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Catheter ablation outcome prediction in persistent atrial fibrillation using weighted principal component analysis.

, , , , and . Biomed. Signal Process. Control., 8 (6): 958-968 (2013)

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Quantifying the PR Interval Pattern During Dynamic Exercise and Recovery., , , and . IEEE Trans. Biomed. Eng., 56 (11): 2675-2683 (2009)Time-varying analysis methods and models for the respiratory and cardiac system coupling in graded exercise., , , and . IEEE Trans. Biomed. Eng., 52 (11): 1921-1930 (2005)Spectral and spatiotemporal variability ECG parameters linked to catheter ablation outcome in persistent atrial fibrillation., , , , , , and . Comput. Biol. Medicine, (2017)Characterization of typical and atypical atrial flutter loops from the vectorcardiogram., , , , , and . EMBC, page 4976-4979. IEEE, (2011)Eigenvalue-based time delay estimation of repetitive biomedical signals., , , , , and . Digit. Signal Process., (2018)Catheter ablation outcome prediction in persistent atrial fibrillation using weighted principal component analysis., , , , and . Biomed. Signal Process. Control., 8 (6): 958-968 (2013)Autonomic Nervous System Effects on the Atrioventricular Conduction Time - Heart Period Relationship During Physical Exercise and Passive Recovery., , , and . CinC, page 1-4. www.cinc.org, (2018)Machine Learning Approach and Waves Synchronization Improvement for the Localization of Atrial Flutter Circuit Based on the 12-leads ECG., , and . CinC, page 1-4. IEEE, (2019)Non-Invasive Localization of Atrial Flutter Circuit Using Recurrence Quantification Analysis and Machine Learning., , , and . CinC, page 1-4. IEEE, (2019)Noninvasive Characterization of Short- and Long-Term Recurrence of Atrial Signals During Persistent Atrial Fibrillation., , , , , , , , and . CinC, www.cinc.org, (2017)