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A non-invasive method to predict electrical cardioversion outcome of persistent atrial fibrillation.

, and . Medical Biol. Eng. Comput., 46 (7): 625-635 (2008)

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Detection of Emotions from Electroencephalographic Recordings by Means of a Nonlinear Functional Connectivity Measure., , , and . IWANN (1), volume 12861 of Lecture Notes in Computer Science, page 242-252. Springer, (2021)Wavelet Entropy Automatically Detects Episodes of Atrial Fibrillation from Single-Lead Electrocardiograms., , , and . Entropy, 17 (9): 6179-6199 (2015)Combined Nonlinear Analysis of Atrial and Ventricular Series for Automated Screening of Atrial Fibrillation., , , and . Complex., (2017)Surface ECG organization analysis to predict paroxysmal atrial fibrillation termination., and . Comput. Biol. Medicine, 39 (8): 697-706 (2009)A Deep Learning Solution for Automatized Interpretation of 12-Lead ECGs., , , and . CinC, page 1-4. IEEE, (2020)Refined Multiscale Entropy Predicts Early Failure in Electrical Cardioversion of Atrial Fibrillation., , , , , and . CinC, page 1-4. IEEE, (2020)Linear and Nonlinear Correlations Between Surface and Invasive Atrial Activation Features in Catheter Ablation of Paroxysmal Atrial Fibrillation., , , , , and . CinC, page 1-4. IEEE, (2021)Hidden Hazards Beneath Cross-Validation Methods in Machine Learning-Based Sleep Apnea Detection., , , , and . CinC, page 1-4. IEEE, (2022)Cuffless Hypertension Risk Assessment and the Significance of Calibration., , , , , and . CinC, page 1-4. IEEE, (2022)Generalized Hurst Exponents as a Tool to Estimate Atrial Fibrillation Organization from the Surface ECG., , and . CinC, page 1199-1202. www.cinc.org, (2013)