不同空氣污染程度下心率變異性線性與非線性分析

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研究生：	陳冠霖 Chen, Kuan-Lin
論文名稱：	不同空氣污染程度下心率變異性線性與非線性分析 Linear and Nonlinear Analysis of Heart Rate Variability under Different Levels of Air Pollution
指導教授：	馬席彬 Ma, Hsi-Pin
口試委員:	蔡佩芸 Tsai, Pei-Yun 黃慶昌 Huang, Ching-Chang
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 電機工程學系 Department of Electrical Engineering
論文出版年：	2025
畢業學年度：	113
語文別：	英文
論文頁數：	74
中文關鍵詞：	心率變異性、空氣汙染
相關次數：	點閱：71 下載：0
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本研究探討了空氣污染對心率變異性（HRV）的影響，HRV 是評估自主神經功能和心血管健康的重要生物標誌物。隨著空氣污染被越來越多地認識為一種顯著的環境壓力源，它對自主神經系統平衡的擾動可能對健康產生不利影響。本研究中，採用了時間序列聚類法，根據空氣污染物濃度將不同地區劃分為高污染區和低污染區。為了確保來自兩個區域的參與者之
間具有可比性，研究使用了傾向分數匹配技術，此方法考量了年齡、性別、既往病史等多種人口和健康因素，有助於盡量降低潛在的混淆變數，從而使得對空氣污染與 HRV 之間關係的檢視更加精確，儘管仍然可能存在一些未考慮到的因素影響結果。HRV 指標的量化分析結果顯示，在以 24 小時為基礎的分析中，高污染區的參與者與低污染區相比，其線性 HRV 指標未出現顯著差異；但在多尺度熵分析中，20 個時間尺度的熵值、以及與 MSE 相關的區域積分，均顯著降低（p < 0.05），進一步支持空氣污染對自主神經功能的潛在不利影響。研究結果表明，長期暴露於高濃度空氣污染可能會損害自主神經功能，從而導致心血管健康惡化。這些發現突顯了將空氣污染視為心血管疾病風險的重要環境因素，同時強調了深入研究空氣污染與心血管健康之間機制的必要性。

This study explores the impact of air pollution on heart rate variability (HRV), an important biomarker for assessing autonomic nervous system function and cardiovascular health. Air pollution has been increasingly recognized as a significant environmental stressor with the potential to disrupt autonomic regulation. In this research, time-series k-means clustering was employed to categorize different geographical regions into high-pollution and low-pollution areas based on air pollutant concentrations. To ensure comparability between participants from the
two areas, propensity score matching was applied, accounting for various demographic and health-related factors such as age, gender, and pre-existing medical conditions. This approach minimized potential confounding variables, providing a more precise evaluation of the relationship between air pollution and HRV, although some unaccounted factors may still influence the results. Analysis of HRV metrics showed no significant differences in linear HRV indices between highand low-pollution regions during 24-hour analyses. However, multiscale entropy (MSE) analysis revealed significantly reduced entropy values across 20 time scales
and MSE-related integral measures (p < 0.05), supporting the potential adverse impact of air pollution on autonomic function. The findings suggest that long-term exposure to elevated levels of air pollution may impair autonomic function, potentially leading to the deterioration of cardiovascular health. These results underscore the importance of considering air pollution as a critical environmental factor in cardiovascular disease risk and highlight the necessity for further research into the mechanisms linking air pollution and cardiovascular health.

摘要......................................................... i
誌謝......................................................... iii
Abstract.................................................... v

Introduction.......................................... 1
1 Background......................................... 1
2 Motivation......................................... 2
3 Contributions...................................... 3
4 Organization....................................... 4

Literature Review.................................... 5
1 Overview........................................... 5
2 Linear Analysis Review............................. 7
3 Nonlinear Analysis Review......................... 10
4 Current State of Knowledge........................ 13

