|
[1]
|
Bai, J., Lu, Y., Wang, H. and Zhao, J. (2022) How Synergy between Mechanistic and Statistical Models is Impacting Research in Atrial Fibrillation. Frontiers in Physiology, 13, Article 957604. [Google Scholar] [CrossRef] [PubMed]
|
|
[2]
|
Pitman, B.M., Chew, S.-H., Wong, C.X., et al. (2022) Prevalence and Risk Factors for Atrial Fibrillation in a Semi-Rural Sub-Saharan African Population: The Heart of Ethiopia: Focus on Atrial Fibrillation (TEFF-AF) Study. Heart Rhythm O2, 3, 839-846. [Google Scholar] [CrossRef] [PubMed]
|
|
[3]
|
Machino, T., Aonuma, K., Maruo, K., et al. (2023) Randomized Crossover Trial of 2-Week Garment Electrocardiogram with Dry Textile Electrode to Reveal Instances of Post-Ablation Recurrence of Atrial Fibrillation Underdiagnosed during 24-Hour Holter Monitoring. PLOS ONE, 18, e0281818. [Google Scholar] [CrossRef] [PubMed]
|
|
[4]
|
Wang, Y.-C., Xu, X., Hajra, A., et al. (2022) Current Ad-vancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study. Diagnostics, 12, Article No. 689. [Google Scholar] [CrossRef] [PubMed]
|
|
[5]
|
Jin, Y., Qin, C., Liu, J., et al. (2020) A Novel Domain Adaptive Residual Network for Automatic Atrial Fibrillation Detection. Knowledge-Based Systems, 203, Article ID: 106122. [Google Scholar] [CrossRef]
|
|
[6]
|
Wang, J. (2020) A Deep Learning Approach for Atrial Fibrilla-tion Signals Classification Based on Convolutional and Modified Elman Neural Network. Future Generation Computer Systems, 102, 670-679. [Google Scholar] [CrossRef]
|
|
[7]
|
Liu, Z., Zhang, Y., Chen, Y., Fan, X. and Dong, C. (2020) De-tection of Algorithmically Generated Domain Names Using the Recurrent Convolutional Neural Network with Spatial Pyramid Pooling. Entropy, 22, Article No. 1058. [Google Scholar] [CrossRef] [PubMed]
|
|
[8]
|
Ullah, H., Bin Heyat, M.B., Akhtar, F., et al. (2022) An End-to-End Car-diac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal. Computational Intelligence and Neuroscience, 2022, Article ID: 9475162. [Google Scholar] [CrossRef] [PubMed]
|
|
[9]
|
Hannun, A.Y., Rajpurkar, P., Haghpanahi, M., et al. (2019) Cardiolo-gist-Level Arrhythmia Detection and Classification in Ambulatory Electrocardiograms Using a Deep Neural Network. Nature Medicine, 25, 65-69. [Google Scholar] [CrossRef] [PubMed]
|
|
[10]
|
Ribeiro, A.H., Ribeiro, M.H., Paixão, G.M.M., et al. (2020) Au-tomatic Diagnosis of the 12-Lead ECG Using a Deep Neural Network. Nature Communications, 11, Article No. 1760. [Google Scholar] [CrossRef] [PubMed]
|
|
[11]
|
Lai, J., Chen, Y., Han, B., et al. (2019) A DenseNet-Based Di-agnosis Algorithm for Automated Diagnosis Using Clinical ECG Data. Journal of Southern Medical University, 39, 69-75.
