請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/7917完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 鄭士康 | zh_TW |
| dc.contributor.advisor | Shyh-Kang Jeng | en |
| dc.contributor.author | 楊惠雯 | zh_TW |
| dc.contributor.author | Hui-Wen Yang | en |
| dc.date.accessioned | 2021-05-19T17:58:34Z | - |
| dc.date.available | 2024-05-23 | - |
| dc.date.copyright | 2020-06-10 | - |
| dc.date.issued | 2020 | - |
| dc.date.submitted | 2002-01-01 | - |
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Smiley Jeya Jothi, "Measurement of arterial oxygen saturation (SpO2) using PPG optical sensor," International Conference on Communication and Signal Processing, ICCSP 2016, pp. 1136-1140, 2016, doi: 10.1109/ICCSP.2016.7754330. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/7917 | - |
| dc.description.abstract | 經驗模態分解是一個被廣為使用的時頻分析工具，然而，訊號中的雜訊干擾，例如突波，可能同時造成模態混合和模態分裂的問題，使得一個物理上有意義的成份被拆解成二個以上的本質模態函數。在此論文中，我們引用近期發展出的經驗模態分解的數學理論，提供突波問題造成模態混合和模態分裂的理論解釋，並且基於此理論基礎，提出了解決突波問題的架構——最小弧長條件。為了更穩健地將突波分離至原先不存在的本質模態函數中，我們加入了以弦波輔助的遮罩方法，而形成了「遮罩—最小弧長—經驗模態分解」。在論文中提供了此方法的數學理論和數值模擬，並且應用至真實世界的訊號，包括電流中的突波干擾、軸承震動訊號、睡眠腦波中的週期性交替模式和核心體溫的生理時鐘。更有甚者，我們將此方法應用在標準十二導心電圖上以分離P波的波形，並且證明由此P波波形所提取的特徵，可以用來偵測受測者是否有潛在的心房顫動。最後，我們將此方法延伸至單位階梯函數上，並且提出一個廣適性的演算法，來處理第N階導數為突波的訊號。 | zh_TW |
| dc.description.abstract | Empirical mode decomposition (EMD) is an extensively utilized tool in time-frequency analysis. However, disturbances such as impulse noise can result in both mode-mixing and mode-splitting effect, in which one physically meaningful component is split in two or more intrinsic mode functions (IMFs). In this work, we provide a mathematical explanation for the cause of mode-mixing and mode-splitting by spikes in EMD, and propose a novel method, the minimum arclength EMD (MA-EMD), to robustly decompose time series data with spikes. To further isolate the spike in a previously non-existed IMF, the masking-aided MA-EMD (MAMA-EMD) is provided. The mathematical foundations and limitations for these two methods are provided. The MAMA-EMD is utilized to deal with four real-world data including electrical current, vibration signals, cyclic alternating pattern in sleep EEG (Electroencephalography), and circadian of core body temperature. In addition, this work developed a tool for P-wave isolation in electrocardiogram (ECG) by the MAMA-EMD method, and showed that the P-wave related features can be used to identify potential atrial fibrillation patients. Finally, we extend our application to the Heaviside step function and propose a general algorithm for signals whose Nth order derivative is a spike function. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-19T17:58:34Z (GMT). No. of bitstreams: 1 ntu-109-D03942010-1.pdf: 4491009 bytes, checksum: c61b26d3af55014d846b5b0bfb008262 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 謝辭 I