Proposed Algorithm................................... 15
1 Overview.......................................... 15
2 Air Pollution Levels Clustering Model............. 17
2.1 Time Series Clustering Algorithm............... 19
2.2 Application to Air Pollution Data.............. 21
3 Propensity Score Matching......................... 21
3.1 Definition and Concept of Propensity Score..... 21
3.2 Principles of Matching......................... 22
3.3 Applications and Advantages.................... 23
3.4 Limitations and Considerations................. 24
4 ECG Data Preprocessing............................ 24
4.1 ECG Data Validation............................ 24
4.2 Handling Abnormal Signals...................... 25
5 Heart Rate Variability............................ 26
5.1 Linear Analysis................................ 26
5.2 Nonlinear Analysis............................. 28
6 Wilcoxon Rank-Sum Test............................ 32
6.1 Assumptions.................................... 32
6.2 Hypothesis..................................... 33
6.3 Test Procedure................................. 33
6.4 Applications and Interpretation................ 34

Results and Analyses.................................. 35
1 Air Pollution Data Handling and Pollution Level Clustering Results.................................................. 36
1.1 Air Pollution Data and Processing.............. 36
1.2 Summary of Air Pollutant Concentrations........ 38
1.3 Analysis of Air Quality Standards.............. 40
1.4 Clustering and Regional Comparisons............ 41
2 Dataset Description and Propensity Score Matching.................................................... 42
2.1 Dataset Description............................ 42
2.2 Propensity Score Matching in Clinical Trial Populations......................................... 44
3 HRV Analysis Results.............................. 46
3.1 Fixed 4-hour Daytime HRV Analysis.............. 46
3.2 Random 4-hour HRV Analysis within 24 Hours..... 52
3.3 Comparison Between Fixed and Random 4-hour HRV Analyses...................................... 60
3.4 Comprehensive 24-hour HRV Analysis............ 64

Conclusion and Future Works........................... 69
1 Conclusion........................................ 69
2 Future Work....................................... 70