|
|
[12]
|
Kleyko, D., Osipov, E. and Wiklund, U. (2020) A Comprehensive Study of Complexity and Performance of Automatic Detection of Atrial Fibrillation: Classification of Long ECG Recordings Based on the PhysioNet Compu-ting in Cardiology Challenge 2017. Biomedical Physics & Engineering Express, 6, Article ID: 025010. [Google Scholar] [CrossRef] [PubMed]
|
|
[13]
|
Kashou, A.H., Ko, W.-Y., Attia, Z.I., et al. (2020) A Comprehen-sive Artificial Intelligence-Enabled Electrocardiogram Interpretation Program. Cardiovascular Digital Health Journal, 1, 62-70. [Google Scholar] [CrossRef] [PubMed]
|
|
[14]
|
Rabinstein, A.A., Yost, M.D., Faust, L., et al. (2021) Arti-ficial Intelligence-Enabled ECG to Identify Silent Atrial Fibrillation in Embolic Stroke of Unknown Source. Journal of Stroke and Cerebrovascular Diseases, 30, Article ID: 105998. [Google Scholar] [CrossRef] [PubMed]
|
|
[15]
|
Attia, Z.I., Noseworthy, P.A., Lopez-Jimenez, F., et al. (2019) An Artificial Intelligence-Enabled ECG Algorithm for the Identification of Patients with Atrial Fibrillation during Sinus Rhythm: A Retrospective Analysis of Outcome Prediction. Lancet, 394, 861-867. [Google Scholar] [CrossRef]
|
|
[16]
|
Jansen, H.J., Bohne, L.J., Gillis, A.M. and Rose, R.A. (2020) A Trial Remodeling and Atrial Fibrillation in Acquired Forms of Cardiovascular Disease. Heart Rhythm O2, 1, 147-159. [Google Scholar] [CrossRef] [PubMed]
|
|
[17]
|
Schwamm, L.H., Kamel, H., Granger, C.B., et al. (2023) Predictors of Atrial Fibrillation in Patients with Stroke Attributed to Large- or Small-Vessel Disease: A Prespecified Secondary Analysis of the STROKE AF Randomized Clinical Trial. JAMA Neurology, 80, 99-103. [Google Scholar] [CrossRef] [PubMed]
|
|
[18]
|
Bisson, A., Lemrini, Y., El-Bouri, W., et al. (2022) Prediction of Incident Atrial Fibrillation in Post-Stroke Patients Using Machine Learning: A French Nationwide Study. Clinical Re-search in Cardiology. [Google Scholar] [CrossRef] [PubMed]
|
|
[19]
|
Lin, J.Y., Larson, J., Schoenberg, J., et al. (2022) Serial 7-Day Electrocardiogram Patch Screening for AF in High-Risk Older Women by the CHARGE-AF Score. JACC: Clinical Electrophysiology, 8, 1523-1534. [Google Scholar] [CrossRef] [PubMed]
|
|
[20]
|
Christopoulos, G., Graff-Radford, J., Lopez, C.L., et al. (2020) Artificial Intelligence-Electrocardiography to Predict Incident Atrial Fibrillation: A Population-Based Study. Circulation: Arrhythmia and Electrophysiology, 13, e009355. [Google Scholar] [CrossRef]
|
|
[21]
|
Khurshid, S., Friedman, S., Reeder, C., et al. (2022) ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation. Circulation, 145, 122-133. [Google Scholar] [CrossRef]
|
|
[22]
|
Hindricks, G., Potpara, T., Dagres, N., et al. (2021) 2020 ESC Guidelines for the Diagnosis and Management of Atrial Fibrillation Developed in Collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): The Task Force for the Diagnosis and Management of Atrial Fibrillation of the European Society of Cardiology (ESC) Developed with the Special Contribution of the European Heart Rhythm Association (EHRA) of the ESC. European Heart Journal, 42, 373-498. [Google Scholar] [CrossRef] [PubMed]
|
|
[23]
|
Monahan, K.H., Bunch, T.J., Mark, D.B., et al. (2022) Influence of Atrial Fibrillation Type on Outcomes of Ablation vs. Drug Therapy: Results from CABANA. EP Europace, 24, 1430-1440. [Google Scholar] [CrossRef] [PubMed]
|
|
[24]
|
Peigh, G. and Passman, R.S. (2023) “Pill-in-Pocket” Anticoagulation for Stroke Prevention in Atrial Fibrillation. Journal of Cardiovascular Electrophysiology. [Google Scholar] [CrossRef] [PubMed]
|
|
[25]
|
Lucà, F., Giubilato, S., Di Fusco, S.A., et al. (2021) Anticoagulation in Atrial Fibrillation Cardioversion: What Is Crucial to Take into Account. Journal of Clinical Medicine, 10, Article No. 3212. [Google Scholar] [CrossRef] [PubMed]
