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系統識別號 U0002-2307202011521200
中文論文名稱 以物聯網技術建置路跑活動緊急救護系統
英文論文名稱 IoT technology applied to emergency medical system for marathon race
校院名稱 淡江大學
系所名稱(中) 資訊工程學系碩士在職專班
系所名稱(英) Department of Computer Science and Information Engineering
學年度 108
學期 2
出版年 109
研究生中文姓名 胡建毅
研究生英文姓名 Chien-Yi Hu
學號 707410139
學位類別 碩士
語文別 中文
第二語文別 英文
口試日期 2020-07-10
論文頁數 51頁
口試委員 指導教授-潘孟鉉
委員-曾學文
委員-鄭建富
委員-潘孟鉉
中文關鍵字 心率變異度  猝死  緊急救護 
英文關鍵字 Heart Rate Variability  Sudden Cardiac Death  Emergency Medical System 
學科別分類 學科別應用科學資訊工程
中文摘要 近年來,路跑運動的盛行,2019年有超過五百場的馬拉松,但每年都有跑者在路跑中發生猝死,而當發生猝死,四至六分鐘,腦細胞開始受損,超過十分鐘,會造成腦部無法復原的損傷及死亡。
在本研究中,我們利用穿戴式裝置監測跑者的心率變異度及定位,並由穿戴式裝置計算跑者風險,將風險值與定位回傳至雲端,當跑者為中風險,縮短回傳間隔時間,當跑者為高風險,通知附近救護人員注意該跑者狀況,當跑者發生心肺功能停止,立即派遣救護人員及救護車前往。
英文摘要 Marathon has become very popular in recent years ,there are more than 500 marathons in 2019. However Sudden Cardiac Death(SCD) was happened in marathons every years. The brain may sustain damage after blood flow has been stopped for about 4~6 minutes. There is irreversible damage to the brain after blood flow has stopped for 10 minutes. To be successful, CPR should be started as soon as possible.
In this paper, we designed and implemented an Emergency Medical Dispatch System (EMDS)for marathons .This system will monitor the runner's Heart rate variability (HRV) and location in real time with wearable devices, determining risk level by wearable devices. Moderate risk: keep monitoring and send a reminder to the dispatcher . High risk: EMDS will send a reminder to the nearest Emergency Medical Technician(EMT).When sudden cardiac death occurs, EMDS will dispatch the nearest EMT and ambulance.
論文目次 目錄
目錄 IV
圖目錄 VI
表目錄 VII
第一章 簡介 1
第二章 相關知識 4
2.1 ECG 心電圖 4
2.2 SCD 猝死 10
2.3 SCDI Sudden Cardiac Death Index 13
第三章 系統架構 15
3.1 感測資料處理 16
3.2 雲端資料串接 17
3.3 後台系統 18
第四章 系統實作 20
4.1 感測資料處理 20
4.2 資料傳輸串接 24
4.3 後台系統 25
第五章 模擬與實驗結果 27
5.1 實驗1 27
5.2 實驗2 28
第六章 結論與未來展望 29
參考文獻 32
附錄-英文論文 35

圖目錄
圖1: 心臟傳導圖 6
圖2: 心電圖電極位置 10
圖3: 系統架構圖 15
圖4: 離散小波轉換 23
圖5: AD8232,Pycom Pytrack,Pycom Sipy,Raspberry Pi Zero W/H 24
圖6: 感測裝置 24
圖7: 從訊息發送到後台資料庫更新時間分佈圖 28
圖8: SCDIndex 模擬結果圖 29

表目錄
表1: 近年來因跑步猝死案件 1
表2: 從訊息發送到後台資料庫更新時間統計表 28
表3:SCDI統計表 29

參考文獻 [1] S. C. Hu et al., ACLS 精華. 2016.
[2] M. S. Thaler, 心電圖學必備, 沃特庫爾 ed. 2014.
[3] I. Alikhani, K. Noponen, and T. Seppänen, "Contribution of body movements on the heart rate variability during high intensity running," in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017, pp. 3993-3996.
[4] W. Yaofeng, Y. Rong, and C. Yuquan, "Heart rate monitoring in dynamic movements from a wearable system," in 2008 5th International Summer School and Symposium on Medical Devices and Biosensors, 2008, pp. 272-275.
[5] R. Bailón, N. Garatachea, I. d. l. Iglesia, J. A. Casajús, and P. Laguna, "Influence of Running Stride Frequency in Heart Rate Variability Analysis During Treadmill Exercise Testing," IEEE Transactions on Biomedical Engineering, vol. 60, no. 7, pp. 1796-1805, 2013.
[6] D. Berwal, R. V. C, S. Dewan, V. J. C, and M. S. Baghini, "Motion Artifact Removal in Ambulatory ECG Signal for Heart Rate Variability Analysis," IEEE Sensors Journal, vol. 19, no. 24, pp. 12432-12442, 2019.
[7] R. O. Cummins, J. P. Ornato, W. H. Thies, and P. E. J. C. Pepe, "Improving survival from sudden cardiac arrest: the" chain of survival" concept. A statement for health professionals from the Advanced Cardiac Life Support Subcommittee and the Emergency Cardiac Care Committee, American Heart Association," vol. 83, no. 5, pp. 1832-1847, 1991.
[8] H. Fujita et al., "Sudden cardiac death (SCD) prediction based on nonlinear heart rate variability features and SCD index," vol. 43, pp. 510-519, 2016.
[9] F. Ghio et al., "Sudden cardiac arrest in a marathon runner. A case report," vol. 4, no. 2, p. 130, 2012.
[10] A. L. Goldberger et al., "PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals," vol. 101, no. 23, pp. e215-e220, 2000.
[11] M. Khazaei, K. Raeisi, A. Goshvarpour, and M. Ahmadzadeh, "Early detection of sudden cardiac death using nonlinear analysis of heart rate variability," Biocybernetics and Biomedical Engineering, vol. 38, no. 4, pp. 931-940, 2018/01/01/ 2018.
[12] B. J. I. s. Kosko, "Fuzzy entropy and conditioning," vol. 40, no. 2, pp. 165-174, 1986.
[13] H. Li, S. Kwong, L. Yang, D. Huang, and D. Xiao, "Hilbert-Huang Transform for Analysis of Heart Rate Variability in Cardiac Health," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. 6, pp. 1557-1567, 2011.
[14] A. R. Panchal et al., "2019 American Heart Association focused update on advanced cardiovascular life support: use of advanced airways, vasopressors, and extracorporeal cardiopulmonary resuscitation during cardiac arrest: an update to the American Heart Association guidelines for cardiopulmonary resuscitation and emergency cardiovascular care," vol. 140, no. 24, pp. e881-e894, 2019.
[15] S. Singh, "Pattern analysis of different ECG signal using Pan-Tompkin’s algorithm 1," 2010.
[16] G. A. Yadav, S. S. J. I. J. o. S. Patil, and Engineering, "Raspberry pi based ecg data acquisition system," no. 6, 2017.
[17] S. D. Greenwald, "The development and analysis of a ventricular fibrillation detector," Massachusetts Institute of Technology, 1986.
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