自12導程心電圖擷取P波特徵辨識竇性心律下之 心房顫動高風險病患

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蕭成儀(Cheng-Yi Hsiao) 查詢紙本館藏

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跨領域轉譯醫學研究所

論文名稱

自12導程心電圖擷取P波特徵辨識竇性心律下之心房顫動高風險病患
(Identification of patients with potential atrial fibrillation during sinus rhythm using isolated P wave characteristics from 12-lead ECGs)

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至系統瀏覽論文 (2026-2-1以後開放)

摘要(中)

心房顫動（Atrial fibrillation，簡稱AF）是最常見的心律不整 (arrhythmia)，許多患者因缺乏明顯症狀而未及時就診接受檢查與治療。先前的研究顯示利用大型資料集建構的深度神經網路（deep neural network，簡稱DNN），能透過12導程心電圖（electrocardiogram，簡稱ECG）篩檢出在竇性心律（sinus rhythm，簡稱SR）下的心房顫動。然而，這些訓練好的模型不僅取得不易，使用對外公開的小型資料集也很難重現相同的結果。本研究所提出的方法，能針對數量有限的資料提取有意義的特徵，藉以偵測在竇性心律下的心房顫動。我們從臺北醫學大學附設醫院的64,196位病人中，蒐集到94,224筆12導程心電圖資料，從中挑選213位病人在心房顫動診斷前的竇性心律心電圖，並隨機選擇247位年齡配對的非心房顫動受試者作為對照。我們所開發的訊號處理技術，簡稱MA-UPEMD，利用主成分分析（principal component analysis，簡稱PCA）與導程間關聯性，可從12導程心電圖中分離出P波，並且定量其時序與空間特徵。合併這些特徵後，機械學習模型產生之AUC為0.64。我們的研究顯示即使只有如此少量的資料，使用我們開發的方法，也能描繪出表現心房電活性的P波，所提取特徵的效能亦優於帶通濾波器 (bandpass filter) 或深度神經網路擷取之P波。我們所提供的方法具生理可解釋性及再現性，能辨識在竇性心律下的心房顫動患者。

摘要(英)

Many AF patients are not properly diagnosed and treated due to the absence of evident signs or symptoms. Researchers used deep neural networks (DNN) with large datasets of 12-lead electrocardiograms (ECG) to detect AF during sinus rhythm (SR). However, these models are not only publicly inaccessible, but similar results are also irreproducible with small datasets. In our study, we established a method to identify AF during SR with explainable features extracted from limited data. We collected 94,224 12-lead ECG recordings from 64,196 patients who visited the Taipei Medical University Hospital. Two hundred and thirteen patients with SR ECG before AF diagnosis were selected, and 247 age-matched normal subjects were also selected to serve as controls. The technique we developed, MU-UPEMMD, extracts P-wave from 12-lead ECG and quantifies its spatiotemporal features by principal component analysis (PCA) and inter-lead relationships. With the combination of these features, the machine learning (ML) model yielded an AUC of 0.64. Our study demonstrates that MU-UPEMMD can delineate P-waves that represent the electrical activity of the atrium even with limited data. The performance of features extracted from P-wave is also superior to those from bandpass filter or DNN. The proposed approach can classify AF patients during SR with results that are physiologically explainable and reproducible.

關鍵字(中)

★ 心房顫動
★ 12導程心電圖
★ 主成分分析
★ P 波迴路

關鍵字(英)

