(3.215.183.251) 您好!臺灣時間:2021/04/22 10:46
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:蔡炘茹
研究生(外文):Tsai, Hsin-Ju
論文名稱:多尺度熵和相位關係用於探討心肺交互作用之研究
論文名稱(外文):Study on Cardiorespiratory Interactions Based on Multiscale Entropy and Phase Relationship
指導教授:羅佩禎羅佩禎引用關係
指導教授(外文):Lo, Pei-Chen
口試日期:2018-09-06
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:2018
畢業學年度:107
語文別:英文
論文頁數:88
中文關鍵詞:心電圖呼吸訊號心肺交互作用多尺度熵非線性相依性相位差
外文關鍵詞:electrocardiograph (ECG)respiratory signalcardiorespiratory interactionmultiscale entropy analysisnonlinear interdependence analysisphase difference
相關次數:
  • 被引用被引用:0
  • 點閱點閱:61
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
本研究利用多尺度熵(Multiscale entropy)、非線性相依性(Nonlinear Interdependence)和相位差分析等方法,探討在不同心理壓力狀態下的心肺交互作用。多尺度熵提供了不同狀態下R-R區間複雜性的資訊。非線性相依性分析主要涉及從心率和呼吸序列中重建相空間軌跡,以估計相似度指標(Similarity index)來分析兩信號對彼此的影響。而在相位差分析中,我們提出了一種更直接估計暫態相位差的方法,並分析相位差在不同相位範圍內發生的機率。
本論文中所分析的對象包括兩個群組,禪定組為8位具有禪坐經驗者、控制組則為25位健康受測者。根據多尺度熵的結果,大多數控制組受測者在專注力測試階段中複雜度較高,而在呼吸控制階段中複雜度則較低。儘管禪定組年齡(範圍:51 - 62歲)比控制組年齡(範圍:20 - 24歲)要大的多,但禪定組平均的複雜度與控制組在休息階段的複雜度相似,這表示通過禪定可以保存良好的心肺功能。在非線性相依性分析中,從呼吸控制階段中可以看出呼吸對心率的影響較大。此外,禪定組的整體平均值高於對照組,這也顯示禪定可以增強心肺間的非線性相依性。在心率和呼吸的相位差中,在禪定或呼吸階段時顯現更好的心肺同步。
This research is aimed to investigate the cardiorespiratory interaction of different mental-stress states by multiscale entropy analysis, nonlinear interdependence analysis and the phase difference analysis. Multiscale entropy provides the information of the complexity of R-R-interval at different mental-stress levels. Nonlinear interdependence analysis mainly involves the reconstruction of phase space trajectory from HR (heart-rate) and RP (respiratory) sequences to estimate the similarity index (SI) to analyze the influence of these two signals on each other. In phase difference analysis, we present a new, yet, more straightforward scheme to evaluate the instantaneous phase differences based on empirical strategies and analyze the probability of occurrence of the phase difference in different phase ranges.
This study involves 33 subject, 8 Zen-meditation practitioners (experimental group) and 25 healthy, ordinary control subjects without any meditation experience. According to the results of multiscale entropy, the most control subjects had higher entropy and complexity in CAT sessions, and had lower complexity in breathing-control (BC) session. Although Zen-meditation practitioners (age: 51-62) were much older than controls (age: 20-24), average entropy of Zen-meditation group is approximately the same as which of control group in Rest sessions. It indicates that the cardiorespiratory functioning may be well preserved via Zen-meditation practice. In nonlinear interdependence analysis, BC session induces stronger influence of RP on HR than the influence of HR on RP. Moreover, the group average of Zen-meditation group is higher than which of control group. The results provide the evidence that Zen meditation enhance the nonlinear interdependence between HR and RP. In phase difference of HR and RP, smaller phase difference between HR and RP reflecting higher cardiorespiratory synchronization appears either at the Zen meditation or in the breathing-control session.