|
|
[26]
|
Tiver, K.D., Quah, J., Lahiri, A., Ganesan, A.N. and McGavigan, A.D. (2021) Atrial Fibrillation Burden: An Update—The Need for a CHA2DS2-VASc-Afburden Score. EP Europace, 23, 665-673. [Google Scholar] [CrossRef] [PubMed]
|
|
[27]
|
Perino, A.C., Fan, J., Askari, M., et al. (2019) Practice Variation in Anticoagulation Prescription and Outcomes after Device-Detected Atrial Fibrillation. Circulation, 139, 2502-2512. [Google Scholar] [CrossRef]
|
|
[28]
|
Shashikumar, S.P., Shah, A.J., Clifforfd, G.D. and Nemati, S. (2018) Detection of Paroxysmal Atrial Fibrillation Using Attention-Based Bidirectional Recurrent Neural Networks. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, 19-23 August 2018, 715-723. [Google Scholar] [CrossRef]
|
|
[29]
|
Kamel, H., Bartz, T.M., Longstreth Jr., W.T., et al. (2021) Cardiac Mechanics and Incident Ischemic Stroke: The Cardiovascular Health Study. Scientific Reports, 11, Article No. 17358. [Google Scholar] [CrossRef] [PubMed]
|
|
[30]
|
Gladstone, D.J., Spring, M., Dorian, P., et al. (2014) Atrial Fi-brillation in Patients with Cryptogenic Stroke. New England Journal of Medicine, 370, 2467-2477. [Google Scholar] [CrossRef]
|
|
[31]
|
Diemberger, I., Biffi, M., Lorenzetti, S., et al. (2018) Predictors of Long-Term Survival Free from Relapses after Extraction of Infected CIED. EP Europace, 20, 1018-1027. [Google Scholar] [CrossRef] [PubMed]
|
|
[32]
|
Sijerčić, A. and Tahirović, E. (2022) Photoplethysmography-Based Smart Devices for Detection of Atrial Fibrillation. Texas Heart Institute Journal, 49, e21756. [Google Scholar] [CrossRef]
|
|
[33]
|
Saarinen, H.J., Joutsen, A., Korpi, K., et al. (223) Wrist-Worn Device Combining PPG and ECG Can Be Reliably Used for Atrial Fibrillation Detection in an Outpatient Setting. Frontiers in Cardiovascular Medicine, 10, Article 1100127. [Google Scholar] [CrossRef] [PubMed]
|
|
[34]
|
Guo, Y., Wang, H., Zhang, H., et al. (2019) Mobile Photoplethysmographic Technology to Detect Atrial Fibrillation. Journal of the American College of Cardiology, 74, 2365-2375. [Google Scholar] [CrossRef] [PubMed]
|
|
[35]
|
Perez, M.V., Mahaffey, K.W., Hedlin, H., et al. (2019) Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation. New England Journal of Medicine, 381, 1909-1917. [Google Scholar] [CrossRef]
|
|
[36]
|
Lubitz, S.A., Faranesh, A.Z., Atlas, S.J., et al. (2021) Rationale and Design of a Large Population Study to Validate Software for the Assessment of Atrial Fibrillation from Data Acquired by a Consumer Tracker or Smartwatch: The Fitbit Heart Study. American Heart Journal, 238, 16-26. [Google Scholar] [CrossRef] [PubMed]
|
|
[37]
|
余超, 周伟, 王涛, 等. 可穿戴设备支持心房颤动人群筛查与管理研究进展[J]. 中国全科医学, 2023, 26(1): 113-117.
|
|
[38]
|
Curry, S.J., Krist, A.H., Owens, D.K., et al. (2018) Screening for Atrial Fibrillation with Electrocardiography: US Preventive Services Task Force Recommendation Statement. JAMA, 320, 478-484. [Google Scholar] [CrossRef] [PubMed]
|
|
[39]
|
Davidson, K.W., Barry, M.J., Mangione, C.M., et al. (2022) Screening for Atrial Fibrillation: US Preventive Services Task Force Recommendation Statement. JAMA, 327, 360-367. [Google Scholar] [CrossRef] [PubMed]
|
|
[40]
|
Chikwetu, L., Miao, Y., Woldetensae, M.K., et al. (2023) Does Deidentification of Data from Wearable Devices Give Us a False Sense of Security? A Systematic Review. Lancet Digit Health, 5, E239-E247. [Google Scholar] [CrossRef]
|
|
[41]
|
Sehrawat, O., Kashou, A.H. and Noseworthy, P.A. (2022) Artificial Intelligence and Atrial Fibrillation. Journal of Cardiovascular Electrophysiology, 33, 1932-1943. [Google Scholar] [CrossRef] [PubMed]
|