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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 呂學士 | |
| dc.contributor.author | Wen-Ting Liao | en |
| dc.contributor.author | 廖文廷 | zh_TW |
| dc.date.accessioned | 2021-06-08T02:52:56Z | - |
| dc.date.copyright | 2017-08-24 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-08-11 | |
| dc.identifier.citation | [1] M. Raghu Ram , K. Venu Madhav , E. Hari Krishna , K. Nagarjuna Reddy, K. Ashoka Reddy,” Use of Multi-Scale Principal Component Analysis for motion artifact reduction of PPG signals,” Recent Advances in Intelligent Computational Systems (RAICS), 2011.
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[17] Yalan Ye, Yunfei Cheng, Wenwen He, Mengshu Hou, Zhilin Zhang,” Combining Nonlinear Adaptive Filtering and Signal Decomposition for Motion Artifact Removal in Wearable Photoplethysmography,” IEEE Sensors Journal, 2016. [18] Navaneet K. Lakshminarasimha Murthy, Pavan C. Madhusudana, Pradyumna Suresha, Vijitha Periyasamy, Prasanta Kumar Ghosh, “Multiple Spectral Peak Tracking for Heart Rate Monitoring from Photoplethysmography Signal During Intensive Physical Exercise,” IEEE Signal Processing Letters, 2015. [19] S. K. Deric Tang, Y. Y. Sebastian Goh, M. L. Dennis Wong, Y. L. Eileen Lew, “PPG signal reconstruction using a combination of discrete wavelet transform and empirical mode decomposition,” International Conference on Intelligent and Advanced Systems, 2016. [20] Kali Vara Prasad Naraharisetti, Manan Bawa, Mansour Tahernezhadi'Comparison of Different Signal Processing Methods.' INTERNATIONAL CONFERENCE ON ELECTRO/INFORMATION TECHNOLOGY,2011. [21] S. Andruschenko, U. Timm, J. 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[25] Rasoul Yousefi, Mehrdad Nourani, Sarah Ostadabbas, Issa Panahi, “A Motion-Tolerant Adaptive Algorithm for Wearable Photoplethysmographic Biosensors,” Journal of Biomedical and Health Informatics, 2014. [26] Yang Wang, Zhiwen Liu, Bin Dong, “Heart rate monitoring from wrist-type PPG based on singular spectrum analysis with motion decision,” Engineering in Medicine and Biology Society, 2016. [27] M. Raghuram; K. Venu Madhav; E. Hari Krishna; K. Ashoka Reddy, “On the Performance of Wavelets in Reducing Motion Artifacts from Photoplethysmographic Signals,” Bioinformatics and Biomedical Engineering, 2010. [28] Satoshi Haraa, Yoshinobu Kawaharaa, Takashi Washioa, Paul von Bünaub, Terumasa Tokunagac,Kiyohumi Yumotod, “Separation of stationary and non-stationary sources with a generalized eigenvalue problem,” Neural Networks Volume 33, September 2012, Pages 7-20. [29] Danny Panknin1, Paul von Bunau, Motoaki Kawanabe,Frank C. Meinecke, Klaus-Robert Muller, “Higher order stationary subspace analysis,” International Meeting on High-Dimensional Data-Driven Science,2015. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/20553 | - |
| dc.description.abstract | 穿戴式裝置於醫療上的應用已被廣泛的應用在臨床醫療及居家照護上，而光體積變化描述圖(PPG)在於即時監控生理資訊(心律、血氧)上，有量測簡易以及成本低的優點，而被大量的探討與研究。然而量測時，因為外在的因素而容易受到干擾(artifact)。為了精確地獲得光體積變化描述圖(PPG)中隱含的生理資訊，本論文提出了基於離散小波轉換與盲訊號分離的演算法來達到干擾(artifact)抑制的處理方法。其中盲訊號分離的演算法利用了獨立成分分析法(ICA)以及平穩子空間分析法(SSA)。最後實驗結果利用峰對峰間距之平均錯誤百分比、峰對峰間距之平均絕對誤差、峰對峰間距之皮爾生相關係數、峰對峰間距之布蘭德-奧特曼差異圖、峰對峰值之平均值、峰對峰值之標準差來做評估。最後結果顯示對受到干擾(artifact)的部分達到了抑制的效果。 | zh_TW |