摘要 i
Abstract ii
誌謝 iv
Content v
List of Figure vii
List of Table xi
Chapter 1 Introduction 1
1.1 Background and Motivation 1
1.2 Scope of this Thesis 5
Chapter 2 Theories and Methods 6
2.1 Introduction of ECG, EEG and respiratory signals 6
2.1.1 ECG 6
2.1.2 Chest and Abdominal Respiration 10
2.1.3 Autonomic Nervous System and Cardiovascular System Modulation 12
2.1.4 EEG 15
2.2 Multiscale Entropy 17
2.3 Nonlinear Interdependence Measure 20
2.3.1 Reconstruction of System Dynamics from observation 20
2.3.2 Estimation of Embedding Dimension 22
2.3.3 Estimation of Time Delay 23
2.3.4 Definition and Estimation 24
2.3.5 Nonlinear Interdependence Analysis 26
2.3.6 Asymmetric property 28
2.4 Phase difference analysis 28
Chapter 3 Experiment and Signal Analysis 31
3.1 Experimental Setup and Procedure for ECG and respiratory signal 31
3.1.1 Experimental protocol 32
3.1.2 Signal acquisition 34
3.1.1 Measurement of ECG 35
3.1.2 Measurement of respiratory signal 36
3.2 Experiment Setup and procedure for EEG 37
3.2.1 Experimental group 37
3.2.2 Signal acquisition 37
3.3 Multiscale Entropy Analysis 39
3.4 Nonlinear Interdependence Analysis 44
3.5 Phase difference 50
Chapter 4 Results 53
4.1 Result of Multiscale Entropy 53
4.1.1 Results for Control Group 53
4.1.2 Results for Zen-meditation Group 56
4.1.3 Comparison between Control Group and Zen-meditation Group 57
4.2 Results of Nonlinear Interdependence Analysis 58
4.2.1 Results for Control Group 58
4.2.2 Results for Zen-meditation Group 61
4.2.3 Comparison between Control Group and Zen-meditation Group 61
4.3 Results of HR-RP Phase Difference analysis 62
4.3.1 Results for Control Group 63
4.3.2 Results for Zen-meditation Group 68
4.3.3 Comparison between Control Group and Zen-meditation Group 69
4.4 Results of Phase Difference analysis for EEG 70
4.4.1 Results for Zen-meditation EEG 71
4.4.2 Results for resting EEG 73
Chapter 5 Conclusions and Discussion 76
5.1 Conclusions 76
5.2 Future work 78
Reference 79
Appendix A R peak and Respiratory Peak Detections 84
A.1 R Peak Detection 84
A.2 Respiratory Peak Detection 86
Appendix B Formal Zen Meditation Practice 88
[1] P. C. Lo and M. Huang, “Scientific Propositions for Brain Reformation by Mailun Chan Ding,” International Journal of Electrical and Electronics Engineering (IJEEE), IASET, ISSN (Print): 2278-9944; ISSN (Online): 2278-9952, vol. 4, issue 4, 2015.
[2] P. C. Lo and M. Huang, “Exploring Preventive Power of Ten-Mailuns Actuation in Chan Ding,” Int J Preventive Medicine Res 1(2), pp. 27-34, 2015.
[3] R. Sudsuang, V. Chentanez and K. Veluvan, “Effect of buddhist meditation on serum cortisol and total protein levels, blood pressure, pulse rate, lung volume and reaction time,” Physiology & Behavior, vol. 50, no. 3, pp. 543-548, 1991.
[4] C. R. K. MacLean, K. G. Walton, S. R. Wenneberg, D. K. Levitsky, J. P. Mandarino, R. Waziri, S. L. Hillis, and R. H. Schneider, “Effects of the transcendental meditation program on adaptive mechanisms: Changes in hormone levels and responses to stress after 4 months of practice,” Psychoneuroendocrinology, vol. 22, pp. 227-295, 1997.
[5] R. Davidson, J. Kabat-Zinn, J. Schumacher, M. Rosenkranz, D. Muller, S. Santorelli, F. Urbanowski, A. Harrington, K. Bonus, and J. Sheridan, “Alterations in brain and immune function produced by mindfulness meditation,” Psychosom Med., vol. 65, pp. 564-570, 2003.
[6] S. Lazar, C. Kerr, R. Wasserman, J. Greve, D. Gray, M. Treadway, M. McGarvey, B. Quinn, J. Dusek, H. Benson, S. Rauch, C. Moore, and B. Fischl, “Meditation experience is associated with increased cortical thickness,” Neuroreport, vol. 16, pp. 1893-1897, 2005.
