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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 闕志達 | |
| dc.contributor.author | Cheng-Ying Wu | en |
| dc.contributor.author | 吳政穎 | zh_TW |
| dc.date.accessioned | 2021-06-16T08:36:28Z | - |
| dc.date.available | 2016-11-13 | |
| dc.date.copyright | 2013-11-13 | |
| dc.date.issued | 2013 | |
| dc.date.submitted | 2013-11-05 | |
| dc.identifier.citation | [1] 衛生福利部國民健康署(2013/10月)
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Guttag, “Computationally Generated Cardiac Biomarkers for Risk Stratification after Acute Coronary Syndrome,” Science Translational Medicine, vol. 3, Sep. 2011. [37] WIKIPEDIA Artificial neural network. (2013/10月) http://en.wikipedia.org/wiki/Artificial_neural_network [38] Ruslan Salakhutdinov, Geoffrey Hinton, “An Efficient Learning Procedure for Deep Boltzmann Machines”, Neural Computation 24, 1967–2006 (2012) c2012 Massachusetts Institute of Technology. [39] Geoffrey Hinton, “A Practical Guide to Training Restricted Boltzmann Machines”, August 2, 2010 [40] WIKIPEDIA sensitivity and specificity. (2013/10月) http://en.wikipedia.org/wiki/Sensitivity_and_specificity [41] MIT-BIH Arrhythmia Database [Online], Available: (2013/10月) http://www.physionet.org/physiobank/database/mitdb/ [42] NASA Goddard Institute for Space Studies (GISS) [Online], Available: (2013/10月) http://data.giss.nasa.gov/gistemp/ [43] S. Geisser, Predictive Inference, New York: Chapman and Hall, 1993. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/58881 | - |
| dc.description.abstract | 急性冠心症肇因於心臟肌肉缺血或缺氧，是長久以來位居我國十大死因前三的重大疾病。判斷急性冠心症病患預後危險程度的系統是非常重要的研究議題。傳統上，TIMI危險分數採用了病患的病史作為判斷之依據，是目前較廣被接受的作法。近年來，由於心電圖之方便性、快速性以及非侵入性，相當多以心電圖分析系統做為急性冠心症病患預後危險評估的研究出現。
本論文提出一套對急性冠心症病患預後危險評估之心電圖分析系統，蒐集並且使用某醫院所提供的病患資料來設計及驗證本論文所提出的系統。這個系統的心電圖分析系統架構包含資料蒐集、前處理、特徵擷取、以及最後使用人工智慧判斷病患之預後狀況，於系統中每一步驟本論文均有提出改進效能之方法。 對病患危險程度分類而言，傳統醫學上的研究通常使用迴歸分析，而本論文提出使用類神經網路設計較為複雜的系統來對急性冠心症病患預後做危險評估分析，利用人工智慧協助急性冠心症預後病患之危險評估也能達到比人為判斷或用統計模型所得出之預後效果還要好的危險程度判斷效果。傳統上由於使用人為判斷或多變數迴歸分析的方式做預後判斷，特徵必須足夠精確才會有顯著之危險分層效果；使用人工智慧的好處在於，即使特徵是由複數個生理因素所影響，在取用足夠多個特徵的狀況下機器仍能自動計算出特徵內的複數個生理因素，並做出危險判斷。換言之，傳統之分析方法需人工去蕪存菁，而人工智慧之方法則是機器自動做計算。本論文之實驗證實本系統所使用之簡單特徵搭配類神經網路可以得到極佳之危險評估效能。本論文所提出的對急性冠心症病患預後危險評估之心電圖分析系統，準確率可達八成以上。 | zh_TW |
| dc.description.abstract | Acute coronary syndrome (ACS), caused by rupture of an atherosclerotic plaque and partial or complete thrombosis of the infarct-related artery, has been on the first three places of the top ten causes of death in Taiwan for many years. Determine the prognosis of patients with acute coronary syndrome risk level of the system is a very important research topic. Traditionally, TIMI risk score using a patient's medical history as a basis for judgment, is the wide acceptance of the practice. In recent years, because the ECG is convenience, rapid and non-invasive, there are many studies about ECG analysis system as a prognosis of patients with acute coronary syndrome risk stratification.
