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系統識別號 U0026-1602201711445800
論文名稱(中文) 長時間生理指標之時頻域分析
論文名稱(英文) A Study on Long Term Physiological Index by Using Time-Frequency Analysis
校院名稱 成功大學
系所名稱(中) 數學系應用數學碩博士班
系所名稱(英) Department of Mathematics
學年度 105
學期 1
出版年 105
研究生(中文) 梁一心
研究生(英文) Yi-Hsin Liang
學號 L16021137
學位類別 碩士
語文別 英文
論文頁數 42頁
口試委員 指導教授-舒宇宸
口試委員-崔博翔
口試委員-黃執中
中文關鍵字 生理指標  心律  反射指標  血管硬度指標  希爾伯特-黃轉換  短時傅立葉轉換  synchrosqueezing wavelet transform 
英文關鍵字 physiological indices  heart rate  stiffness index  reflection index  Hilbert-Huang transform  short time Fourier transform  synchrosqueezing wavelet transform 
學科別分類
中文摘要 本論文透過長期監控光容積訊號所紀錄的八項生理指標: 心律(HR)、反射
指標(RI)、血管硬度指標(SI)、正常心跳間距之標準差(SDNN)、正常心跳間期
差值平方和的均方根(RMSSD)、極低頻功率(VLF)、低頻功率(LF)、高頻功率
(HF),分析疲勞程度及生理指標長時間的週期。在生理指標上,我觀察到低頻的呼吸訊號所產生的高頻泛音會影響心律的頻譜。為了讓訊號更穩定,所以採用了希爾伯特-黃轉換(Hilbert-Huang Transform) 中的經驗模態分解法(Empirical Mode Decomposition) 來去除呼吸的頻率,接下來利用兩項不同的時頻域分析方法: Short-Time Fourier Transform 及Synchrosqueezing Wavelet Transform 來找出心跳的頻率、SDNN、RMSSD。我紀錄了四個月中共323 筆的生理訊號透過Logistic Regression、Decision Tree、Support Vector Machine、Nearest Neighbors、Naive Bayers 等方式進行資料分析,做出個人疲勞預測模型,可以透過新的一筆資料判斷個人的疲勞指標。此外,藉由傅立葉分析,且透過HR、RI、SI、SDNN、RMSSD 的頻譜,我發現每7 天都會有一個循環,透過VLF、LF、HF 的頻譜,則是每28 天會出現一個週期。

關鍵字: 生理指標,心律,反射指標,血管硬度指標,希爾伯特-黃轉換,短時傅立葉轉換,synchrosqueezing wavelet transform
英文摘要 In this thesis, eight physiological indices: heart rate (HR), reflection index (RI),
stiffness index (SI), standard deviation of normal-to-normal interval (SDNN), root
mean square of successive differences (RMSSD), very low frequency (VLF), low frequency(LF), and high frequency (HF), are recorded for long term analysis. These indices are computed from photoplethysmograph signals and used to develop a model for the level of fatigue. By visualizing the signals, I observed that the modes of high frequencies are affected by the modes of low-frequencies which are related to breath. In order to make the signals more stable, I use the empirical mode decomposition (EMD) of Hilbert-Huang transform (HHT) to remove the modes of breathing. After that, I use two different time-frequency analysis methods, short-time Fourier transform (STFT) and synchrosqueezing wavelet transform (SST), to compute the HR, SDNN, and RMSSD. I have recorded physiological signals for 323 times in the four months, and then analyse these records by using different methods, logistic regression, decision tree, support vector machine, nearest neighbors, and naive Bayers. By logistic regression, I make a personal predictive model of fatigue so that I can judge whether I am tired or not. In addition, by Fourier analysis, I found that from the spectrum of HR, RI, SI, SDNN and RMSSD, they show a major cycle 7 days. Besides, from the spectrums of VLF, LF, and HF, a major cycle every 28 days can be found.