★ atrial fibrillation
★ 12 lead ECG
★ principal component analysis
★ P loop

論文目次

目錄
中文摘要 iv
英文摘要 vi
致謝 viii
圖目次 x
表目次 xiii
縮寫字目次 xiv
第一章緒論 1
第一節心房顫動 1
第二節研究背景 4
第三節研究目的 8
第二章文獻探討 9
第一節資料前處理 9
第二節特徵 10
第三節機械學習演算法 13
第四節創新診斷法 18
第五節相關研究 18
第三章研究方法 36
第一節研究資料 36
第二節 ECG訊號前處理 38
第三節特徵提取 52
第四節 AF患者分類 59
第四章研究結果 66
第一節 MA-UPEMD與頻帶濾波器提取P波訊號之比較 66
第二節 ML模型預測AF之比較 68
第三節僅以P波振幅和期間作為特徵 71
第四節特徵重要性 73
第五章討論 75
第六章結論與未來展望 80
參考文獻 81

參考文獻

參考文獻
1 Mayo Clinic. Atrial fibrillation. https://www.mayoclinic.org/diseases-1 M Clinic. 2023.
2 H-W Yang, C-Y Hsiao, Y-Q Peng, et al. Identification of patients with potential atrial fibrillation during sinus rhythm using isolated p wave characteristics from 12-lead ecgs. Journal of Personalized Medicine 2022; 12(10):1608.
3 H Zulkifly, GY Lip, DA Lane. Epidemiology of atrial fibrillation. International journal of clinical practice 2018; 72(3):e13070.
4 SS Chugh, R Havmoeller, K Narayanan, et al. Worldwide epidemiology of atrial fibrillation: A global burden of disease 2010 study. Circulation 2014; 129(8):837-847.
5 EJ Benjamin, PA Wolf, RB D’Agostino, et al. Impact of atrial fibrillation on the risk of death: The framingham heart study. Circulation 1998; 98(10):946-952.
6 O Oladiran, I Nwosu. Stroke risk stratification in atrial fibrillation: A review of common risk factors. Journal of community hospital internal medicine perspectives 2019; 9(2):113-120.
7 PA Wolf, RD Abbott, WB Kannel. Atrial fibrillation as an independent risk factor for stroke: The framingham study. stroke 1991; 22(8):983-988.
8 C-E Chiang, T-J Wu, K-C Ueng, et al. 2016 guidelines of the taiwan heart rhythm society and the taiwan society of cardiology for the management of atrial fibrillation. Journal of the Formosan Medical Association 2016; 115(11):893-952.
9 S Lévy, M Maarek, P Coumel, et al. Characterization of different subsets of atrial fibrillation in general practice in france: The alfa study. Circulation 1999; 99(23):3028-3035.
10 MP Turakhia, AJ Ullal, DD Hoang, et al. Feasibility of extended ambulatory electrocardiogram monitoring to identify silent atrial fibrillation in high‐risk patients: The screening study for undiagnosed atrial fibrillation (study‐af). Clinical cardiology 2015; 38(5):285-292.
11 SR Steinhubl, J Waalen, AM Edwards, et al. Effect of a home-based wearable continuous ecg monitoring patch on detection of undiagnosed atrial fibrillation: The mstops randomized clinical trial. Jama 2018; 320(2):146-155.
12 C-H Tseng, C Lin, H-C Chang, et al. Cloud-based artificial intelligence system for large-scale arrhythmia screening. Computer 2019; 52(11):40-51.
13 MP Turakhia, M Desai, H Hedlin, et al. Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: The apple heart study. American heart journal 2019; 207:66-75.
14 P Sharma, TW Barrett, J Ng, et al. Surface ecg f wave analysis at initial onset of paroxysmal and persistent atrial fibrillation. Journal of cardiovascular electrophysiology 2017; 28(5):498-503.
15 S Petrutiu, J Ng, GM Nijm, et al. Atrial fibrillation and waveform characterization. IEEE engineering in medicine and biology magazine 2006; 25(6):24-30.
16 TX Zhao, CA Martin, JP Cooper, PR Gajendragadkar. Coarse fibrillatory waves in atrial fibrillation predict success of electrical cardioversion. Annals of Noninvasive Electrocardiology 2018; 23(4):e12528.
17 Rohde & Schwarz USA I. Capturing small ECG signals in medical applications. https://www.rohde-schwarz.com/us/applications/capturing-small-ecg-signals-in-medical-applications-application-card_56279-152385.html. 2023.
18 J Duan, Q Wang, B Zhang, et al. Accurate detection of atrial fibrillation events with rr intervals from ecg signals. Plos one 2022; 17(8):e0271596.
