應用模擬電生理及人工智慧技術創造跨臨床心電圖資料庫之心肌缺血成像模型

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姓名

哈卓司(Harnod Zeus) 查詢紙本館藏

畢業系所

生物醫學工程研究所

論文名稱

應用模擬電生理及人工智慧技術創造跨臨床心電圖資料庫之心肌缺血成像模型
(Application of Virtual Electrophysiology and Artificial Intelligence Technology to Establish a Visualized Model of Myocardial Ischemia)

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摘要(中)

過去15年來，缺血性心臟病一直是世界10大首要死因之一，在2016年世界衛生組織(World Health Organization, WHO)的統計中，一千多萬人口死於該病灶。除了冠狀動脈阻塞造成的風險外，還可能引起許多併發症，包括：心室心律不整、心搏停止、房室傳導阻滯，甚至是心衰竭及心因性猝死；上述併發症危險性雖高，但是可以藉由及早血管重建治療來降低其風險，例如：經皮冠狀動脈介入治療、放置血管支架或非侵入性血栓溶劑。然而，現今用於檢測及定位心肌梗塞的醫療儀器，包括：單光子電腦斷層掃描與心導管檢查等，存在許多潛在缺點，低準確度、昂貴、耗時、侵入性或具有放射性、難以重現性及長期監測等，此外視為黃金標準的檢測方法也不具備遠距居家監測、快篩與早期診斷的能力，有鑑於此市場需求，本研究選用普遍被用於監測心臟電氣活動的12導程心電圖並用於心肌缺血的診斷，在現今的研究中，使用12導程心電圖訊號判斷是否發生心肌缺血已經具有不錯的準確度，然而定位心肌缺血發生的位置仍未被實踐。
臨床上，心肌缺血以及梗塞會造成該區域心肌細胞的跨細胞膜電位值異常，特徵有：延遲激發時間、激發電位值降低、靜止電位值上升、激發動作間期縮短等，而具備這些特徵的跨細胞膜電位的心肌細胞會導致該區域復極化電位梯度值上升，並造成鄰近缺血區域的心電圖導程訊號ST間期電位值上升或下降，以及T波電位值上升甚至是反轉等現象，本研究利用了這種現象的電生理機轉，以電腦模擬建立出擬真的心臟放電模型，並於該模型下模擬心肌缺血可能發生的所有位置與缺血嚴重程度後，使用forward計算得到這些心肌缺血模式下的12導程心電圖訊號並建立龐大的心肌缺血心電圖資料庫，以模擬心臟模型結合具有穩健性的稀疏表示式分類演算法應用於具有高度個體變異性的143位心肌梗塞病患，定位心肌梗塞位置的準確度可以達到0.91，最後將心肌缺血位置與嚴重程度從心臟模型投影到二維圓形平面上，過此技術，本研究視覺化的呈現心肌血流灌注情形並提升12導程心電圖診斷心肌梗塞的能力。

摘要(英)

In the past 15 years, ischemic heart disease is one of the world’s biggest killers. According to statistics from the World Health Organization in 2016, 10 million deaths were caused by it. This is not only due to the damage caused by coronary artery occlusion, but also many complications of myocardial ischemia including ventricular arrhythmia, cardiac arrest, atrioventricular block, and even heart failure and sudden cardiac death. Although these complications will cause serious damage, it can be prevented by early revascularization such as percutaneous coronary intervention (PCI), placing vascular stents, or thrombolytic agents. However, several clinical examination methods for diagnosing ischemia, such as single photon emission computed tomography and cardiac catheterization, may have many potential shortcomings, including low accuracy, high expense, time consumption, intrusive, the need of injection of radiocontrast agent, the problem of long-term monitoring, and the difficulty of reproducibility. Moreover, the gold standard examination method lacks of the abilities of remote home monitoring, rapid screening, and early diagnosis. In view of this market demand, the 12-lead electrocardiogram (ECG), which is commonly used to monitor the cardiac electrical activity, was selected and used for the diagnosis of myocardial ischemia. Besides, in the existing researches, the 12-lead ECG signals utilized to identify myocardial ischemia has achieved good accuracy. However, the localization of ischemia based on 12-lead ECG has not been practiced.
In clinic, myocardial ischemia and infarction will cause abnormalities to the waveform of myocardial cells’ transmembrane potentials (TMPs) in that region, including delayed activation time, reduced activation potential, rising resting potential, and reduced activation interval. These abnormal TMPs will increase the gradient of repolarized potentials in the ischemic region, and lead to the elevation or depression of the amplitude of ST segments and T waves, and even the inversion of T waves. Accordingly, based on this phenomenon of the electrophysiological mechanism, this study established a realistic ventricle-thorax anisotropic computer model to simulate the real-world electrical pacing of cardiac. By modifying the waveform of TMPs in different regions with various degrees of modification, we could simulate all possible ischemic regions and severity corresponding to real-world patients. Through forward calculation, we could calculate the 12-lead ECG signals from the ischemic cardiac potentials and established a massive 12-lead ECG ischemia database. The realistic computer cardiac model combined with a robust sparse representation classification algorithm was applied to 143 patients with myocardial infarction, and the accuracy of locating myocardial infarction could reach 0.91. Finally, we projected the severity and ischemic region from ventricular model into a 2D circular plane, which could visually display the perfusion of myocardial cells and improve the ability of 12-lead ECG to diagnose myocardial infarction.

關鍵字(中)

★ 心肌缺血
★ 心肌梗塞
★ 模擬心臟模型
★ 12導程心電圖
★ 視覺化診斷
★ 人工智慧

關鍵字(英)

★ myocardial ischemia
★ myocardial infarction
★ simulation model
★ 12-lead ECG
★ visual diagnosis
★ AI

論文目次

摘要 i
Abstract ii
目錄 iv
圖目錄 vi
第一章緒論 1
1-1 研究背景與動機 1
1-2 相關文獻導讀 2
1-3 本文架構 5
第二章研究原理 6
2-1 心臟電生理 6
2-2 跨細胞膜電位 7
2-3 12導程心電圖 9
第三章研究方法 11
3-1 擬真異質性電腦模擬心臟模型 11
3-2 基於心臟模型的心肌缺血模擬 13
3-2-1 五種缺血嚴重程度的跨細胞膜電位波型 13
3-2-2 建立26種心肌缺血位置 14
3-2-3 微調缺血區域的空間分布 14
3-2-4 模擬正常案例的心臟電位 15
3-3 Forward Calculation 15
3-4 訊號前處理與特徵提取 18
3-5 稀疏表示式分類器 21
3-6 產生驗證資料集 29
3-7 其他分類器模型 30
第四章研究結果與討論 31
4-1 使用模擬資料驗證模型 31
4-2 真實案例測試模型性能與成像效果 33
4-3 導程縮減 (Lead Reduction) 41
4-4 穩健性測試 43
4-4-1 多種雜訊程度下方法的性能 43
4-4-2 多種心軸角度的分類性能 45
4-5 優點與限制 47
第五章結論與延伸應用 48
5-1 結論 48
5-2 延伸應用 49
5-2-1 建立五種嚴重程度與七個心肌肥大位置的模擬 50
5-2-2 收縮型心衰竭12導程心電圖 50
參考文獻 52

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指導教授

羅孟宗(Men-Tzung Lo)

審核日期

2021-8-2

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