Keyword: physiological indices, heart rate, stiffness index, reflection index,
Hilbert-Huang transform, short time Fourier transform, synchrosqueezing
wavelet transform
論文目次 1 Introduction 1
2 Time-Frequency Analysis 7
2.1 Fourier Analysis 7
2.1.1 Short-time Fourier Transform (STFT) 9
2.1.2 Empirical Mode Decomposition 11
2.1.3 Synchrosqueezing Wavelet Transform 13
3 Methods for Classification 16
3.1 Physiological Indices (PI) 16
3.1.1 Physiological Indices: HR, RI and SI 17
3.1.2 Physiological Indices: SDNN and RMSSD 18
3.1.3 Physiological Indices: VLF, LF and HF 19
3.2 Methods for Classification 21
3.2.1 Decision Tree 21
3.2.2 k Nearest Neighborhood 23
3.2.3 Logistic Regression 24
3.2.4 Naive Bayes Classifier 24
3.2.5 Support Vector Machine 25
3.2.6 ROC Curve 26
3.2.7 Confusion Matrix 27
4 Numerical Results 29
4.1 HR and SDNN Computed by Different Method 29
4.2 Fatigue Classification 31
4.3 Long-Term Frequency Analysis 36
5 Conclusion 39
Bibliography 40
參考文獻 [1] World Health Organization. The top 10 causes of death. http://www.who.int/mediacentre/factsheets/fs310/en/index3.html.
[2] Ministry of Health and Welfare in Taiwan. 103 年國人死因統計結果. http://www.mohw.gov.tw/news/531349778.
[3] Valentín Fuster and Bridget B Kelly. Usefulness of heart rate variability as a predictor of sudden cardiac death in muscular dystrophies. Acta Myologica, 27:114–122, 2010.
[4] L. Politano, A. Palladino, G. Nigro, M. Scutifero, and V. & Cozza. Promoting cardiovascular health in the developing world: A critical challenge to achieve global health. National Academies Press (US), 2008.
[5] Chirinos and Julio A. et al. Arterial wave reflections and incident cardiovascular events and heart failure: Mesa (multiethnic study of atherosclerosis). Journal of the American College of Cardiology, 60(21):2170–2177, 2012.
[6] T. G. Farrell, Y. Bashir, T. Cripps, M. Malik, J. Poloniecki, E. D. Bennett, D. E. Ward, and A. J. Camm. Risk stratification for arrhythmic events in postinfarction patients based on heart rate variability, ambulatory electrocardiographic variables and the signal-averaged electrocardiogram. J Am Coll Cardiol, 18(3):687–697,1991.
[7] R. E. Kleiger, J. P. Miller, J. T. Bigger, Jr, and A. J. Moss. Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. Am J Cardiol, 59(4):256–262, 1987.
[8] S. Dash, K. H. Chon, S. Lu, and E. A. Raeder. Automatic real time detection of atrial fibrillation. Annals of Biomedical Engineering, 37(9):1701–1709, 2009.
[9] J T Bigger, J L Fleiss, R C Steinman, L M Rolnitzky, R E Kleiger, and J N Rottman. Frequency domain measures of heart period variability and mortality after myocardial infarction. Circulation, 85(1):164–171, 1992.
[10] Wan-Hua Lin, Dan Wu, Chunyue Li, Heye Zhang, and Yuan-Ting Zhang. Comparison of heart rate variability from ppg with that from ecg. In Yuan-Ting Zhang, editor, The International Conference on Health Informatics: ICHI 2013, Vilamoura, Portugal on 7-9 November, 2013, pages 213–215, Cham, 2014. Springer International Publishing.
[11] N. Selvaraj, A. Jaryal, J. Santhosh, K. K. Deepak, and S. Anand. Assessment of heart rate variability derived from finger-tip photoplethysmography as compared to electrocardiography. Journal of Medical Engineering & Technology, 32(6):479–484, 2008.
[12] Norden E. Huang and Samuel S. P. Shen. Hilbert-Huang Transform and Its Applications. World Scientific, 2005.
[13] Ingrid Daubechies, Jianfeng Lu, and Hau-Tieng Wu. Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. ELSEVIER, 30:243–261, 2011.
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