博碩士論文 111827001 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:59 、訪客IP:3.133.153.224
姓名 彭鈺琪(Yu-Qi Peng)  查詢紙本館藏   畢業系所 生物醫學工程研究所
論文名稱 結合體表心電圖及深度學習發展心血管疾病患者之泛化性風險預測模型與個人化數位孿生心臟
(Utilizing Body Surface Potentials and Deep Learning to Develop Generalizable Risk Prediction Models and Personalized Digital Twin Hearts for Cardiovascular Disease Patients)
相關論文
★ 非接觸式生理感測訊號分析研究★ 以磁振造影探究有病灶及無病灶神經疾病的自動偵測方法之開發
★ 複雜系統跨頻率耦合方法★ 不同麻醉深度之相位-振幅耦合量測及強度比較
★ 基於小波轉換之單一導程心電圖 重構12導程心電圖與分類★ 發展非侵入式即時交感神經活性指標之量測系統
★ 以靜息態功能性磁振造影探討頸動脈支架手術對於頸動脈狹窄病患大腦功能之影響★ 運用加速度計實現具多項生理功能量測之即時監控IOT平台
★ 功能性抗生物沾黏單層膜於冠狀動脈心血管疾病標誌物之檢測應用★ 創新利用模擬呼吸竇性心律不整之多階熵評估乙型腎上腺素阻斷劑在心衰竭病人之治療成效
★ 發展高抗干擾非接觸式生理訊號監測系統★ 應用特徵分群技術於非侵入式神經活性與行 為活動訊號之生物指標萃取
★ 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★ 從同步鼾聲聲學分析和睡眠動態核磁共振成像進行靜態顱面測量和動態上呼吸道塌陷觀察,並探討其與阻塞性睡眠呼吸中止症嚴重程度的關聯。
★ 口內負壓睡眠裝置對於睡眠呼吸中止病人的轉譯研究- 針對解剖結構治療療效及策略探討★ 體外加強反搏治療裝置開發
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2027-8-16以後開放)
摘要(中) 根據世界衛生組織(WHO)公布的全球前十大死因調查中,心血管疾病包含缺血性心臟疾病和中風佔據該排行前二名將近20年,且每年有1790萬人死於心血管疾病,相當於三分之一的死亡人數。了解心臟的電生理活動有助於心臟疾病的診斷與治療,心電圖紀錄了心臟放電活動訊號,為臨床上非侵入式檢測心臟疾病的利器,配合深度學習強大的運算能力,有望協助臨床上的疾病診斷,達成數位醫療和即時診斷的目標。
  本研究使用體表心電圖發展多項研究主題,囊括應用深度學習於心血管疾病辨識和風險評估的研究以及建立個人化數位心臟模型,藉以剖析深度學習於臨床應用的可能性,透過不同面向研究模型的穩定性、跨裝置與跨資料庫之泛化能力和真實應用情境的效能。此外,使用高密度的體表心電圖與心電圖成像技術,運用非侵入的方法建立個人化的心臟模型,以期協助個人化的精準醫療決策。研究主題包含四大項:(1)單導心電圖於辨識低左心室射血分率之心臟衰竭,使用穿戴式裝置心電圖配合居家量測,檢測出潛在心臟功能與構造異常患者,提醒盡早就醫檢查;(2)從竇性心律12導心電圖中找出潛在的陣發型心房顫動,並應用於與心房顫動息息相關的中風患者之風險評估;(3)針對急性心肌梗塞患者的預後死亡風險預測與長期死亡風險分層能力;(4)使用心電圖成像技術建立個人化數位孿生心臟,多數心臟疾病源自於心臟構造異常與心肌細胞基質改變,進而影響心肌細胞的傳導,從CT影像與體表心電圖重構心臟電位,藉以分析異常的心臟放電情形與傳導途徑。
  上述主題涵蓋不同使用場合與情景的應用,從醫院臨床資料到居家個人使用、適用大眾群體到專注於特定患者群體的模型、群體適用之模型到專注於個人化的孿生心臟模型,發展從群體到個體層面的各式疾病診斷協助工具。
摘要(英) Cardiovascular diseases, including ischemic heart disease and stroke, have been the leading causes of death according to the World Health Organization (WHO) over the past 20 years. Understanding electrophysiology is crucial for comprehending the pathogenesis and diagnosis of cardiovascular diseases. An electrocardiogram (ECG) is a noninvasive tool that records the electrical cardiac activity through the body surface. With the strong computational capabilities of deep learning (DL) models, its application in clinical diagnosis is promising. This aligns with the goals of digital medicine and instant diagnosis.
