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姓名	王國偉(Kuo-Wei Wang)  查詢紙本館藏	畢業系所	電機工程學系
論文名稱	經驗模態分解法為基礎之醫學影像分析技術開發 (Development of medical image analysis techniques using empirical mode decomposition-base approaches)
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摘要(中)	近年來，以經驗模態分解為基礎的方法，例如經驗模態分解法( Empirical Mode Decomposition)、總體經驗模態分解法(Ensemble Empirical Mode Decomposition )、多變量經驗模態分解法(Multivariate Empirical Mode Decomposition)與互補總體經驗模態分解法(Complementary Ensemble Empirical Mode Decomposition)等常被應用在萃取醫學應用之非穩態的信號，如分析血壓、心電圖心跳速率變化、肺動脈高血壓、腦部波介面以及功能性磁振造影的血氧濃度相依信號等。經驗模態分解法可將信號分解成有限的本質模態函數(intrinsic mode functions, IMF)，以往研究顯示，經驗模態分解法是一種資料驅動的方法，並適用於萃取隨機訊號。但經驗模態分解法對於突然變化或間斷的信號會有模式混合的現象，導致萃取本質模態函數出現異常。然而總體經驗模態分解法(EEMD)處理訊號時，須經由大量重複測試雜訊添加的信號移位過程，以往的研究經驗，使用總體經驗模態分解法去除殘餘雜訊信號的過程非常耗時。 在此篇論文中，首先我們開發以互補總體經驗模態分解法(Complementary Ensemble Empirical Mode Decomposition)，分析多頻道腦電磁儀(MEG)的信號，萃取受試者聽覺穩態誘發磁場並分解成本質模態(IMFs)，經由與空間模板(Spatial Template)比對，匹配出與聽覺穩態磁場高度相關之本質模態，最後重組成去除雜訊後的穩態聽覺磁場。另外，實驗的第二部分，使用多變量模態分解法(Multivariate Empirical Mode Decomposition)，可將功能性磁振造影每一張影像上的血氧濃度水平依賴信號(Blood Oxygen Level Depend Signals) 分解成共同特徵的本質模態函數，計算本質模態與原始血液熱動力學(Hemodynamic response)之間的相關係數(correlation coefficient)，匹配出與嗅覺刺激血液熱動力學高度相關之本質模態函數，主要目地是重組這些被匹配出的本質模態函數，獲得去除雜訊或人工假影的影像。
摘要(英)	In recent years, Empirical mode decomposition (EMD)-based methods, i.e., EMD, ensemble empirical mode decomposition (EEMD), complementary ensemble empirical mode decomposition (CEEMD) and multivariate empirical mode decomposition(MEMD) have been used to extract nonstationary signals in many applications, such as analysis of blood pressure, detection of heart-rate variability in electrocardiogram (ECG) , pulmonary hypertension, brain computer interface, BOLD fMRI signals , and etc . The EMD approach decomposes a signal into a finite number of intrinsic mode functions (IMF) by iteratively conducting the sifting process, which has been demonstrated as a powerful data-driven tool for extracting meaningful stochastic signals. But EMD is also very sensitive to any unexpected changes in the signal like in case of missing signal components in certain time intervals, and can lead to mode mixing which impeded the interpretation of the extracted IMFS. The EEMD approached which repeatedly performs the shifting process on a noise-added signal for a mass of trials. However, the reduction of residual noise in EEMD is time-consuming which requires a large amount of trials for average. In this dissertation, firstly, we developed a complementary ensemble empirical mode decomposition (CEEMD)–based approach to extract steady-state auditory evoked fields (SSAEF) in multi-channel MEG data. The CEEMD utilizes noise assisted data analysis (NADA) approach by adding positive and negative noise to decompose MEG signals into intrinsic mode functions (IMF)，By correlating each IMFs with the multi-channel MEG data, the spatial distribution of each IMF can be obtained. Pertinent SSAEF-related IMFs were then chosen through a template matching process to reconstruct noise-suppressed SSAEFs. Secondly, we adopted multivariate empirical mode decomposition (MEMD) to extract olfactory-related features in fMRI BOLD signals. The MEMD enables common features of different scales in an image slice to be arranged in distinct IMFs, so that the task-related signals can be selected and reconstructed. And the noise and artifacts can be removed from reconstructed BOLD signals by deselecting task-unrelated components.
關鍵字(中)	★ 穩態聽覺誘發磁場 ★ 腦電磁儀 ★ 互補總體經驗模態分解法 ★ 多變量經驗模態分解法 ★ 功能性磁振造影	關鍵字(英)	★ Steady-state auditory evoked field ★ magneto-encephalography ★ complementary empirical mode decomposition ★ multivariate empirical mode decomposition ★ functional MRI
論文目次	TABLE OF CONTENTS 摘要 I Abstract III 誌謝 V TABLE OF CONTENTS VI LIST OF FIGURES VII LIST OF TABLES VIII Chapter 1 Introduction 1 1.1 Motivation and Background 1 1.2 Reviews of Previous Works 4 1.3 Organization and Main Task 7 Chapter 2 Material and Methods 10 2.1 SSAEF Stimulus Experiment 10 2.1.1 Auditory Stimulus Experiment 10 2.1.2 Subject and Task 10 2.1.3 MEG Recordings 11 2.1.4 Complementary Empirical Mode Decomposition(CEEMD) and Creation of Spatial Maps for Intrinsic Mode Functions 12 2.1.5 Selection of Pertinent IMFs using K‑means for Reconstruction of Noise Suppressed SSAEF 15 2.1.6 Creation of Right- and left-hemisphere Spatial Templates Based on Amplitude of N100m Peak in Auditory Evoked Fields (AEF) 16 2.1.7 Calculation of laterality index (LI) 18 2.2 Olfactory stimulus experiment 21 2.2.1 Subject and Olfactory Stimulations 21 2.2.2 Image Acquisition 22 2.2.3 Decomposition of Bold Signals Into IMFs Using MEMD 24 2.2.4 Selection of Olfactory-Related IMFs for Reconstructing Slice Images 28 2.2.5 Image Reprocessing Using SPM 29 Chapter 3 Experimental Result 33 3.1 Study Result of CEEMD-Base Approach for SSAEF 33 3.2 Study Result of MEMD-Base Approach for BOLD Signals 43 Chapter 4 Discussion 50 Chapter 5 Conclusion and future work 62 5.1 Conclusion 62 5.2 Future work 63 Bibliographies 65
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指導教授	李柏磊(Po-Lei Lee)	審核日期	2019-6-18
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