| dc.description.abstract | Wearable devices in medical applications has been widely used in clinical care and home care. For the use of real-time monitoring for physiological information (heart rhythm, blood oxygen), photoplethysmogram(PPG) has the advantage of easy measurement and low cost. Thus, it has been a lot of discussions and researches. However, while measuring, it is vulnerable to the external interference and thus cause the artifact contamination. In order to accurately obtain the physiological information implied in the photoplethysmogram (PPG), we propose a method to deal with the artifact suppression based on the algorithm of discrete wavelet transform and blind source separation in this thesis. The algorithm for the separation of blind source uses independent component analysis (ICA) and stationary subspace analysis (SSA). In the final results of the experiment, we use the average error percentage of the peak-to-peak spacing, the average absolute error of the peak-to-peak spacing, the Pearson correlation coefficient of the peak-to-peak spacing, the Bland-Altman difference plot of the peak-to-peak spacing, the average of the peak to the peak values, the standard deviation of the peak to the peak values to do the assessment. The final result shows that the proposed methods have the effect of artifact suppression. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T02:52:56Z (GMT). No. of bitstreams: 1 ntu-106-R04943112-1.pdf: 2598910 bytes, checksum: ec2135cf76b0b8c39aa32b8eb259b0c8 (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | CONTENTS
中文摘要 I ABSTRACT II CONTENTS III LIST OF FIGURES VII LIST OF TABLE XI Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Thesis Organization 3 Chapter 2 Photoplethysmography 5 2.1 Introduction of Photoplethysmography 5 2.2 Measurement 7 Chapter 3 Algorithm and Analysis 9 3.1 Independent Component Analysis 9 3.1.1 Introduction of Independent Component Analysis 9 3.1.2 Definition of Independent Component Analysis 13 3.1.3 Definition of Independence and the restrictions using ICA 14 3.1.4 Introduction of measures of non-Gaussianity 17 3.1.5 Kurtosis 17 3.1.6 Negentropy 18 3.1.7 Introduction of Preprocessing for ICA 21 3.1.8 Centering 21 3.1.9 Whitening 22 3.1.10 Ambiguities of ICA 23 3.1.11 FastICA 24 3.1.12 FastICA for one unit 24 3.1.13 FastICA for several unit 25 3.1.14 Flow path of the FastICA algorithm 26 3.2 Stationary Subspace Analysis 26 3.2.1 Introduction of Stationary Subspace Analysis 27 3.2.2 Definition of Stationary Subspace Analysis 28 3.2.3 Measuring stationary 31 3.2.4 Objective function 33 3.2.5 Flow path of the SSA algorithm 35 3.3 Discrete Wavelet Transform (DWT) 36 3.3.1 Introduction 36 3.3.2 The Discrete Wavelet Transform Decomposition 37 3.3.3 Mother Wavelet Selection 38 3.3.4 De-noising Method 41 Chapter 4 Experiment Methods and Results 42 4.1 Experiments Using Stationary Subspace Analysis 42 4.1.1Suppression Experiment 1 42 4.1.2 Suppression Experiment 2 57 4.1.3 Suppression Experiment 3 61 4.2 Experiments Using Independent Component Analysis 65 4.2.1Suppression Experiment 1 65 4.2.2 Suppression Experiment 2 80 4.2.3 Suppression Experiment 3 84 Chapter 5 Conclusion and Future Work 88 5.1 Conclusion 88 5.2 Future Work 89 Chapter 6 Reference 92 | |
| dc.language.iso | en | |
| dc.title | 基於盲訊分離與離散小波轉換之抑制PPG上雜訊的方法 | zh_TW |
| dc.title | Blind Source Separation Based Method with DWT
for Artifact Suppression in PPG Signals | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 孟慶宗,彭盛裕,曹昱 | |
| dc.subject.keyword | 盲訊號分離演算法,獨立成分分析法,平穩子空間分析法,離散小波轉換,光體積變化描述圖, | zh_TW |
| dc.subject.keyword | BSS,ICA,SSA,PPG, | en |
| dc.relation.page | 97 | |
| dc.identifier.doi | 10.6342/NTU201702887 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2017-08-11 | |
| 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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