博碩士論文 101581008 詳細資訊




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姓名 葉建宏(Chien-hung Yeh)  查詢紙本館藏   畢業系所 電機工程學系
論文名稱 複雜系統跨頻率耦合方法
(On Cross-Frequency Coupling in Complex System)
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摘要(中) 在生物醫學基礎發展中,神經訊號彼此之間將會以何種方式進行運算及溝通,並建構生理網絡?此議題受關注的程度與日俱增。
在生理及生物系統中,有一種特別奇妙且十分具有挑戰性的現象,亦即系統的輸出在不同尺度下具有自我相似性,也稱作碎形現象。這些碎形特徵在健康個體中十分穩定,然而若是個體因為老化或是遭受病理情況等因素導致系統擾動,碎形特徵則會大幅改變或減少。這種現象也表明系統自適性和隱含在系統內的碎形調控機制有密切關連。為了解訊號的自我相關特徵,我們使用去勢波動分析作為分析方法。由於多模態的調變是睡眠時腦波的重要特質,而短時間的碎形特徵可及時反映心律變異的副交感調變。分析結果顯示腦電波及心律變異和睡眠階段密切相關,並且藉由經驗模態分解及去勢波動分析方法可萃取睡眠期間大腦皮層自主神經之重要特徵。另一方面,我們也使用動物模型探討生物鐘,並主要著眼於生物主要時鐘如何產生約略24小時節律的規則及影響。不限於固定尺度,結果顯示生物主要時鐘會在廣泛的尺度下影響活動記錄的表現。
除了探討訊號本身在時域中不同尺度間的結構特性,眾多研究顯示在複雜系統中存在跨尺度頻率耦合現象。此現象代表兩個相同或著不同的訊號在不同尺度上可能會相互影響。一個在生理上常見的問題是,訊號的非穩態特性會導致許多傳統的分析方法結果缺乏結果的可靠性。藉由發展適用於非線性及非穩態的分析方法,我們試著更深入的了解生理現象的動態機制以及參與耦合的頻率範圍。例如,在單擺測試中,使用電子量角器得出的指標對於敘述痙攣情況下的不正常肌肉活動需求往往不太足夠。為了定量表示肌電訊號和肢體擺動之間的關係,我們提出一個新的指標,此指標立基於相位振幅耦合分析,並且將中風偏癱患者擺動記錄和表面肌電訊號之間的作用方式納入考慮。此研究顯示相位振幅耦合分析指標可合理的定量表示中風偏癱患者痙攣嚴重程度,並僅需要用到少於三個擺動週期的原始擺動訊號。此外,為了增加臨床上痙攣量測的選擇方式,我們進一步證實Wii遙控器以其便利性及高性價比的優勢可作為痙攣量測可行之替代的方法。總結來說,本論文將使用非線性分析手段處理生物訊號,並建立臨床上可行之生醫指標。
摘要(英)

What role do neuronal oscillations play in shaping computation and communication in physiological networks? There is increasing interest to this question.
One of the most intriguing and challenging phenomena in physiological and biological systems is scale-invariant/fractal patterns in the fluctuations of system outputs. These fractal patterns are robust in healthy physiological systems but are significantly altered or reduced in perturbed systems associated with aging and pathological conditions, indicating important underlying fractal controls that provide system integrity and adaptability. To estimate correlations in the fluctuations, we will utilize a widely accepted analytical tool, namely, detrended fluctuation analysis (DFA). Since the multi-mode modulation is a key feature of sleep EEG, and the short-term fractal property reflects the sympathovagal modulation of heart rate variability (HRV). We show that the properties of electroencephalography (EEG) and HRV are strongly correlated with sleep status and are interesting in clinic diagnosis, and the dynamic properties of sleep EEG and HRV derived by empirical mode decomposition (EMD) and DFA represent important features for cortex and autonomic nervous system (ANS) activities during sleep. On the other side, we also devote to circadian study mainly using animal models. Circadian investigations have mainly focused on understanding the generation of ~24-hour oscillations of the circadian pacemaker (the master clock of the system). Instead of acting as a generator of oscillations at a fixed time scale, our studies reveal that the master clock influences motor activity controls over a wide range of time scales as well.
Apart from the discoveries of temporal structure from the oscillation itself, many recent studies of complex systems have found that cross-frequency coupling (CFC) exists; that is, interactions occur between rhythms at different frequencies that are either within the same signals or in different signals. A generic problem in physiology is nonstationarity in signals that make many conventional analyses unreliable. By approaching using a nonlinear and nonstationary method, we try to better understand the dynamic of physiological phenomena and to disclosure the frequency bands that involved in coupling. For example, parameters derived from the goniometer measures in Pendulum test are insufficient in describing of the function of abnormal muscle activity in the spasticity. To explore a quantitative evaluation of muscle activation-movement interaction, we propose a novel index based on phase amplitude coupling (PAC) analysis with the consideration of the relations between movement and surface electromyography (SEMG) activity for hemiplegic stroke patients. This study indicates the feasibility of using the novel indices based on PAC in the evaluation of the spasticity among the hemiplegic stroke patients with less than 3 swinging cycles. Besides, to increase the available measures in clinical use, we additionally provide evidences to show that the Wii remote may serve as a convenient and cost-efficient tool for the assessment of spasticity as well.
In summary, by utilizing the nonlinear approach, we analyze the biological signals and develop the effective biomarkers for clinical use in this dissertation.
關鍵字(中) ★ 耦合
★ 碎形
★ 去勢波動分析
★ 跨頻率
★ 多尺度
關鍵字(英) ★ coupling
★ fractal
★ DFA
★ cross frequency
★ multiscale
論文目次

