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Optimizing the Automated Detection of Atrial Fibrillation Episodes in Long-term Recording Instrumentation.

, , , , , and . MIXDES, page 460-464. IEEE, (2018)

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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.Selected design issues of the medical cyber-physical system for telemonitoring pregnancy at home., , , , , and . Microprocess. Microsystems, (2016)Robust extraction of fuzzy rules with artificial neural network based on fuzzy inference system., , , , and . Int. J. Intell. Inf. Database Syst., 6 (1): 77-92 (2012)Prediction of Newborn Sex with Neural Networks Approach to Fetal Cardiotocograms Classification., , , , and . Information Technologies in Biomedicine, volume 47 of Advances in Soft Computing, Springer, (2008)Two-Step Analysis of the Fetal Heart Rate Signal as a Predictor of Distress., , , and . ACIIDS (2), volume 7197 of Lecture Notes in Computer Science, page 431-438. Springer, (2012)Baseline and Acceleration Episodes - Clinically Significant Nonstationarities in FHR Signal: Part I. Coefficients of Inconsistency., , and . CORES, volume 30 of Advances in Soft Computing, page 527-534. Springer, (2005)A dedicated software environment for quantitative evaluation of various QRS detectors., , and . MIXDES, page 487-491. IEEE, (2016)Recognition of Atrial Fibrilation Episodes in Heart Rate Variability Signals Using a Machine Learning Approach., , , , , , , , and . MIXDES, page 419-424. IEEE, (2019)The influence of coincidence of fetal and maternal QRS complexes on fetal heart rate reliability., , , , , and . Medical Biol. Eng. Comput., 44 (5): 393-403 (2006)Coping with Limitation of Bedside Measurement Instrumentation for Reliable Assessment of Fetal Heart Rate Variability., , and . Information Technologies in Biomedicine, volume 47 of Advances in Soft Computing, Springer, (2008)