References............................................. 71
                                

[1] World Health Organization, “Cardiovascular Diseases (CVDs).”https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1, 2024.
[2] Ministry of Health and Welfare, Taiwan, “107 年死因統計結果分析.”https://www.mohw.gov.tw/dl-70030-92c670b6-7335-4ff7-86b6-e51be93197e7.html, 2018.
[3] J. F. Thayer, S. S. Yamamoto, and J. F. Brosschot, “The relationship of autonomic imbalance, heart rate variability and cardiovascular disease risk factors,” International Journal of Cardiology, vol. 141, no. 2, pp. 122–131, 2010.
[4] Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology , “Heart rate variability,” Circulation, vol. 93, no. 5, pp. 1043–1065, 1996.
[5] H.-T. Hsu, “Analysis of the Interaction between Urban Air Pollution and the Cardiovascular System Based on Linear/Nonlinear Heart Rate Variability Indexes,” master’s thesis, National Tsing Hua University, Hsinchu, Taiwan, 2023.
[6] J. Schwartz, “Long-term effects of exposure to particulate air pollution,” Clinics in Occupational and Environmental Medicine, vol. 5, no. 4, pp. 837–848, 2006.
[7] F. Shaffer, Z. M. Meehan, and C. L. Zerr, “A critical review of ultra-short-term heart rate variability norms research,” Frontiers in neuroscience, vol. 14, p. 594880, 2020.
[8] G. R. Sandercock, P. D. Bromley, and D. A. Brodie, “The reliability of short-term measurements of heart rate variability,” International Journal of Cardiology, vol. 103, no. 3, pp. 238–247, 2005.
[9] K. N. Shields, “Traffic-related air pollution exposures and changes in heart rate variability in Mexico City: A panel study,” Environ Health, vol. 12, 2013.
[10] C.-C. Chan, “Association between nitrogen dioxide and heart rate variability in a susceptible population,” European Journal of Cardiovascular Prevention and Rehabilitation, vol. 12, no. 6, pp. 580–586, 2005.
[11] D. F. Dietrich, “Differences in Heart Rate Variability Associated with Long-Term Exposure to NO2, volume = 116,” Environmental Health Perspectives, no. 10, pp. 1357–1361, 2008.
[12] D. Meier-Girard, “Association of long-term exposure to traffic-related PM(10) with heart rate variability and heart rate dynamics in healthy subjects,” Environ. Int, vol. 125,
pp. 107–116, 2019.
[13] J. L. Barclay, B. G. Miller, S. Dick, M. Dennekamp, I. Ford, G. S. Hillis, J. G. Ayres, and
A. Seaton, “A panel study of air pollution in subjects with heart failure: negative results in
treated patients,” Occupational and Environmental Medicine, vol. 66, no. 5, pp. 325–334,
2009.
[14] X. Xing, F. Liu, X. Yang, Q. Liu, X. Wang, Z. Lin, K. Huang, J. Cao, J. Li, M. Fan,
X. Chen, C. Zhang, S. Chen, X. Lu, D. Gu, and J. Huang, “Declines in heart rate variability associated with short-term PM2.5 exposure were modified by blood pressure control
and treatment: A multi-city panel study in China,” Environmental Pollution, vol. 287,
p. 117572, 2021.
[15] T.-Y. Tsai, L.-W. Lo, S.-H. Liu, et al., “Delayed association of acute particulate matter 2.5
air pollution exposure with loss of complexity in cardiac rhythm dynamics: insight from
detrended fluctuation analysis,” Environmental Science and Pollution Research, vol. 28,
pp. 10931–10939, 2021.
[16] C.-S. Hung, C.-C. Huang, S.-C. Pan, H.-P. Ma, C.-C. Huang, Y.-L. L. Guo, and Y.-L. Ho,
“Acute particulate matter exposure is associated with disturbances in heart rate complexity in patients with prior myocardial infarction,” Science of The Total Environment, vol. 733, p. 138842, 2020.
[17] X. Huang, Y. Ye, L. Xiong, R. Y. Lau, N. Jiang, and S. Wang, “Time series k-means: A new k-means type smooth subspace clustering for time series data,” Information Sciences, vol. 367-368, pp. 1–13, 2016.
[18] P. R. ROSENBAUM and D. B. RUBIN, “The central role of the propensity score in observational studies for causal effects,” Biometrika, vol. 70, pp. 41–55, 04 1983.
[19] R. B. D. Jr, “Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group,” Statistics in Medicine, vol. 17, pp. 2265–2281, 1998.
[20] M. Costa, A. L. Goldberger, and C.-K. Peng, “Multiscale entropy analysis of complex physiologic time series,” Physical review letters, vol. 89, no. 6, p. 068102, 2002.
[21] Y.-L. Ho, C. Lin, Y.-H. Lin, and M.-T. Lo, “The Prognostic Value of Non-Linear Analysis of Heart Rate Variability in Patients with Congestive Heart Failure—A Pilot Study of Multiscale Entropy,” PLOS ONE, vol. 6, pp. 1–6, 04 2011.
[22] H. Liu, Z. Yang, F. Meng, L. Huang, W. Qu, H. Hao, J. Zhang, and L. Li, “Chronic vagus nerve stimulation reverses heart rhythm complexity in patients with drug-resistant epilepsy: An assessment with multiscale entropy analysis,” Epilepsy Behavior, vol. 83, pp. 168–174, 2018.
[23] C.-K. Peng, S. V. Buldyrev, S. Havlin, M. Simons, H. E. Stanley, and A. L. Goldberger, “Mosaic organization of DNA nucleotides,” Phys. Rev. E, vol. 49, pp. 1685–1689, Feb 1994.
[24] D. P. Francis, K. Willson, P. Georgiadou, R. Wensel, L. C. Davies, A. Coats, and M. Piepoli, “Physiological basis of fractal complexity properties of heart rate variability in man,” The Journal of Physiology, vol. 542, no. 2, pp. 619–629, 2002.
[25] K. Willson, D. P. Francis, R. Wensel, A. J. S. Coats, and K. H. Parker, “Relationship between detrended fluctuation analysis and spectral analysis of heart-rate variability,” Physiological Measurement, vol. 23, p. 385, mar 2002.
[26] H. B. Mann and D. R. Whitney, “On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other,” The Annals of Mathematical Statistics, vol. 18, no. 1, pp. 50 – 60, 1947.
[27] S.-C. C. Lung, N. Chen, J.-S. Hwang, S.-C. Hu, W.-C. V. Wang, T.-Y. J. Wen, and C.-H. Liu, “Panel study using novel sensing devices to assess associations of pm2. 5 with heart rate variability and exposure sources,” Journal of Exposure Science & Environmental Epidemiology, vol. 30, no. 6, pp. 937–948, 2020.
[28] R. R. Wilcox, Introduction to robust estimation and hypothesis testing. Academic press, 2011.

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