This study covers several topics that utilize ECGs and DL models for cardiovascular disease identification and risk stratification, aiming to analyze the feasibility and generalizability of DL models in real-world clinical settings. Moreover, body surface potential mapping leverages ECG imaging to noninvasively reconstruct epicardial potentials and create personalized digital twin hearts to achieve personalized precision medicine decision support. The topics include (1) Identification of low left ventricular ejection fraction heart failure using a single-lead ECG. (2) Identification of paroxysmal atrial fibrillation from sinus rhythm 12-lead ECG. (3) Prognosis prediction of first-year mortality in post-myocardial infarction patients using 12-lead ECG and DL. (4) Developing a personalized digital twin heart with ECG imaging technique.
The topics mentioned above cover applications in various scenarios, ranging from clinical hospital to personal home usage, from models applicable to the general population to specific patient groups, and from population-wide models to personalized digital twin heart models, developing various disease diagnostic support tools from the population level to the individual level.
關鍵字(中) ★ 心血管疾病
★ 深度學習
★ 數位醫療
★ 心臟衰竭
★ 心房顫動
★ 數位孿生心臟
★ 心電圖逆問題
★ 心電圖成像
關鍵字(英) ★ cardiovascular diseases
★ deep learning
★ digital medicine
★ heart failure
★ atrial fibrillation
★ digital twin heart
★ inverse probelm of electrocardiography
★ ECG imaging
論文目次 摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vii
第一章 緒論 1
1-1 深度學習與心血管疾病之應用 1
1-2 心臟構造與心電圖簡介 1
1-3 研究目的 2
第二章 居家使用單導極心電圖識別低左心室射血分數之心臟衰竭 4
2-1 研究背景 4
2-2 文獻探討與動機 4
2-3 研究方法 6
2-3-1 臨床12導心電訊號資料 6
2-3-2 深度學習模型開發 7
2-3-3 單導心電圖資料與前處理 8
2-3-4 心率變異度分析 9
2-4 研究結果 13
2-4-1 回顧性研究分類結果與表現比較 13
2-4-2 前瞻性研究成果 16
2-4-3 心率變異度辨識能力 17
2-4-4 整合結果 19
2-4-5 LVEF次等級表現分析 19
2-4-6 模型穩健性測試-單導心電圖不同輸入時長預測表現 20
2-4-7 裝置端運算與應用 22
2-5 討論與限制 23
2-6 結論 24
第三章 竇性心律心電圖辨識潛在陣發型心房顫動於中風患者的風險分層 25
3-1 研究背景 25
3-2 文獻探討與動機 25
3-3 研究方法 27
3-3-1 模型開發與訓練 27
3-3-2 可解釋性深度學習模型 28
3-3-3 外部驗證集-中風患者的心房顫動檢測 30
3-4 研究結果 30
3-4-1 內部測試集預測結果 30
3-4-2 模型預測之解釋性 32
3-4-3 外部驗證集-應用於中風患者 40
3-4-4 外部驗證集之追蹤分析 41
3-5 討論與限制 43
3-6 結論 44
第四章 12導心電圖預測心肌梗塞病患預後死亡率 45
4-1 研究背景 45
4-2 文獻探討與動機 45
4-3 研究方法 46
4-3-1 開發資料集 46
4-3-2 模型開發 47
4-4 研究結果 48
4-4-1 分類結果 48
4-4-2 長期存活分析 48
4-5 結論 52
第五章 基於心電圖成像技術建立個人化數位孿生心臟於疾病應用 53
5-1 研究背景 53
5-1-1 Forward problem 54
5-1-2 Inverse problem 55
5-2 文獻探討 55
5-3 研究方法 56
5-3-1 Method of fundamental solutions (MFS) 56
5-3-2 正則化參數最佳化方法-CRESO 59
5-3-3 等時線圖(isochrone)與激發起搏位置判定 60
5-3-4 資料來源-公開資料庫EDGAR 60
5-3-5 表現指標 62
5-4 研究結果 63
5-4-1 離體犬隻心臟於仿真軀體模型 63
5-4-2 臨床PVC患者量測異位點激發案例 69
5-5 討論與限制 71
5-6 結論 72
參考文獻 73
附錄一 80
參考文獻 1. Macfarlane, P.W., et al., Comprehensive electrocardiology. 2010: Springer Science & Business Media.
