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研究生:劉寯
研究生(外文):Chun, Liu
論文名稱:急性心肌梗塞非侵入式皮膚交感神經之聚集放電現象探討
論文名稱(外文):Cluster phenomenon of non-invasive skin sympathetic nerve activity in acute myocardial infarction patients
指導教授:林顯豐
指導教授(外文):Lin, Shien-Fong
口試日期:2021-01-18
學位類別:碩士
校院名稱:國立交通大學
系所名稱:生醫工程研究所
學門:工程學門
學類:生醫工程學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:英文
論文頁數:46
中文關鍵詞:急性心肌梗塞neuECG自律神經系統交感神經活動聚集現象
外文關鍵詞:Acute Myocardial InfarctionneuECGAutonomic Nervous SystemClustering Nerve Activity
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背景:急性心肌梗塞(Acute Myocardial Infarction, AMI)已知會影響自律神經的功能,我們假設可以在非侵入式皮膚交感神經活動 (Skin Sympathetic Nerve Activity, SKNA)上發現因急性心肌梗塞病患的失衡自律神經系統而產生的交感神經聚集放電現象。
方法:本研究中包含兩部分實驗,分別為AMI病患數據分析以及動物大鼠實驗,人體實驗包含20位無AMI以及20位AMI病患,使用寬頻的生理訊號(neuECG)量測儀連續紀錄皮膚交感神經訊號,SKNA訊號經由訊號處理以描繪出高頻訊號的包絡(eSKNA),通過標記神經聚集現象,受測者分為無AMI(n=20)、無聚集出現之AMI (AMINCA, n=11)以及聚集現象之AMI (AMICA)三類。動物大鼠實驗包含了8隻8到10周的成鼠,以注射藥物(Propranolol, 2.5mg/kg)方式,人工誘使自律神經失衡,並觀察其是否出現皮膚交感神經聚集放電現象。
結果:以40位為樣本,平均eSKNA分別與心律變異分析(HRV)中的高頻(rho=-0.372)和低頻(rho=-0.336)有統計上的顯著相關性,交叉比較的結果表明,eSKNA是評估20位AMI患者有效的替代指標。高低頻比(LF/HF ratio)的比較中,AMINCA組別顯著高於AMICA組別。在大鼠實驗中,成功誘使心律下降,但並未發現類似於人體實驗中的神經聚集現象。
結論:這是第一次在AMI病患非侵入式皮膚交感神經上發現聚集放電現象的研究,根據AMI數據分析與動物實驗結果,我們認為交感神經聚集現象是獨特的放電模式,卻不單因自律神經失衡這個一變數所造成,此現象應為如心肌梗塞等心臟相關疾病,造成心肌放電異常伴隨自律神經失衡所產生的複雜表現,藉由辨別聚集放電現象有潛力成為創新的生物標記,去評斷AMI病患的自律神經調節狀態。
Background: The balance of autonomic nervous system (ANS) is affected by cardiac disease such as acute myocardial infarction. We hypothesize that alteration of ANS balance in AMI patients could induce the synchronous neuronal discharge (cluster phenomenon) in non-invasive skin sympathetic nerve activity (SKNA).
Methods: AMI patients (n=20) and non-AMI controls (n=20) were participated in the study. The wide-band bioelectrical signals (neuECG) were recorded non-invasively for 5 minutes. SKNA were depicted as the envelope of SKNA (eSKNA) through signal optimization. By identification of cluster phenomenon, the participants were assorted into non-AMI (n=20), no cluster appearing (AMINCA, n=11), and cluster appearing (AMICA, n=9) groups. Sprague-Dawley male rats (n=8) were obtained in the animal experiment, which aged 8 to 10 weeks. By injecting propranolol (2.5mg/kg), induced ANS imbalance. We observed whether the SKNA cluster phenomenon also occurred in rats.
Results: The results validated eSKNA is an effective biomarker to evaluate ANS in AMI patients. The frequency of cluster phenomenon is 0.01-0.03 Hz and the amplitude is around 3 µV. The LF/HF ratio of AMINCA revealed significantly higher than AMICA. The heart rate successfully decreased but similar SKNA cluster phenomenon did not discover in the animal experiment.
Conclusion: This is the first evidence that reports the SKNA cluster phenomenon in AMI patients. This unique synchronous nerve discharge pattern could be detected with the non-invasive recording of SKNA after signal optimization. We conclude SKNA cluster phenomenon is a unique time-domain pattern of ANS synchronous discharge. However, it is not a protection mechanism when ANS balance is altered. It might be a complex performance between ANS coordination and cardiac disease involvement such as AMI. SKNA cluster phenomenon could be an innovative biomarker to estimate ANS regulation ability in AMI patients.
摘要 II
Abstract III
致謝 IV
Contents V
List of Figures VII
List of Tables VIII
Chapter 1. Introduction 1
1.1 Autonomic Nervous System 1
1.2 Sympathetic Nervous System Structure 2
1.2 Microneurography 3
1.3 Skin Sympathetic Nerve Activity 4
1.4 Acute Myocardial Infarction 4
1.5 Autonomic regulation parameters 5
1.6 Objective 6
Chapter 2. Methods 8
2.1 Experimental design 8
2.1.1 AMI patients’ SKNA analysis 9
2.1.2 Animals experiment 12
2.2 Signal processing 14
2.2.1 Signal partition 15
2.2.2 Signal rectification 15
2.2.3 Signal smoothing - Moving average 16
2.2.4 Signal smoothing - Root mean square 17
2.2.5 Binary Time series 19
2.2.6 Intensity graph 20
2.2.7 SKNA cluster definition 21
2.3 GUI of NerveAct 23
2.4 HRV analysis 24
2.5 Statistics analysis 24
Chapter 3. Results 25
3.1 Correlation of eSKNA and HRV analysis 25
3.2 Average eSKNA and HRV analysis in AMINCA and AMICA groups 32
3.3 Animal models results 33
Chapter 4. Discussion 36
Chapter 5. Conclusion 40
Chapter 6. Acknowledgement 41
References 42
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