIS Ⅱ及NI LabVIEW進行量測 25
2-8卷積神經網路與倒傳遞神經網路 28
2-8-1倒傳遞神經網路(Backpropagation Neural Network) 28
2-8-2卷積神經網路(Convolution Neural Network) 32
第三章、研究過程 35
3-1資料庫概述 35
3-2倒傳遞神經網路的訓練 36
3-2-1時域特徵提取 36
3-2-2時頻域特徵提取 43
3-2-3訓練過程以及結果 47
3-3卷積神經網路的訓練 54
3-3-1時頻域特徵提取 54
3-3-2訓練過程與結果 57
3-4驗證神經網路以及時頻圖特徵提取方法的合理性 62
3-4-1驗證卷積神經網路的設定 62
3-4-2驗證自行開發的演算法 65
3-5驗證倒傳遞神經網路架構的合理性 68
3-5-1主成分分析 68
3-5-2訓練結果 70
3-6採用平均參數值的概念對倒傳遞神經網路進行訓練 73
3-6-1訓練結果 73
3-7測試自行量測的脈搏訊號 79
3-7-1以NI ELVIS Ⅱ量測到的脈搏做神經網路的驗證資料 79
第四章、結論以及未來展望 80
4-1實驗結果的總結 80
4-2未來展望 81
參考文獻 82
參考文獻
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