2. McDonagh, T.A., et al., 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: Developed by the Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC) With the special contribution of the Heart Failure Association (HFA) of the ESC. European heart journal, 2021. 42(36): p. 3599-3726.
3. Bytyçi, I. and G. Bajraktari, Mortality in heart failure patients. Anatolian journal of cardiology, 2015. 15(1): p. 63.
4. Muntwyler, J., et al., One-year mortality among unselected outpatients with heart failure. European heart journal, 2002. 23(23): p. 1861-1866.
5. Siirilä-Waris, K., et al., Characteristics, outcomes, and predictors of 1-year mortality in patients hospitalized for acute heart failure. European heart journal, 2006. 27(24): p. 3011-3017.
6. Levy, D., et al., Long-term trends in the incidence of and survival with heart failure. New England Journal of Medicine, 2002. 347(18): p. 1397-1402.
7. Savarese, G., et al., Prevalence and prognostic implications of longitudinal ejection fraction change in heart failure. JACC: Heart Failure, 2019. 7(4): p. 306-317.
8. Bhalla, V., et al., Diagnostic ability of B-type natriuretic peptide and impedance cardiography: testing to identify left ventricular dysfunction in hypertensive patients. American journal of hypertension, 2005. 18(S2): p. 73S-81S.
9. Davie, A., et al., Value of the electrocardiogram in identifying heart failure due to left ventricular systolic dysfunction. British Medical Journal, 1996. 312(7025): p. 222-223.
10. Casolo, G., et al., Decreased spontaneous heart rate variability in congestive heart failure. Am J Cardiol, 1989. 64(18): p. 1162-7.
11. Florea, V.G. and J.N. Cohn, The autonomic nervous system and heart failure. Circ Res, 2014. 114(11): p. 1815-26.
12. Tsai, C.H., et al., Usefulness of heart rhythm complexity in heart failure detection and diagnosis. Sci Rep, 2020. 10(1): p. 14916.
13. Shah, S.A., et al., Relation of short-term heart rate variability to incident heart failure (from the Multi-Ethnic Study of Atherosclerosis). Am J Cardiol, 2013. 112(4): p. 533-40.
14. Chiou, Y.-A., C.-L. Hung, and S.-F. Lin, AI-assisted echocardiographic prescreening of heart failure with preserved ejection fraction on the basis of intrabeat dynamics. Cardiovascular Imaging, 2021. 14(11): p. 2091-2104.
15. Attia, Z.I., et al., Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram. Nature medicine, 2019. 25(1): p. 70-74.
16. Attia, Z.I., et al., Prospective evaluation of smartwatch-enabled detection of left ventricular dysfunction. Nature medicine, 2022. 28(12): p. 2497-2503.
