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研究生:黃雍文
研究生(外文):Yung-Wen Huang
論文名稱:緊急醫療資料庫之時間序列分析
論文名稱(外文):Time Sequence Analysis of EMS Database
指導教授:歐陽彥正歐陽彥正引用關係
口試委員:孫維仁楊孟翰
口試日期:2015-07-17
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:生醫電子與資訊學研究所
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:2015
畢業學年度:103
語文別:中文
論文頁數:71
中文關鍵詞:時間序列分析緊急醫療服務醫療資料庫週期模式
外文關鍵詞:Time series analysisEmergence medical servicesMedical databasePeriodical patterns
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「緊急醫療服務」(Emergency Medical Services, EMS)提供重大傷、病患由事故現場、直到到達醫院急診部門前的緊急救護服務。到院前實施的緊急處置能不僅能夠減少病患的死亡及失能,同時能降低其復健與急救的醫療成本。
為了提供有效的緊急醫療服務,在有限的資源內如何適度的配置是一個重要的議題。像某些類型的緊急醫療事件的發生具有週期性的發生高峰是可以預見的,而醫療資源也應對這些週期的配置。舉例來說,溺水與中暑的發生在夏季時達到發生率的高點,而中風則相反,好發於冬季。
本研究最大的目的是對緊急醫療資料庫中共32個求救原因進行全面性的分析,希望找出緊急醫療事件中顯著的週期性模式。研究方法上,採取了一個新穎的分析方法,傅立葉─高斯分解法,能對於輸入的時間序列進行週期性分析,並產生出高闡述性的圖像結果,並且使用傳統的統計分析來提供使用者可靠的統計指標。在研究結果顯示,週期性模式是廣泛的出現在不同類型的緊急醫療事件中。


Emergency Medical Services (EMS) provide medical cares to seriously injured or illed patients, while they are being transported from the incident sites to the hospitals. Out-of-hospital acute medical interventions aims not only to reduce the mortality rate and the degree of disability of patients but also to minimize patients’ rehabilitation and medical costs. One critical issue in providing effective EMS is the quantities of resources to be allocated. It is conceivable that some types of EMS incidents should have periodical patterns and the public health agents should allocate the EMS resources accordingly. For example, drowning and hyperthermia incidents should peak during summer seasons, while stoke incidents should peak during winter seasons. The primary objective of the study presented in this thesis is to conduct comprehensive time series analyses based on the records in an EMS database in order to identify significant periodical patterns of EMS incidents. In this study, a novel analysis method designed to provide the user with a highly interpretable picture of the periodical patterns in the input time series has been employed. The analyses were then followed by carrying out the conventional statistical tests to provide the user with solid statistical metrics. The results derived from this study reveal that periodical patterns are commonly present in many types of EMS incidents.

誌謝 i
中文摘要 ii
ABSTRACT iii
CONTENTS iv
LIST OF FIGURES viii
LIST OF TABLES ix
Chapter 1 緒論 1
1.1 研究背景 1
1.2 研究動機 1
1.3 研究結果 2
1.4 論文架構 2
Chapter 2 相關研究 3
2.1 時間序列的分析方法 3
2.1.1 傅立葉轉換(Fourier transform) 3
2.1.2 小波轉換(Wavelet transform) 4
2.1.3 STL 5
2.1.4 Hilbert-Huang transform 7
2.1.5 其他週期分析方法 9
Chapter 3 研究材料與資料特性 10
3.1 緊急醫療的起源 10
3.2 資料來源 10
3.3 資料前處理 12
3.3.1 移除空跑資料 12
3.3.2 時間序列的時間尺度標準化 13
3.4 資料數據統計 13
3.4.1 報案數量 13
3.4.2 空跑比例 14
3.4.3 男女比例 15
Chapter 4 分析方法 17
4.1 統計方法 18
4.1.1 Chi-squared test 18
4.1.2 Ljung-Box test 18
4.2 傅立葉-高斯分解法 19
4.2.1 第一部分─傅立葉分解 19
4.2.2 第二部分─高斯分解 20
4.3 個案分析 21
4.3.1 原始資料處理 21
4.3.2 傅立葉分解 23
4.3.3 高斯分解 27
Chapter 5 分析結果 31
5.1 統計分析 31
5.1.1 Chi-squared test 31
5.1.2 Ljung-Box test 31
5.2 有長期趨勢和年週期的求救原因 34
5.2.1 一氧化碳中毒 34
5.2.2 到院前心肺功能停止(創傷類) 35
5.2.3 到院前心肺功能停止(非創傷類) 35
5.2.4 呼吸問題(喘呼吸急促) 36
5.2.5 呼吸道問題(異物哽塞) 36
5.2.6 發燒 37
5.2.7 肢體外傷 38
5.2.8 頭部外傷 39
5.2.9 昏迷(意識不清) 39
5.2.10 噁心、嘔吐、腹瀉 40
5.2.11 墜落傷 41
5.2.12 生物咬螫傷 41
5.2.13 癲癇抽搐 42
5.2.14 穿刺傷 42
5.3 只有年週期的求救原因 43
5.3.1 溺水 43
5.3.2 毒藥物中毒 44
5.3.3 電擊傷 44
5.4 只有長期趨勢的求救原因 45
5.5 其他 47
Chapter 6 討論 49
6.1.1 統計分析結果討論 49
6.1.2 FGD分析結果討論 49
6.1.3 FGD與HHT的比較 52
6.1.4 傅立葉─高斯分解法 54
Chapter 7 結論 56
7.1 總結 56
7.2 未來研究方向 57
參考文獻 59
附錄 62
Rank-based adaptive mutation evolutionary algorithm 62
FGD與HHT 32個求救原因比較總表 64



