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

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：83

、訪客IP：3.147.48.240

姓名

胡鎧麟(Kai-Lin Hu) 查詢紙本館藏

畢業系所

光機電工程研究所

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

相關論文

★ 直下式背光模組最佳化之設計	★ 反射式發光二極體光源之近燈頭燈設計
★ 指紋辨識之光學成像系統設計	★ 微型投影機之LED光源設計
★ 具積體型稜鏡體之指紋辨識光學模組的光學特性分析研究	★ 應用田口穩健設計法於特殊函數調變變化規範下的絕熱式光方向完全耦合器波導結構設計優化
★ 雙反射面鏡型太陽能集光模組設計	★ 使用光線追跡法設計軸對稱太陽能集光器
★ 應用於直下式背光模組之邊射型發光二極體設計與其模組研究	★ 高功率LED二次光學透鏡模組設計
★ 微型雷射投影機光學設計	★ LED陣列用於室內照明之設計與驗證
★ 應用於聚光型太陽光電系統之二次光學元件設計與分析	★ 一種色溫及色彩可控制的多光源燈具設計
★ 運用光場程式化技巧快速設計LED直下式背光模組之研究	★ 應用於彩色共焦顯微術之繞射元件設計

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

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

摘要(英)

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

關鍵字(中)

★ 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

關鍵字(英)

★ Wavelet transform
★ Ensemble empirical mode decomposition
★ Photoplethysmography
★ Backpropagation neural network
★ Convolution neural network

論文目次

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

參考文獻
[1] Maeda, Y., Sekine, M., Tamura, T., Moriya, A., Suzuki, T., & Kameyama, K. (2008, August). Comparison of reflected green light and infrared photoplethysmography. In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 2270-2272). IEEE.
[2] Zuo, W., Wang, P., & Zhang, D. (2014). Comparison of three different types of wrist pulse signals by their physical meanings and diagnosis performance. IEEE journal of biomedical and health informatics, 20(1), 119-127.
[3] Shahbakhti, M., Bagheri, H., Shekarchi, B., Mohammadi, S., & Naji, M. (2016). A new strategy for ecg baseline wander elimination using empirical mode decomposition. Fluctuation and Noise Letters, 15(02), 1650017..
[4] Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., ... & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, 454(1971), 903-995.
[5] Wu, Z., & Huang, N. E. (2009). Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in adaptive data analysis, 1(01), 1-41.
[6] Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., ... & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, 454(1971), 903-995.
[7] Zhang, D., Zhang, L., Zhang, D., & Zheng, Y. (2008, May). Wavelet based analysis of doppler ultrasonic wrist-pulse signals. In 2008 International Conference on BioMedical Engineering and Informatics (Vol. 2, pp. 539-543). IEEE.
[8] Huang, Q., Zheng, G., Yan, G., & Dai, M. (2011, October). Key points recognition of pulse wave based on wavelet transform. In 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI) (Vol. 4, pp. 1753-1756). IEEE.
[9] Zhang, Z., Umek, A., & Kos, A. (2017). Computerized Radial Artery Pulse Signal Classification for Lung Cancer Detection. Facta Universitatis, Series: Mechanical Engineering, 15(3), 535-543.
[10] Liang, Y., Chen, Z., Liu, G., & Elgendi, M. (2018). A new, short-recorded photoplethysmogram dataset for blood pressure monitoring in China. Scientific data, 5, 180020.
[11] Zhang, S., Chen, X., & Sun, Q. (2015, October). Arteriosclerosis feature extraction for human pulse signals. In 2015 International Symposium on Bioelectronics and Bioinformatics (ISBB) (pp. 192-195). IEEE.
[12] Olhede, S. C., & Walden, A. T. (2002). Generalized morse wavelets. IEEE Transactions on Signal Processing, 50(11), 2661-2670.
[13] Lilly, J. M., & Olhede, S. C. (2008). Higher-order properties of analytic wavelets. IEEE Transactions on Signal Processing, 57(1), 146-160.
[14] Lilly, J. M., & Olhede, S. C. (2012). Generalized Morse wavelets as a superfamily of analytic wavelets. IEEE Transactions on Signal Processing, 60(11), 6036-6041.
[15] https://keras.io/about/
[16] Mallat, S. G. (1989). A theory for multiresolution signal decomposition: the wavelet representation. IEEE transactions on pattern analysis and machine intelligence, 11(7), 674-693.
[17] Wang, X., & Zhang, J. (2005, June). A traffic incident detection method based on wavelet Mallat algorithm. In Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications, 2005. SMCia/05. (pp. 166-172). IEEE.
[18] Daubechies, I. (1992). Ten lectures on wavelets. Society for industrial and applied mathematics.
[19] DOI: http://dx.doi.org/10.17632/yynb8t9x3d.1#file-a6c8e62b-023e-43d8-970a-f07f76dfefc7
[20] DOI: http://dx.doi.org/10.17632/yynb8t9x3d.1#file-abbea806-c03f-4e58-a944-4902c1ba87d1
[21] Siam, Ali; Abd El-Samie, Fathi; Abu Elazm, Atef; El-Bahnasawy , Nirmeen; Elbanby, Ghada (2019), “Real-World PPG dataset”, Mendeley Data, v1http://dx.doi.org/10.17632/yynb8t9x3d.1
[22] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
[23] http://deeplearning.net/tutorial/lenet.html
[24] De Boer, P. T., Kroese, D. P., Mannor, S., & Rubinstein, R. Y. (2005). A tutorial on the cross-entropy method. Annals of operations research, 134(1), 19-67.
[25] https://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html
[26] https://www.jianshu.com/p/8bf8effaf219
[27] Goupillaud, P., Grossmann, A., & Morlet, J. (1984). Cycle-octave and related transforms in seismic signal analysis. Geoexploration, 23(1), 85-102.
[28] Kumar, P., & Foufoula‐Georgiou, E. (1997). Wavelet analysis for geophysical applications. Reviews of geophysics, 35(4), 385-412.
[29] Aguiar‐Conraria, L., & Soares, M. J. (2014). The continuous wavelet transform: Moving beyond uni‐and bivariate analysis. Journal of Economic Surveys, 28(2), 344-375.
[30] Rilling, G., Flandrin, P., & Goncalves, P. (2003, June). On empirical mode decomposition and its algorithms. In IEEE-EURASIP workshop on nonlinear signal and image processing (Vol. 3, No. 3, pp. 8-11). NSIP-03, Grado (I).
[31] Junsheng, C., Dejie, Y., & Yu, Y. (2006). Research on the intrinsic mode function (IMF) criterion in EMD method. Mechanical systems and signal processing, 20(4), 817-824.
[32] Rohatgi, A. (2017). WebPlotDigitizer.
[33] https://www.cnblogs.com/LeftNotEasy/archive/2011/01/19/svd-and-applications.html
[34] Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2(11), 559-572.
[35] Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of educational psychology, 24(6), 417.
[36] https://builtin.com/data-science/step-step-explanation-principal-component-analysis
[37] Zhang, S., & Sun, Q. (2015, September). Human pulse recognition based on wavelet transform and BP network. In 2015 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) (pp. 1-4). IEEE.
[38] Shannon, C. E. (1949). Communication theory of secrecy systems. The Bell system technical journal, 28(4), 656-715.
[39] Boashash, B. (2015). Time-frequency signal analysis and processing: a comprehensive reference. Academic Press.

指導教授

陳奇夆

審核日期

2020-8-18

推文