[7] C. Y. Liu and P. C. Lo, “Investigation of spatial characteristics of meditation EEG using wavelet analysis and fuzzy classifier,” Proceedings of the fifth IASTED International Conference: biomedical engineering, pp. 91-96, 2007.
[8] H. Y. Huang and P. C. Lo, “EEG dynamics of experienced Zen meditation practitioners probed by complexity index and spectral measure,” Medical Engineering & Technology, vol. 33, pp. 314-321, 2009.
[9] C. Y. Liu, C. C. Wei, and P. C. Lo, “Variation Analysis of Sphygmogram to Assess Cardiovascular System under Meditation,” Evidence-Based Complementary and Alternative Medicine, vol. 6, pp. 107-122, 2009.
[10] P. C. Lo and S. D. Wu, “Effect of Zen Meditation Practice on Perceived Stress in College Students: a Survey Study,” Biomedical Engineering: Applications, Basis and Communications, vol. 19, pp. 409, 2007.
[11] S. Pincus, “Approximate entropy as a measure of system complexity.”, Proceedings of the National Academy of Sciences, vol. 88, no. 6, pp. 2297-2301, 1991.
[12] J. Richman and J. Moorman, “Physiological time-series analysis using approximate entropy and sample entropy,” American Journal of Physiology-Heart and Circulatory Physiology, vol. 278, no. 6, pp. H2039-H2049, 2000.
[13] ] O. Rosso, S. Blanco, J. Yordanova, V. Kolev, A. Figliola, M. Schürmann and E. Başar, “Wavelet entropy: a new tool for analysis of short duration brain electrical signals,” Journal of Neuroscience Methods, vol. 105, no. 1, pp. 65-75, 2001.
[14] C. Bandt and B. Pompe, “Permutation Entropy: A Natural Complexity Measure for Time Series,” Physical Review Letters, vol. 88, no. 17, 2002.
[15] S. Roberts, W. Penny and I. Rezek, “Temporal and spatial complexity measures for electroencephalogram based brain-computer interfacing,” Medical & Biological Engineering & Computing, vol. 37, no. 1, pp. 93-98, 1999.
[16] M. Costa, A. Goldberger and C. Peng, “Multiscale Entropy Analysis of Complex Physiologic Time Series,” Physical Review Letters, vol. 89, no. 6, 2002.
[17] M. Costa, A. Goldberger and C. Peng, “Multiscale entropy analysis of biological signals,” Physical Review E, vol. 71, no. 2, 2005.
[18] A. Humeau, G. Mahe, F. Chapeau-Blondeau, D. Rousseau and P. Abraham, “Multiscale Analysis of Microvascular Blood Flow: A Multiscale Entropy Study of Laser Doppler Flowmetry Time Series,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 10, pp. 2970-2973, 2011.
[19] A. Humeau-Heurtier, G. Mahe, S. Durand and P. Abraham, “Multiscale Entropy Study of Medical Laser Speckle Contrast Images,” IEEE Transactions on Biomedical Engineering, vol. 60, no. 3, pp. 872-879, 2013.
[20] S. Wu, P. Wu, C. Wu, J. Ding and C. Wang, “Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine,” Entropy, vol. 14, no. 8, pp. 1343-1356, 2012.
[21] H. Niu and J. Wang, “Quantifying complexity of financial short-term time series by composite multiscale entropy measure,” Communications in Nonlinear Science and Numerical Simulation, vol. 22, no. 1-3, pp. 375-382, 2015.
[22] A. Goldberger, C. Peng and L. Lipsitz, “What is physiologic complexity and how does it change with aging and disease?,” Neurobiology of Aging, vol. 23, no. 1, pp. 23-26, 2002.
[23] N. Rulkov, M. Sushchik, L. Tsimring and H. Abarbanel, “Generalized synchronization of chaos in directionally coupled chaotic systems,” Physical Review E, vol. 51, no. 2, pp. 980-994, 1995.
[24] M. Rosenblum, A. Pikovsky and J. Kurths, “Phase Synchronization of Chaotic Oscillators,” Physical Review Letters, vol. 76, no. 11, pp. 1804-1807, 1996.