中文摘要 II ABSTRACT III CONTENTS IV LIST OF FIGURES VII LIST OF TABLES X CHAPTER 1. INTRODUCTION 1 1.1 STATEMENT OF PURPOSE 1 1.2 CONTRIBUTION 2 1.3 RELATED WORKS 3 1.3.1 EMD and time-frequency decomposition 3 1.3.2 Modifications of EMD 5 1.4 SPIKE PROBLEMS 7 1.4.1 Spike problems in signal processing 7 1.4.2 Spike problems in EMD 7 1.5 OVERVIEW 9 CHAPTER 2. BACKGROUND 11 2.1 EMD 11 2.2 IMPULSE RESPONSE OF EMD 11 2.3 MODE-MIXING AND MODE-SPLITTING 14 2.4 MASKING EMD 15 2.5 THE SPIKE DETECTION 16 CHAPTER 3. EFFECT ANALYSIS OF SPIKE IN EMD AND MA-EMD 18 3.1 EFFECT ANALYSIS OF SPIKE PROBLEM IN EMD 18 3.2 MSI: MEASUREMENT OF MODE-SPLITTING 20 3.3 NE: NEWLY GENERATED EXTREMA 21 3.4 SK-EMD: SKIPPING THE EXTREMA ON SPIKES 21 3.5 MA-EMD 22 3.5.1 Minimum arclength criterion 22 3.5.2 Mathematical foundation 24 3.5.3 Simulation verification 25 3.5.4 Comparative accuracy analysis 31 3.6 COMPARISON BETWEEN SKIP AND MA-EMD 34 CHAPTER 4. MASKING-AIDED MINIMUM ARCLENGTH EMD 36 4.1 INTRODUCTION 36 4.2 DETERMINATION OF MASKING SIGNAL 37 4.3 SIMULATION VERIFICATION 39 4.3.1 Single Sinusoid 39 4.3.2 DUFFING WAVE 42 4.4 LIMITATIONS 44 4.5 EXAMPLES 45 4.5.1. Electrical current 45 4.5.2 Rotor test rig 48 4.5.3. Cyclic alternation pattern subtype classification in sleep electroencephalography 51 CHAPTER 5. APPLICATION OF MAMA-EMD ON P-WAVE EXTRACTION FOR DETECTION OF POTENTIAL ATRIAL FIBRILLATION PATIENTS 56 5.1 SIGNIFICANCE FOR AF DETECTION 56 5.2 RECENT WORKS RELATED TO AF DETECTION UNDER SINUS RHYTHM 56 5.3 METHOD FOR P-WAVE ANALYSES 57 5.3.1 Subject selection 57 5.3.2 ECG Signal processing 59 5.3.3 Feature extraction 63 5.3.4 Statistical analysis 66 5.4 STATISTICAL SIGNIFICANCE OF THE FEATURES 67 5.4.1 Morphology features 67 5.4.2 PCA related features 69 5.4.3 Inter-lead P-wave dispersion 69 5.5. CLASSIFICATION OF AF AND CONTROL PATIENTS 70 5.6 DISCUSSION AND IMPLICATION 72 CHAPTER 6. EXTENSION TO STEP FUNCTION 75 6.1 GENERALIZED ALGORITHM 75 6.2 EXAMPLE: PHOTOPLETHYSMOGRAM (PPG) RECORDING 76 CHAPTER 7. CONCLUSION 80 BIBLIOGRAPHY 82 | - |
| dc.language.iso | en | - |
| dc.subject | 訊號處理 | zh_TW |
| dc.subject | 經驗模態分解 | zh_TW |
| dc.subject | 心房顫動 | zh_TW |
| dc.subject | 可適性濾波 | zh_TW |
| dc.subject | adaptive filter | en |
| dc.subject | Empirical mode decomposition | en |
| dc.subject | signal processing | en |
| dc.subject | atrial fibrillation | en |
| dc.title | 經驗模態分解中突波問題的解決架構 | zh_TW |
| dc.title | A solution framework for spike problem in empirical mode decomposition | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 108-2 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.coadvisor | 羅孟宗 | zh_TW |
| dc.contributor.coadvisor | ; | en |
| dc.contributor.oralexamcommittee | 王淵弘;林亮宇;黃文良 | zh_TW |
| dc.contributor.oralexamcommittee | ;; | en |
| dc.subject.keyword | 經驗模態分解,訊號處理,心房顫動,可適性濾波, | zh_TW |
| dc.subject.keyword | Empirical mode decomposition,signal processing,atrial fibrillation,adaptive filter, | en |
| dc.relation.page | 89 | - |
| dc.identifier.doi | 10.6342/NTU202000887 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2020-05-29 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電信工程學研究所 | - |
| 顯示於系所單位： | 電信工程學研究所 | |
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