17. Attia, Z.I., et al., Automated detection of low ejection fraction from a one-lead electrocardiogram: application of an AI algorithm to an electrocardiogram-enabled Digital Stethoscope. European Heart Journal-Digital Health, 2022. 3(3): p. 373-379.
18. Bachtiger, P., et al., Point-of-care screening for heart failure with reduced ejection fraction using artificial intelligence during ECG-enabled stethoscope examination in London, UK: a prospective, observational, multicentre study. The Lancet Digital Health, 2022. 4(2): p. e117-e125.
19. Pan, J. and W.J. Tompkins, A real-time QRS detection algorithm. IEEE transactions on biomedical engineering, 1985(3): p. 230-236.
20. Electrophysiology, T.F.o.t.E.S.o.C.t.N.A.S.o.P., Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circulation, 1996. 93(5): p. 1043-1065.
21. Bauer, A., et al., Phase-rectified signal averaging detects quasi-periodicities in non-stationary data. Physica A: Statistical Mechanics and its Applications, 2006. 364: p. 423-434.
22. Bauer, A., et al., Deceleration capacity of heart rate as a predictor of mortality after myocardial infarction: cohort study. The lancet, 2006. 367(9523): p. 1674-1681.
23. İşler, Y. and M. Kuntalp, Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure. Computers in biology and medicine, 2007. 37(10): p. 1502-1510.
24. Guzzetti, S., et al., Symbolic dynamics of heart rate variability: a probe to investigate cardiac autonomic modulation. Circulation, 2005. 112(4): p. 465-470.
25. La Rovere, M.T., et al., Short-term heart rate variability strongly predicts sudden cardiac death in chronic heart failure patients. circulation, 2003. 107(4): p. 565-570.
26. Hu, W., et al., Deceleration and acceleration capacities of heart rate associated with heart failure with high discriminating performance. Scientific reports, 2016. 6(1): p. 23617.
27. Savarese, G., et al., Heart failure with mid-range or mildly reduced ejection fraction. Nature Reviews Cardiology, 2022. 19(2): p. 100-116.
28. Joglar, J.A., et al., 2023 ACC/AHA/ACCP/HRS guideline for the diagnosis and management of atrial fibrillation: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation, 2024. 149(1): p. e1-e156.
29. Wolf, P.A., R.D. Abbott, and W.B. Kannel, Atrial fibrillation as an independent risk factor for stroke: the Framingham Study. stroke, 1991. 22(8): p. 983-988.
30. Odutayo, A., et al., Atrial fibrillation and risks of cardiovascular disease, renal disease, and death: systematic review and meta-analysis. bmj, 2016. 354.
31. Hart, R., et al., Cardioembolic vs. noncardioembolic strokes in atrial fibrillation: frequency and effect of antithrombotic agents in the stroke prevention in atrial fibrillation studies. Cerebrovascular diseases, 2000. 10(1): p. 39-43.
32. Kottkamp, H., Human atrial fibrillation substrate: towards a specific fibrotic atrial cardiomyopathy. European heart journal, 2013. 34(35): p. 2731-2738.
33. Tanigawa, M., et al., Prolonged and fractionated right atrial electrograms during sinus rhythm in patients with paroxysmal atrial fibrillation and sick sinus node syndrome. Journal of the American College of Cardiology, 1991. 17(2): p. 403-408.
34. Pachon M, J.C., et al., A new treatment for atrial fibrillation based on spectral analysis to guide the catheter RF-ablation. EP Europace, 2004. 6(6): p. 590-601.
35. Attia, Z.I., et al., An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. The Lancet, 2019. 394(10201): p. 861-867.
36. Noseworthy, P.A., et al., Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial. The Lancet, 2022. 400(10359): p. 1206-1212.
37. Raghunath, S., et al., Deep neural networks can predict new-onset atrial fibrillation from the 12-lead ECG and help identify those at risk of atrial fibrillation–related stroke. Circulation, 2021. 143(13): p. 1287-1298.