[25] R. Quiroga, J. Arnhold and P. Grassberger, “Learning driver-response relationships from synchronization patterns,” Physical Review E, vol. 61, no. 5, pp. 5142-5148, 2000.
[26] J. Arnhold, P. Grassberger, K. Lehnertz and C. Elger, “A robust method for detecting interdependences: application to intracranially recorded EEG,” Physica D: Nonlinear Phenomena, vol. 134, no. 4, pp. 419-430, 1999.
[27] T. Kohonen, “The self-organizing map,” Proceedings of the IEEE, vol. 78, no. 9, pp. 1464-1480, 1990.
[28] J. Vesanto and E. Alhoniemi, “Clustering of the self-organizing map,” IEEE Transactions on Neural Networks, vol. 11, no. 3, pp. 586-600, 2000.
[29] C. Schäfer, M. Rosenblum, H. Abel and J. Kurths, “Synchronization in the human cardiorespiratory system,” Physical Review E, vol. 60, no. 1, pp. 857-870, 1999.
[30] C. Schäfer, M. G. Rosenblum, J. Kurths, and H.-H. Abel, “Heartbeat synchronised with respiration,” Nature, Vol. 392(6673), pp. 239-241, March 1998.
[31] M. G. Rosenblum, A. S. Pikovsky, and J. Kurths, “Phase Synchronization of Chaotic Oscillators,” Physical Review Letters, vol. 76, pp. 1804, 1996.
[32] C. J. Brouse, G. A. Dumont, D. Myers, E. Cooke, J. Stinson, J. Lim, J. M. Ansermino, and R. Barbieri, “Real-time cardiorespiratory coherence is blind to changes in respiration during general anesthesia,” in Proc. of International IEEE Conference on Engineering in Medicine and Biology Society, pp. 5360-5364, 2013.
[33] I. Takahashi, T. Takaishi and K. Yokoyama, “Overcoming Drowsiness by Inducing Cardiorespiratory Phase Synchronization,” IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 3, pp. 982-991, 2014.
[34] M. Kabir, H. Dimitri, P. Sanders, R. Antic, E. Nalivaiko, D. Abbott and M. Baumert, “Cardiorespiratory Phase-Coupling Is Reduced in Patients with Obstructive Sleep Apnea,” PLoS ONE, vol. 5, no. 5, pp. e10602, 2010.
[35] N. Goldschlager and M. Goldman, “Principles of clinical electrocardiography.,” Norwalk, Conn.: Appleton & Lange, 1989.
[36] F. Censi, G. Calcagnini, S. Lino, S. Seydnejad, R. Kitney and S. Cerutti, “Transient phase locking patterns among respiration, heart rate and blood pressure during cardiorespiratory synchronisation in humans,” Medical & Biological Engineering & Computing, vol. 38, no. 4, pp. 416-426, 2000.
[37] G. M. Shepherd, Neurobiology, 2nd ed. New York: Oxford University Press, 1988.
[38] P. Lo and C. Chang, “Spatially Nonlinear Interdependence of Alpha-Oscillatory Neural Networks under Chan Meditation,” Evidence-Based Complementary and Alternative Medicine, vol. 2013, pp. 1-12, 2013.
[39] P. C. Lo and J. C. Chen, “Prominent Gamma in Deep Zen meditation EEG – Brain-basis Hypothesis,” 1st Global Conference on Biomedical Engineering (GCBME 2014) and 9th Asian Pacific Conference on Medical and Biological Engineering (APCMBE 2014), Oct. 9-12, 2014, Taiwan.
[40] H. Huang and P. Lo, “EEG dynamics of experienced Zen meditation practitioners probed by complexity index and spectral measure,” Journal of Medical Engineering & Technology, vol. 33, no. 4, pp. 314-321, 2009.
[41] J. Vesanto and E. Alhoniemi, “Clustering of the self-organizing map,” IEEE Transactions on Neural Networks, vol. 11, no. 3, pp. 586-600, 2000.
[42] D. Lehmann, P. Faber, S. Tei, R. Pascual-Marqui, P. Milz and K. Kochi, “Reduced functional connectivity between cortical sources in five meditation traditions detected with lagged coherence using EEG tomography,” NeuroImage, vol. 60, no. 2, pp. 1574-1586, 2012.