A novel ECG analysis system for post-acute-coronary-syndrome (post-ACS) patients is proposed and applied to clinical data collected from a hospital. The ECG analysis system involves pre-processing, beat detection, feature extraction, and using machine learning to make prediction, is proposed. Each part in this system is enhanced by new algorithms or techniques. Instead of using regression analysis used in traditional biomedical researches, we use artificial neural network to build a more sophisticated model to classify the patients. Machine learning like artificial neural network can find the information hidden in the features, even if the used features are affected by multiple medical factors, while users have to use much more sophisticated features when they make prognosis by inspection or multivariate statistical analysis. The system we proposed gives the accuracy over 80% for stratification using only eight ECG features. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T08:36:28Z (GMT). No. of bitstreams: 1 ntu-102-R00943016-1.pdf: 3566044 bytes, checksum: 3c25e9c49e342d6c27dd851881a90e2f (MD5) Previous issue date: 2013 | en |
| dc.description.tableofcontents | 口試委員會審定書………..…………………………………………….……………..I
誌謝…………………...…..……………………………………………..………...…III 摘要……………………...……………………………...…………………..….………V ABSTRACT…………..……………………………………………….…….………VII 圖目錄 XI 表目錄 XIV 1. 第一章 緒論 1 1.1 研究動機 1 1.2 心電圖分析系統介紹 3 1.2.1 心電圖介紹 3 1.2.2 心電圖分析系統架構 4 1.3 論文組織 4 2. 第二章 心電圖之前處理 5 2.1 心電圖雜訊 5 2.2 心電圖資料 7 2.2.1 Cohort-A 7 2.2.2 Cohort-B 7 2.2.3 MIMIC II 資料庫 8 2.3 去除心電圖雜訊的方法 9 2.3.1 文獻回顧 9 2.3.2 以集成經驗模態分解法去除心電圖雜訊 9 2.4 逐次降頻分段式集成經驗模態分解法 12 2.4.1 經驗模態分解法及集成經驗模態分解法 12 2.4.2 逐次降頻分段式集成經驗模態分解法 18 2.5 心跳偵測 23 2.5.1 Pan-Tompkins演算法 23 2.5.2 Pan-Tompkins演算法問題 25 2.5.3 改進Pan-Tompkins演算法 27 3. 第三章 心電圖特徵 29 3.1 文獻回顧 29 3.1.1 心跳間期相關的心電圖特徵 30 3.1.1.1 心跳變異度 31 3.1.1.2 心跳間期的自相關函數 33 3.1.1.3 心跳的減速能力 34 3.1.1.4 多尺度熵分析 37 3.1.2 形狀變化的心電圖特徵 42 3.1.2.1 T波變化 42 3.1.2.2 形狀變異度 43 3.2 本系統所使用之心電圖特徵 44 4. 第四章 機器學習法 46 4.1 K-最近鄰居演算法 46 4.2 支持向量機 46 4.3 類神經網路 47 4.3.1 倒傳遞類神經網路 47 4.3.2 深層學習類神經網路 52 4.3.2.1 架構及演算法 52 4.3.2.2 延伸型限制性波茲曼機演算法 54 5. 第五章 心電圖分析之系統 56 5.1 心電圖分析系統架構 56 5.1.1 心電訊號正規化 57 5.1.2 去除VPC及APC影響 59 5.1.3 去除RR間期的趨勢 61 5.1.4 去掉異常資料 62 5.1.5 將心電訊號特徵正規化 64 5.2 各個方法對心電圖分析系統表現的影響 65 6. 第六章 系統表現與臨床實驗結果 69 6.1 心電圖分析系統用於ACS後病患危險評估之結果 69 6.2 與GRACE SCORE比較危險分層能力 79 6.3 BPNN與DNN實驗結果比較 81 6.4 使用MIMIC II資料庫驗證本論文所提出的心電圖分析系統 83 7. 第七章 結論與展望 86 參考文獻 88 | |
| dc.language.iso | zh-TW | |
| dc.subject | 類神經網路 | zh_TW |
| dc.subject | 心電圖 | zh_TW |
| dc.subject | 急性冠心症預後 | zh_TW |
| dc.subject | ACS prognosis | en |
| dc.subject | ECG | en |
| dc.subject | Artificial Neural Network | en |
| dc.title | 以心電圖為基礎預測急性冠心症病人預後風險之研究 | zh_TW |
| dc.title | Risk Prediction for Post-ACS Patients Using ECG | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 102-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 吳安宇,曹恆偉,洪啟盛 | |
| dc.subject.keyword | 急性冠心症預後,心電圖,類神經網路, | zh_TW |
| dc.subject.keyword | ACS prognosis,ECG,Artificial Neural Network, | en |
| dc.relation.page | 93 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2013-11-05 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電子工程學研究所 | zh_TW |
| 顯示於系所單位： | 電子工程學研究所 | |
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