38. Hygrell, T., et al., An artificial intelligence–based model for prediction of atrial fibrillation from single-lead sinus rhythm electrocardiograms facilitating screening. Europace, 2023. 25(4): p. 1332-1338.
39. Sanna, T., et al., Cryptogenic stroke and underlying atrial fibrillation. New England Journal of Medicine, 2014. 370(26): p. 2478-2486.
40. Schuchert, A., G. Behrens, and T. Meinertz, Impact of long‐term ECG recording on the detection of paroxysmal atrial fibrillation in patients after an acute ischemic stroke. Pacing and clinical electrophysiology, 1999. 22(7): p. 1082-1084.
41. Rizos, T., et al., Detection of paroxysmal atrial fibrillation in acute stroke patients. Cerebrovascular Diseases, 2010. 30(4): p. 410-417.
42. Rabinstein, A.A., et al., Artificial intelligence-enabled ECG to identify silent atrial fibrillation in embolic stroke of unknown source. Journal of Stroke and Cerebrovascular Diseases, 2021. 30(9): p. 105998.
43. Choi, J., et al., Artificial intelligence predicts undiagnosed atrial fibrillation in patients with embolic stroke of undetermined source using sinus rhythm electrocardiograms. Heart Rhythm, 2024.
44. Lundberg, S.M. and S.-I. Lee, A unified approach to interpreting model predictions. Advances in neural information processing systems, 2017. 30.
45. Shapley, L.S., A value for n-person games. 1953.
46. Kaplan, E.L. and P. Meier, Nonparametric estimation from incomplete observations. Journal of the American statistical association, 1958. 53(282): p. 457-481.
47. Cox, D.R., Regression models and life‐tables. Journal of the Royal Statistical Society: Series B (Methodological), 1972. 34(2): p. 187-202.
48. Smolina, K., et al., Determinants of the decline in mortality from acute myocardial infarction in England between 2002 and 2010: linked national database study. Bmj, 2012. 344.
49. Gerber, Y., et al., Mortality associated with heart failure after myocardial infarction: a contemporary community perspective. Circulation: Heart Failure, 2016. 9(1): p. e002460.
50. Sulo, G., et al., Heart failure complicating acute myocardial infarction; burden and timing of occurrence: a nation‐wide analysis including 86 771 patients from the Cardiovascular Disease in Norway (CVDNOR) Project. Journal of the American Heart Association, 2016. 5(1): p. e002667.
51. Smolina, K., et al., Long-term survival and recurrence after acute myocardial infarction in England, 2004 to 2010. Circulation: Cardiovascular Quality and Outcomes, 2012. 5(4): p. 532-540.
52. Dani, S.S., et al., Trends in premature mortality from acute myocardial infarction in the United States, 1999 to 2019. Journal of the American Heart Association, 2022. 11(1): p. e021682.
53. Schiele, F., et al., Compliance with guidelines and 1-year mortality in patients with acute myocardial infarction: a prospective study. European heart journal, 2005. 26(9): p. 873-880.
54. Szummer, K., et al., Improved outcomes in patients with ST-elevation myocardial infarction during the last 20 years are related to implementation of evidence-based treatments: experiences from the SWEDEHEART registry 1995–2014. European heart journal, 2017. 38(41): p. 3056-3065.
55. Granger, C.B., et al., Predictors of hospital mortality in the global registry of acute coronary events. Archives of internal medicine, 2003. 163(19): p. 2345-2353.
56. Antman, E.M., et al., The TIMI risk score for unstable angina/non–ST elevation MI: a method for prognostication and therapeutic decision making. Jama, 2000. 284(7): p. 835-842.
57. de Araújo Gonçalves, P., et al., TIMI, PURSUIT, and GRACE risk scores: sustained prognostic value and interaction with revascularization in NSTE‐ACS. European heart journal, 2005. 26(9): p. 865-872.
