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Recognition of Atrial Fibrilation Episodes in Heart Rate Variability Signals Using a Machine Learning Approach.

, , , , , , , , and . MIXDES, page 419-424. IEEE, (2019)

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Control and signal processing software embedded in smart wristband monitor of silent atrial fibrillation., , , , and . MIXDES, page 585-590. IEEE, (2017)Electrical Activity of Uterus as Reliable Information on Contractions During Pregnancy and Labour., , , , , and . ITIB (1), volume 471 of Advances in Intelligent Systems and Computing, page 353-366. Springer, (2016)Classification of Uterine Electrical Activity Patterns for Early Detection of Preterm Birth., , , , and . CORES, volume 226 of Advances in Intelligent Systems and Computing, page 559-568. Springer, (2013)Analysis of nonstationarities in fetal heart rate signal: inconsistency measures of baselines using acceleration/deceleration patterns., , , , , and . ISSPA (2), page 9-12. IEEE, (2003)0-7803-7946-2.Recognition of Atrial Fibrilation Episodes in Heart Rate Variability Signals Using a Machine Learning Approach., , , , , , , , and . MIXDES, page 419-424. IEEE, (2019)Baseline and Acceleration Episodes - Clinically Significant Nonstationarities in FHR Signal: Part II. Indirect Comparison., , and . CORES, volume 30 of Advances in Soft Computing, page 535-542. Springer, (2005)Hardware design issues and functional requirements for smart wristband monitor of silent atrial fibrillation., , , and . MIXDES, page 596-600. IEEE, (2017)Telemonitoring of pregnant women at home - Biosignals acquisition and measurement., , , , and . MIXDES, page 83-87. IEEE, (2015)Combining ε-similar Fuzzy Rules for Efficient Classification of Cardiotocographic Signals., , , , and . MIXDES, page 213-217. IEEE, (2020)On ε-insensitive simplification of fuzzy rules for fetal distress assessment., , , , and . Expert Syst. Appl., (2021)