19 I Savelieva, AJ Camm. Clinical relevance of silent atrial fibrillation: Prevalence, prognosis, quality of life, and management. Journal of Interventional Cardiac Electrophysiology 2000; 4:369-382.
20 PE Dilaveris, HL Kennedy. Silent atrial fibrillation: Epidemiology, diagnosis, and clinical impact. Clinical cardiology 2017; 40(6):413-418.
21 A Rizwan, A Zoha, R Zhang, et al. A review on the role of nano-communication in future healthcare systems: A big data analytics perspective. IEEE Access 2018; 6:41903-41920.
22 JR Sutton, R Mahajan, O Akbilgic, R Kamaleswaran. Physonline: An open source machine learning pipeline for real-time analysis of streaming physiological waveform. IEEE journal of biomedical and health informatics 2018; 23(1):59-65.
23 R Mahajan, R Kamaleswaran, JA Howe, O Akbilgic. Cardiac rhythm classification from a short single lead ecg recording via random forest. ed.^,eds. 2017 Computing in Cardiology (CinC): IEEE, 2017:1-4.
24 S-C Pei, C-C Tseng. Elimination of ac interference in electrocardiogram using iir notch filter with transient suppression. IEEE transactions on biomedical engineering 1995; 42(11):1128-1132.
25 A Rizwan, A Zoha, IB Mabrouk, et al. A review on the state of the art in atrial fibrillation detection enabled by machine learning. IEEE reviews in biomedical engineering 2020; 14:219-239.
26 J Park, C Lee, E Leshem, et al. Early differentiation of long-standing persistent atrial fibrillation using the characteristics of fibrillatory waves in surface ecg multi-leads. Scientific reports 2019; 9(1):2746.
27 A Petrenas, V Marozas, L Sornmo, A Lukosevicius. An echo state neural network for qrst cancellation during atrial fibrillation. IEEE transactions on biomedical engineering 2012; 59(10):2950-2957.
28 P Guzik, J Piskorski, T Krauze, et al. Heart rate asymmetry by poincaré plots of rr intervals. 2006.
29 Ö Yakut, ED Bolat. An improved qrs complex detection method having low computational load. Biomedical Signal Processing and Control 2018; 42:230-241.
30 J Park, S Lee, M Jeon. Atrial fibrillation detection by heart rate variability in poincare plot. Biomedical engineering online 2009; 8(1):1-12.
31 C-C Lin, H-Y Chang, Y-H Huang, C-Y Yeh. A novel wavelet-based algorithm for detection of qrs complex. Applied Sciences 2019; 9(10):2142.
32 JS Lee, SJ Lee, M Choi, et al. Qrs detection method based on fully convolutional networks for capacitive electrocardiogram. Expert systems with applications 2019; 134:66-78.
33 J Pan, WJ Tompkins. A real-time qrs detection algorithm. IEEE transactions on biomedical engineering 1985; 3:230-236.
34 AF Hussein, M Burbano-Fernandez, G Ramírez-Gonzalez, et al. An automated remote cloud-based heart rate variability monitoring system. IEEE access 2018; 6:77055-77064.
35 B Remeseiro, V Bolon-Canedo. A review of feature selection methods in medical applications. Computers in biology and medicine 2019; 112:103375.
36 AN Putra, H Oktavianto. A portable and mobile systems for identifying cardiac arrhythmia using naïve bayes. ed.^,eds. 2023 International Electronics Symposium (IES): IEEE, 2023:173-178.
37 F Chiarugi, M Varanini, F Cantini, et al. Noninvasive ecg as a tool for predicting termination of paroxysmal atrial fibrillation. IEEE Transactions on Biomedical Engineering 2007; 54(8):1399-1406.
38 B Pourbabaee, MJ Roshtkhari, K Khorasani. Deep convolutional neural networks and learning ecg features for screening paroxysmal atrial fibrillation patients. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2017; 48(12):2095-2104.
39 B Young, D Brodnick, R Spaulding. A comparative study of a hidden markov model detector for atrial fibrillation. Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat No 98TH8468): IEEE, 1999:468-476.