[1] Bray, J.E., Straney, L.D.J., Barger, B., Finn, J.C., "Effect of public awareness campaigns on calls to ambulance across Australia," Stroke, pp. 1377-80, 5 2015.
[2] Arnt H.R., Willich S.N., Schreiber C., Bruggemann T., Stern R., Schultheiss H. P., “Diurnal, weekly and seasonal variation of sudden death. Population-based analysis of 24,061 consecutive cases.,” European Heart Journal, pp. 315-20, 2 2000.
[3] Brennan PJ, Greenberg G, Miall WE, Thompson SG, “Seasonal variation in arterial blood pressure.,” British Medical Journal, 10 1982.
[4] 陳品良, “一個新的時間序列分析法及其在大型醫學資料庫的應用,” 於 台灣大學資訊工程學研究所博士學位論文, 台北市, 2014.
[5] R. Sarikaya, “Fractional Fourier transform features for speech recognition,” 於 IEEE International Conference on Acoustics, Speech and Signal Processing, 2004.
[6] J. S. Lim, Two-dimensional signal and image processing, Englewood Cliffs, NJ: Prentice Hall, 1990.
[7] A. Grossmann, J. Morlet, “Decomposition of Hardy Functions into Square Integrable Wavelets of Constant Shape,” SIAM J. Math. Anal., pp. 723-36, 1984.
[8] A. Haar, “Zur Theorie der orthogonalen Funktionensysteme,” Mathematische Annalen, pp. 331-371, 1910.
[9] Rafael Navarro, Antonio Tabernero, “Gaussian wavelet transform: Two alternative fast implementations for images,” Multidimensional Systems and Signal Processing, pp. 421-436, 11 1991.
[10] I. Daubechies, Ten Lectures on Wavelets, SIAM, 1992, p. 194.
[11] Baker, Jack W., “Quantitative Classification of Near-Fault Ground Motions,” Bulletin of the Seismological Society of America, pp. 1486-1501, 10 2007.
[12] Robert B. Cleveland, William S. Cleveland, Jean E. McRae, and Irma Terpenning, “STL: A Seasonal-Trend Decomposition Procedure Based on Loess,” Journal of Official Statistics, pp. 3-73, 1 1990.
[13] William S. Cleveland, Susan J. Devlin, “Locally Weighted Regression: An Approach to Regression Analysis by Local,” Journal of the American Statistical Association, pp. 596-610, 9 1988.
[14] Huang, N. E.; Shen, Z.; Long, S. R.; Wu, M. C.; Shih, H. H.; Zheng, Q.; Yen, N. C.; Tung, C. C.; Liu, H. H., “The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Nonstationary Time Series Analysis,” Proceedings of the Royal Society of London. A 454, pp. 903-993, 1998.
[15] Toshihisa Tanaka, Danilo P. Mandic, “Complex Empirical Mode Decomposition,” IEEE SIGNAL PROCESSING LETTERS, pp. 101-4, 2 2007.
[16] Nai-Fu Chang,Tung-Chien Chen, Cheng-Yi Chiang, Liang-Gee Chen, “On-line empirical mode decomposition biomedical microprocessor for Hilbert Huang transform,” IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 420-3, 11 2011.
[17] Rodolfo T. Gonçalves, Guilherme R. Franzini, Guilherme F. Rosetti, André L. C. Fujarra, Kazuo Nishimoto, “Analysis Methodology for Vortex-Induced Motion (VIM) of a Monocolumn Platform Applying the Hilbert–Huang Transform Method,” Journal of Offshore Mechanics and Arctic Engineering, p. 011103, 10 2011.
[18] E. Wigner, “On the Quantum Correction For Thermodynamic Equilibrium,” Phys. Rev., p. 749, 6 1932.
[19] M. B. Priestley, “Evolutionary spectra and non-stationary processes,” Journal of the Royal Statistical Society. Series B., pp. 204-237, 1965.
[20] D.-Z. Hung, J.-F. Deng, C.-C. Yang 且 L.-Y. Jen, “The climate and the occurrence of carbon monoxide poisoning in Taiwan.,” Hum Exp Toxicol, pp. 493-5, 7 1994.
[21] Chang, D.T.-H.; Jung-Hsin Lin ; Chih-Hung Hsieh ; Yen-Jen Oyang, “On the Design of Optimization Algorithms for Prediction of Molecular Interactions,” 於 BIBE ''09. Ninth IEEE International Conference on, Taichung, 2009.


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