摘要.........................................................................................................................................iii
ABSTRACT.............................................................................................................................iv
ACKNOWLEDGEMENTS.....................................................................................................vi
TABLE OF CONTENTS.......................................................................................................vii
LIST OF FIGURES..................................................................................................................x
LIST OF TABLES.................................................................................................................xvi
NOMENCLATURE.............................................................................................................xvii
Chapter I Introduction...........................................................................................................1
1.1 Motivation..............................................................................................................1
1.2 Objectives...............................................................................................................5
1.3 Dissertation Structure............................................................................................. 5
Chapter II Fractal Regulation...............................................................................................7
2.1 Detrended Fluctuation Analysis..............................................................................8
2.2 Sleep: Interaction between Heart Rate Variability and Sleep EEG………....……9
2.2.1 Material and Methods……………...……………………………...…..11
2.2.2 Results………………...…………...………..…………………………16
2.2.3 Discussion and Conclusions……...……….……...……………………23
2.3 Circadian……………………………………………..……….…………………25
2.3.1 Arctic Animal……………………………...…………………………. 25
2.3.2 Wild Monkey……...……….…………………..…….………………...31
Chapter III Nonlinear Phase-Amplitude Coupling Analysis............................................34
3.1 PAC Quantification.................................................................................................35
3.1.1 Cycle Frequency Computation...............................................................38
3.1.2 Cycle-Shuffled Surrogate Data.............................................................39
3.1.3 Frequency Comodulograms..................................................................40
3.1.4 Discussion and Summary......................................................................40
3.2 Spasticity Quantification……………………….………………………………...42
3.2.1 Material……...………...……………………………………..………..44
3.2.2 Methods…………...………………………………....………………..47
3.2.3 Results……………..…...……………...………………………………51
3.2.4 Discussion and Conclusions……...…….…......………………………55
Chapter IV Nonlinear Amplitude-Amplitude Coupling Analysis……………....…....59
4.1 Assessment of AAC..............................................................................................59
4.1.1. Spectral Cross-Frequency Comodulation Analysis (SCFCA) .............59
4.1.2. Intrinsic Mode Amplitude-Amplitude Coupling (IMAAC) ..................61
4.2 Validation and Comparison………………….…...……………..………..………64
4.2.1 Synthetic Signals………………………........………..………..………65
4.2.2 Results………...………………………….....………..………..………66
4.3 Discussions and Summary………………….……..…………….…………..........69
Chapter V Concluding Remarks, Incomplete Study and Future Works…................73
5.1 Conclusions............................................................................................................73
5.2 Incomplete study: Cross-Frequency Coupling in Movement Disorder….……....74
5.2.1 Seizure..………………………....…………………...……….………74
5.2.2 Parkinson Disease..…………………….…………....………..………90
5.3 Future Works…………………………….……………………………................92
Bibliographies....................................................................................................................94
Appendix..........................................................................................................................113
A. Simulated Results of EMD Method…...…………………………………….113
B. Validation of Wii Remote in Measuring Spasticity…….……….….…….....116
C. Publications during Ph.D. studies…………………………………….……...131
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指導教授 李柏磊、羅孟宗(Po-lei Lee Men-tzung Lo) 審核日期 2016-4-19
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