58. D′Ascenzo, F., et al., TIMI, GRACE and alternative risk scores in Acute Coronary Syndromes: a meta-analysis of 40 derivation studies on 216,552 patients and of 42 validation studies on 31,625 patients. Contemporary clinical trials, 2012. 33(3): p. 507-514.
59. Wu, A.H., et al., Hospital outcomes in patients presenting with congestive heart failure complicating acute myocardial infarction: a report from the Second National Registry of Myocardial Infarction (NRMI-2). Journal of the American College of Cardiology, 2002. 40(8): p. 1389-1394.
60. Hathaway, W.R., et al., Prognostic significance of the initial electrocardiogram in patients with acute myocardial infarction. Jama, 1998. 279(5): p. 387-391.
61. Raghunath, S., et al., Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network. Nature medicine, 2020. 26(6): p. 886-891.
62. Mohammad, M.A., et al., Development and validation of an artificial neural network algorithm to predict mortality and admission to hospital for heart failure after myocardial infarction: a nationwide population-based study. The Lancet Digital Health, 2022. 4(1): p. e37-e45.
63. Khera, R., et al., Use of machine learning models to predict death after acute myocardial infarction. JAMA cardiology, 2021. 6(6): p. 633-641.
64. Oliveira, M., et al., Machine learning prediction of mortality in Acute Myocardial Infarction. BMC Medical Informatics and Decision Making, 2023. 23(1): p. 70.
65. Lee, W., et al., Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction. Scientific reports, 2021. 11(1): p. 12886.
66. Kwon, J.-m., et al., Deep-learning-based risk stratification for mortality of patients with acute myocardial infarction. PloS one, 2019. 14(10): p. e0224502.
67. Plonsey, R. and D.B. Heppner, Considerations of quasi-stationarity in electrophysiological systems. The Bulletin of mathematical biophysics, 1967. 29: p. 657-664.
68. Jackson, J.D., Classical electrodynamics. 2021: John Wiley & Sons.
69. Franzone, P.C., et al., Finite element approximation of regularized solutions of the inverse potential problem of electrocardiography and applications to experimental data. Calcolo, 1985. 22(1): p. 91-186.
70. Durrer, D., et al., Total excitation of the isolated human heart. Circulation, 1970. 41(6): p. 899-912.
71. Barr, R.C. and M. Spach, Inverse calculation of QRS-T epicardial potentials from body surface potential distributions for normal and ectopic beats in the intact dog. Circulation research, 1978. 42(5): p. 661-675.
72. Gardner, P., et al., Electrophysiologic and anatomic basis for fractionated electrograms recorded from healed myocardial infarcts. Circulation, 1985. 72(3): p. 596-611.
73. Burnes, J.E., et al., Noninvasive ECG imaging of electrophysiologically abnormal substrates in infarcted hearts: A model study. Circulation, 2000. 101(5): p. 533-540.
74. Cluitmans, M.J., et al., In vivo validation of electrocardiographic imaging. JACC: Clinical Electrophysiology, 2017. 3(3): p. 232-242.
75. Ramanathan, C., et al., Noninvasive electrocardiographic imaging for cardiac electrophysiology and arrhythmia. Nature medicine, 2004. 10(4): p. 422-428.
76. Cuculich, P.S., et al., The electrophysiological cardiac ventricular substrate in patients after myocardial infarction: noninvasive characterization with electrocardiographic imaging. Journal of the American College of Cardiology, 2011. 58(18): p. 1893-1902.
77. Barr, R.C., M. Ramsey, and M.S. Spach, Relating epicardial to body surface potential distributions by means of transfer coefficients based on geometry measurements. IEEE Transactions on biomedical engineering, 1977(1): p. 1-11.
78. Wang, Y. and Y. Rudy, Application of the method of fundamental solutions to potential-based inverse electrocardiography. Annals of biomedical engineering, 2006. 34: p. 1272-1288.
79. Bouhamama, O., et al., A patchwork method to improve the performance of current methods for solving the inverse problem of electrocardiography. IEEE Transactions on Biomedical Engineering, 2022. 70(1): p. 55-66.
