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姓名 李慧君(Hui-Chun Li) 查詢紙本館藏 畢業系所 資訊工程學系在職專班 論文名稱 ECG訊號品質評估與篩選
(ECG Signal Quality Evaluation and Screening)相關論文 檔案 [Endnote RIS 格式]
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摘要(中) 近年來，心電圖應用的領域範圍相當廣泛，但是對於接收數據後沒有建立起一套篩選標準，導致研究者需要先判斷心電圖訊號品質才能繼續後續分析。本研究為了改善以往只能使用肉眼判斷訊號品質，或自訂閥值之評估方法，故提出統計指標配合決策樹篩選。本研究實驗數據使用Physionet 生理訊號資料庫共 200 筆，以及感測器與模擬器收集之訊號共 238 筆，在本研究所獲得的準確率分別達95%、93.24%，而先前研究所使用的數據庫，透過本研究方法獲得的準確率為83%。本研究不受感測儀器以及受測者之個體差異，皆能保有良好的準確度，透過高準確度的訊號品質評估，可有效地減少心電圖訊號品質評估人員的負擔。 摘要(英) Electrocardiogram is likely to find very extensive use in many application areas in recent years, however, there is no standard electrocardiographic criteria in screening after collecting the ECG data, which leads researchers have to proceed quality estimation of the ECG signal before interpreting the analysis. For improving the traditional method of signal quality estimation by the naked eye or the improved threshold, are used as statistical indicators to identify with decision tree analysis screening. A total of 200 retrieved data through Physionet database, and a total of 238 collected signals by ECG sensors and simulators are analyzed in this study. A comparison between this study and previous studies’ database in the accuracy was 95% and 93.24%, and 83%, respectively. Therefore, this study result is not influenced by the devices and individual differences and both can maintain good accuracy. Through the high level of signal accuracy quality assessment, the burden for the ECG researchers can be efficiently reduced. 關鍵字(中) ★ 心電圖
★ 訊號品質
★ 決策樹關鍵字(英) ★ ECG
★ Signal Quality
★ Decision Tree論文目次 致謝.........................................................................................................................i
摘要........................................................................................................................ii
Abstract................................................................................................................. iii
目錄....................................................................................................................... iv
圖目錄................................................................................................................... vi
表目錄.................................................................................................................. vii
第一章 緒論....................................................................................................1
1.1. 研究背景與動機................................................................................1
1.2. 研究目的............................................................................................2
1.3. 論文架構............................................................................................3
第二章 文獻回顧............................................................................................4
2.1. 文獻方法............................................................................................4
2.2. 常用統計指標....................................................................................6
2.3. 常用評估指標與分類方法................................................................8
第三章 心電訊號評估和篩選......................................................................11
3.1. 數據預處理......................................................................................11
3.2. ECG 訊號特徵擷取.........................................................................14
3.3. 決策樹 ECG 訊號篩選....................................................................17
第四章 實驗..................................................................................................19
4.1. 實驗環境..........................................................................................19
4.2. 心電圖訊號資料庫..........................................................................21
4.3. 心電圖訊號品質評估實驗..............................................................25
4.4 決策樹實驗............................................................................................29
4.5. 評估實驗準確率..............................................................................30
4.6. 討論..................................................................................................34
第五章 結論與未來展望..............................................................................35
5.1. 結論..................................................................................................35
5.2. 未來方向..........................................................................................36
參考文獻..............................................................................................................37
附錄......................................................................................................................39參考文獻 [1] 許博鈞, "動態複雜度脈波傳導速率生醫指標臨床應用研究," 國立東華大學
電機工程研究所博士論文, 2013.
[2] 楊心忠, "基於智慧型手機之心血管訊號分析系統," 國立台灣海洋大學電機
工程研究所碩士論文, 2016.
[3] R. V. Andreao, B. Dorizzi, and J. Boudy, "ECG signal analysis through hidden
Markov models," IEEE Transactions on Biomedical engineering, vol. 53, no. 8, pp.
1541-1549, 2006.
[4] S. Huang, J. Li, P. Zhang, and W. Zhang, "Detection of mental fatigue state with
wearable ECG devices," International journal of medical informatics, vol. 119, pp.
39-46, 2018.
[5] H. Lee, J. Lee, and M. Shin, "Using Wearable ECG/PPG Sensors for Driver
Drowsiness Detection Based on Distinguishable Pattern of Recurrence Plots,"
Electronics, vol. 8, no. 2, p. 192, 2019.
[6] W. Zhu, X. Chen, Y. Wang, and L. Wang, "Arrhythmia Recognition and
Classification Using ECG Morphology and Segment Feature Analysis," IEEE/ACM
Trans Comput Biol Bioinform, vol. 16, no. 1, pp. 131-138, Jan-Feb 2019.
[7] H. Al-Libawy, A. Al-Ataby, W. Al-Nuaimy, and M. A. Al-Taee, "HRV-based operator
fatigue analysis and classification using wearable sensors," in 2016 13th
International Multi-Conference on Systems, Signals & Devices (SSD), 2016, pp.
268-273: IEEE.
[8] 楊 順 聰 , " 創 新 性 心 臟 診 斷 與 照 護 程 序 之 探 討 ,"
2010,http://www.etop.org.tw/index.php?c=adm11252&d=adm&i=161010.
[9] Z. Zhao and Y. Zhang, "SQI quality evaluation mechanism of single-lead ECG
signal based on simple heuristic fusion and fuzzy comprehensive evaluation,"
Frontiers in physiology, vol. 9, p. 727, 2018.
[10] T. I. Incorporated, "Understanding Lead-Off Detection in ECG," 2015.
[11] C. Liu, P. Li, L. Zhao, F. Liu, and R. Wang, "Real-time signal quality assessment for
ECGs collected using mobile phones," in 2011 Computing in Cardiology, 2011, pp.
357-360: IEEE.
[12] A. Němcová, R. Smíšek, L. Maršánová, L. Smital, and M. Vítek, "A Comparative
Analysis of Methods for Evaluation of ECG Signal Quality after Compression,"
BioMed research international, vol. 2018, 2018.38
[13] "心電圖," https://zh.wikipedia.org/wiki/%E5%BF%83%E7%94%B5%E5%9B%BE.
[14] V. Chudáček, L. Zach, J. Kužílek, J. Spilka, and L. Lhotská, "Simple scoring system
for ECG quality assessment on Android platform," in 2011 Computing in
Cardiology, 2011, pp. 449-451: IEEE.
[15] U. Satija, B. Ramkumar, and M. S. Manikandan, "A review of signal processing
techniques for electrocardiogram signal quality assessment," IEEE reviews in
biomedical engineering, vol. 11, pp. 36-52, 2018.
[16] A. Velayudhan and S. Peter, "Noise Analysis and Different Denoising Techniques
of ECG Signal-A Survey," IOSR Journal of Electronics and Communication
Engineering, vol. 3, no. Iii, pp. 641-644, 2015.
[17] J. R. Quinlan, "Induction of decision trees," Machine learning, vol. 1, no. 1, pp.
81-106, 1986.
[18] L. Breiman, Classification and regression trees. Routledge, 2017.
[19] Physionet, " https://physionet.org/cgi-bin/atm/ATM."
[20]鯨 揚 科 技 , "AECG100 "
https://www.whaleteq.com/tw/Products/Detail/31/AECG100#tag=proSpec.
[21] M. Integrated, "MAX30001 Evaluation System (MAXIM),"
https://www.maximintegrated.com/en/products/analog/data-converters/analog
-front-end-ics/MAX30001EVSYS.html.指導教授 陳慶瀚 審核日期 2019-7-23 推文 plurk
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