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Low-Pass Filtering Approach via Empirical Mode Decomposition Improves Short-Scale Entropy-Based Complexity Estimation of QT Interval Variability in Long QT Syndrome Type 1 Patients.

, , , , , , , , , and . Entropy, 16 (9): 4839-4854 (2014)

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Managing Complex Scheduling Problems with Dynamic and Hybrid Constraints.. University of Michigan, USA, (2007)An impedance-guided intra arterial catheter., , , and . EMBC, page 5323-5326. IEEE, (2013)Multiscale Complexity Analysis of Short QT Interval Variability Series Stratifies the Arrhythmic Risk of Long QT Syndrome Type 1 Patients., , , , , , , , and . CinC, page 1-4. www.cinc.org, (2018)A Refined Multiscale Self-Entropy Approach for the Assessment of Cardiac Control Complexity: Application to Long QT Syndrome Type 1 Patients., , , , , , , and . Entropy, 17 (11): 7768-7785 (2015)Refined multiscale entropy analysis of heart period and QT interval variabilities in long QT syndrome type-1 patients., , , , , , , , , and 3 other author(s). EMBC, page 5554-5557. IEEE, (2013)Frequency Domain Heart Period and QT Interval Variability Markers Are Linked to Arrhythmic Risk in Long QT Syndrome Type 2., , , , , , and . CinC, page 1-4. IEEE, (2020)Truth Maintenance with Cognitive Dissonance. University of Mariland at College Park, (2001)Low-Pass Filtering Approach via Empirical Mode Decomposition Improves Short-Scale Entropy-Based Complexity Estimation of QT Interval Variability in Long QT Syndrome Type 1 Patients., , , , , , , , , and . Entropy, 16 (9): 4839-4854 (2014)Time, frequency and information domain analysis of heart period and QT variability in asymptomatic long QT syndrome type 2 patients., , , , , , , and . EMBC, page 294-297. IEEE, (2015)Filtering approach based on empirical mode decomposition improves the assessment of short scale complexity in long QT syndrome type 1 population., , , , , , , , and . EMBC, page 6671-6674. IEEE, (2014)