80. Tikhonov, A.N. and V. Arsenin, Solutions of ill-posed problems. (No Title), 1977.
81. Oster, H.S. and Y. Rudy, The use of temporal information in the regularization of the inverse problem of electrocardiography. IEEE transactions on biomedical engineering, 1992. 39(1): p. 65-75.
82. Erem, B., et al., Using transmural regularization and dynamic modeling for noninvasive cardiac potential imaging of endocardial pacing with imprecise thoracic geometry. IEEE transactions on medical imaging, 2013. 33(3): p. 726-738.
83. Yao, B. and H. Yang, Physics-driven spatiotemporal regularization for high-dimensional predictive modeling: A novel approach to solve the inverse ECG problem. Scientific reports, 2016. 6(1): p. 39012.
84. Orkild, B.A., et al. A Sliding Window Approach to Regularization in Electrocardiographic Imaging. in 2022 Computing in Cardiology (CinC). 2022. IEEE.
85. Messinger-Rapport, B.J. and Y. Rudy, Regularization of the inverse problem in electrocardiography: A model study. Mathematical Biosciences, 1988. 89(1): p. 79-118.
86. Hansen, P.C., Analysis of discrete ill-posed problems by means of the L-curve. SIAM review, 1992. 34(4): p. 561-580.
87. Golub, G.H., M. Heath, and G. Wahba, Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics, 1979. 21(2): p. 215-223.
88. Colli-Franzone, P., et al., A mathematical procedure for solving the inverse potential problem of electrocardiography. Analysis of the time-space accuracy from in vitro experimental data. Mathematical Biosciences, 1985. 77(1-2): p. 353-396.
89. Johnston, P.R. and R.M. Gulrajani, A new method for regularization parameter determination in the inverse problem of electrocardiography. IEEE Transactions on Biomedical Engineering, 1997. 44(1): p. 19-39.
90. Kupradze, V.D., On the approximate solution of problems in mathematical physics. Russian Mathematical Surveys, 1967. 22(2): p. 58.
91. Kupradze, V., Potential methods in elasticity theory. Fizmatizdat, Moscow, 1963.
92. Sapp, J.L., et al., Inverse solution mapping of epicardial potentials: quantitative comparison with epicardial contact mapping. Circulation: Arrhythmia and Electrophysiology, 2012. 5(5): p. 1001-1009.
93. Aras, K., et al., Experimental data and geometric analysis repository—EDGAR. Journal of electrocardiology, 2015. 48(6): p. 975-981.
94. Bergquist, J.A., et al., The electrocardiographic forward problem: A benchmark study. Computers in biology and medicine, 2021. 134: p. 104476.
95. HWSchulze, W., et al., A simulation dataset for ECG imaging of paced.
96. Schulze, W.H., ECG imaging of ventricular activity in clinical applications. Vol. 22. 2015: KIT Scientific Publishing.
97. Oster, H.S., et al., Noninvasive electrocardiographic imaging: reconstruction of epicardial potentials, electrograms, and isochrones and localization of single and multiple electrocardiac events. Circulation, 1997. 96(3): p. 1012-1024.
98. Ghodrati, A., D.H. Brooks, and R.S. MacLeod, Methods of solving reduced lead systems for inverse electrocardiography. IEEE transactions on biomedical engineering, 2007. 54(2): p. 339-343.
99. Marques, V.G., et al. Effect of Reducing the Number of Leads from Body Surface Potential Mapping in Computer Models of Atrial Arrhythmias. in 2019 Computing in Cardiology (CinC). 2019. IEEE.
100. Bear, L.R., et al., The impact of torso signal processing on noninvasive electrocardiographic imaging reconstructions. IEEE Transactions on Biomedical Engineering, 2020. 68(2): p. 436-447.
指導教授 羅孟宗(Men-Tzung Lo) 審核日期 2024-7-26
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明