40 S Asgari, A Mehrnia, M Moussavi. Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine. Computers in biology and medicine 2015; 60:132-142.
41 M Mohebbi, H Ghassemian. Prediction of paroxysmal atrial fibrillation based on non-linear analysis and spectrum and bispectrum features of the heart rate variability signal. Computer methods and programs in biomedicine 2012; 105(1):40-49.
42 S Kara, M Okandan. Atrial fibrillation classification with artificial neural networks. Pattern Recognition 2007; 40(11):2967-2973.
43 R He, K Wang, N Zhao, et al. Automatic detection of atrial fibrillation based on continuous wavelet transform and 2d convolutional neural networks. Frontiers in physiology 2018; 9:1206.
44 S Ladavich, B Ghoraani. Rate-independent detection of atrial fibrillation by statistical modeling of atrial activity. Biomedical Signal Processing and Control 2015; 18:274-281.
45 PS Doliwa, V Frykman, M Rosenqvist. Short-term ecg for out of hospital detection of silent atrial fibrillation episodes. Scandinavian Cardiovascular Journal 2009; 43(3):163-168.
46 G Kaleschke, B Hoffmann, I Drewitz, et al. Prospective, multicentre validation of a simple, patient-operated electrocardiographic system for the detection of arrhythmias and electrocardiographic changes. Europace 2009; 11(10):1362-1368.
47 A Samol, M Masin, R Gellner, et al. Prevalence of unknown atrial fibrillation in patients with risk factors. Europace 2013; 15(5):657-662.
48 DD McManus, J Lee, O Maitas, et al. A novel application for the detection of an irregular pulse using an iphone 4s in patients with atrial fibrillation. Heart Rhythm 2013; 10(3):315-319.
49 JK Lau, N Lowres, L Neubeck, et al. Iphone ecg application for community screening to detect silent atrial fibrillation: A novel technology to prevent stroke. International journal of cardiology 2013; 165(1):193-194.
50 G Marazzi, F Iellamo, M Volterrani, et al. Comparison of microlife bp a200 plus and omron m6 blood pressure monitors to detect atrial fibrillation in hypertensive patients. Advances in therapy 2012; 29:64-70.
51 J Wiesel, S Abraham, FC Messineo. Screening for asymptomatic atrial fibrillation while monitoring the blood pressure at home: Trial of regular versus irregular pulse for prevention of stroke (tripps 2.0). The American journal of cardiology 2013; 111(11):1598-1601.
52 J Wiesel, L Fitzig, Y Herschman, FC Messineo. Detection of atrial fibrillation using a modified microlife blood pressure monitor. American journal of hypertension 2009; 22(8):848-852.