[43] S. Forman, J. Cohen, M. Fitzgerald, W. Eddy, M. Mintun and D. Noll, “Improved Assessment of Significant Activation in Functional Magnetic Resonance Imaging (fMRI): Use of a Cluster-Size Threshold,” Magnetic Resonance in Medicine, vol. 33, no. 5, pp. 636-647, 1995.
[44] M. Hämäläinen, R. Hari, R. Ilmoniemi, J. Knuutila and O. Lounasmaa, “Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain,” Reviews of Modern Physics, vol. 65, no. 2, pp. 413-497, 1993.
[45] C. Mathis, B. Bacskai, S. Kajdasz, M. McLellan, M. Frosch, B. Hyman, D. Holt, Y. Wang, G. Huang, M. Debnath and W. Klunk, “A lipophilic thioflavin-T derivative for positron emission tomography (PET) imaging of amyloid in brain,” Bioorganic & Medicinal Chemistry Letters, vol. 12, no. 3, pp. 295-298, 2002.
[46] B. Harvald and J. Marquardsen, “CORRELATION BETWEEN EEG AND AUTOPSY FINDINGS IN 59 PATIENTS WITH APOPLEXY,” Acta Psychiatrica Scandinavica, vol. 36, no. 150, pp. 135-137, 1961.
[47] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning internal representations by error propagation Parallel Distributed Processing,” California Univ San Diego La Jolla Inst for Cognitive Science, 1985.
[48] J. Richman and J. Moorman, “Physiological time-series analysis using approximate entropy and sample entropy,” American Journal of Physiology-Heart and Circulatory Physiology, vol. 278, no. 6, pp. H2039-H2049, 2000.
[49] F. Takens, “Detecting strange attractors in turbulence,” in Dynamical Systems and Turbulence, Warwick 1980. vol. 898, D. Rand and L.-S. Young, Eds., ed: Springer Berlin Heidelberg, 1981, pp. 366-381.
[50] J. Arnhold, P. Grassberger, K. Lehnertz, and C. E. Elger, “A robust method for detecting interdependences: application to intracranially recorded EEG,” Physica D-Nonlinear Phenomena, vol. 134, no. 4, pp. 419-430, 1999.
[51] M. Rey and P. Guillemant, “Apport des mathématiques non-linéaires (théorie du chaos) à l'analyse de l'EEG,” Neurophysiologie Clinique/Clinical Neurophysiology, vol. 27, pp. 406-428, 1997.
[52] P. Grassberger and I. Procaccia, “Characterization of Strange Attractors,” Physical Review Letters, vol. 50, no. 5, pp. 346-349, 1983.
[53] L. Cao, “Practical method for determining the minimum embedding dimension of a scalar time series,” Physica D: Nonlinear Phenomena, vol. 110, no. 1-2, pp. 43-50, 1997.
[54] A. M. Fraser and H. L. Swinney, “Independent coordinates for strange attractors from mutual information,” Physical Review A, vol. 33, no. 2, pp. 1134-1140, 1986.
[55] A. M. Fraser, “Reconstructing attractors from scalar time series: A comparison of singular system and redundancy criteria,” Physica D: Nonlinear Phenomena, vol. 34, no. 3, pp. 391-404, 1989.
[56] W. Liebert and H. G. Schuster, “Proper choice of the time delay for the analysis of chaotic time series,” Physics Letters A, vol. 142, no. 2-3, pp. 107-111, 1989.
[57] H. Kantz, Nonlinear time series analysis vol. 7: Cambridge university press 2003.
[58] R. Quiroga, J. Arnhold, and P. Grassberger, “Learning driver-response relationships from synchronization patterns,” Physical Review E, vol. 61, no. 5, pp. 5142-5148, 2000.
[59] R. Quian Quiroga, A. Kraskov, T. Kreuz, and P. Grassberger, “Performance of different synchronization measures in real data: A case study on electroencephalographic signals,” Physical Review E, vol. 65, no. 4, 2002.
[60] R. M. Pigache, “Comparison of scoring methods for tests of attention, including an error index for use with schizophrenic patients,” Perceptual & Motor Skills, vol. 42, pp. 243-253, 1976.
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