53 J-P Couderc, S Kyal, LK Mestha, et al. Detection of atrial fibrillation using contactless facial video monitoring. Heart Rhythm 2015; 12(1):195-201.
54 H Kottkamp, B Hügl, B Krauss, et al. Electromagnetic versus fluoroscopic mapping of the inferior isthmus for ablation of typical atrial flutter: A prospective randomized study. Circulation 2000; 102(17):2082-2086.
55 J Hampton. The ecg in practice: Elsevier Health Sciences, 2013.
56 S Raghunath, JM Pfeifer, AE Ulloa-Cerna, 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):1287-1298.
57 ZI Attia, PA Noseworthy, F Lopez-Jimenez, 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):861-867.
58 S Khurshid, S Friedman, C Reeder, et al. Ecg-based deep learning and clinical risk factors to predict atrial fibrillation. Circulation 2022; 145(2):122-133.
59 H-W Yang, S-K Jeng, H-WV Young, et al. A minimum arclength method for removing spikes in empirical mode decomposition. IEEE Access 2019; 7:13284-13294.
60 Y-H Wang, C-H Yeh, H-WV Young, et al. On the computational complexity of the empirical mode decomposition algorithm. Physica A: Statistical Mechanics and its Applications 2014; 400:159-167.
61 PYWAVELETS. Wavelet symlets 10 (sym10). https://wavelets.pybytes.com/wavelet/sym10/. 2023.
62 P Laguna, R Jané, P Caminal. Automatic detection of wave boundaries in multilead ecg signals: Validation with the cse database. Computers and biomedical research 1994; 27(1):45-60.
63 R Deering, JF Kaiser. The use of a masking signal to improve empirical mode decomposition. Proceedings(ICASSP′05) IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005. Vol 4: IEEE, 2005:iv/485-iv/488 Vol. 4.
64 Y-H Wang, K Hu, M-T Lo. Uniform phase empirical mode decomposition: An optimal hybridization of masking signal and ensemble approaches. Ieee Access 2018; 6:34819-34833.
65 G Rilling, P Flandrin. One or two frequencies? The empirical mode decomposition answers. IEEE transactions on signal processing 2007; 56(1):85-95.
66 Y-H Wang, H-WV Young, M-T Lo. The inner structure of empirical mode decomposition. Physica A: Statistical Mechanics and its Applications 2016; 462:1003-1017.
67 W-H Hsieh, C-Y Lin, ALD Te, et al. A novel noninvasive surface ecg analysis using interlead qrs dispersion in arrhythmogenic right ventricular cardiomyopathy. PloS one 2017; 12(8):e0182364.
68 RN Bracewell. The fourier transform. Scientific American 1989; 260(6):86-95.
69 F Pedregosa, G Varoquaux, A Gramfort, et al. Scikit-learn: Machine learning in python. the Journal of machine Learning research 2011; 12:2825-2830.
70 SM Lundberg, S-I Lee. A unified approach to interpreting model predictions. Advances in neural information processing systems 2017.
71 MV Perez, KW Mahaffey, H Hedlin, et al. Large-scale assessment of a smartwatch to identify atrial fibrillation. New England Journal of Medicine 2019; 381(20):1909-1917.
72 J Mant, D Edwards. Stroke prevention in atrial fibrillation: Putting the guidelines into practice. Drugs & aging 2010; 27:859-870.
73 MP Turakhia, PD Ziegler, SK Schmitt, et al. Atrial fibrillation burden and short-term risk of stroke: Case-crossover analysis of continuously recorded heart rhythm from cardiac electronic implanted devices. Circulation: Arrhythmia and Electrophysiology 2015; 8(5):1040-1047.
74 MA Allessie, K Konings, CJ Kirchhof, M Wijffels. Electrophysiologic mechanisms of perpetuation of atrial fibrillation. The American journal of cardiology 1996; 77(3):10A-23A.
75 KJ Hari, TP Nguyen, EZ Soliman. Relationship between p-wave duration and the risk of atrial fibrillation. Expert Review of Cardiovascular Therapy 2018; 16(11):837-843.
76 T-F Chao, C-J Liu, T-C Tuan, et al. Lifetime risks, projected numbers, and adverse outcomes in asian patients with atrial fibrillation: A report from the taiwan nationwide af cohort study. Chest 2018; 153(2):453-466.
77 MP Turakhia, DD Hoang, P Zimetbaum, et al. Diagnostic utility of a novel leadless arrhythmia monitoring device. The American journal of cardiology 2013; 112(4):520-524.

指導教授

羅孟宗林澂(Men-Tzung Lo Chen Lin)

審核日期

2024-1-30

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