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Entropy 2014, 16(9), 4839-4854; doi:10.3390/e16094839

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

1
Department of Cardiothoracic, Vascular Anesthesia and Intensive Care, IRCCS Policlinico San Donato, Via Morandi 30, 20097 San Donato Milanese, Milan, Italy
2
Department of Anesthesia and Intensive Care Unit, Humanitas Clinical and Research Center, Via Manzoni 56, 20089 Rozzano, Italy
3
IRCCS Maugeri Foundation, 20138 Milan, Italy
4
Center for Cardiac Arrhythmias of Genetic Origin, IRCCS Istituto Auxologico Italiano, Centro Diagnostico San Carlo, Via Pier Lombardo 22, 20135 Milan, Italy
5
Department of Molecular Medicine, University of Pavia, 27100 Pavia, Italy
6
Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
7
Department of Medicine and Pharmacology, Vanderbilt University, Nashville 37232, TN, USA
8
Department of Internal Medicine, University of Stellenbosch, Matieland, 7602, Stellenbosch, South Africa
9
Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
10
Institute of Human Genetics, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
11
Department of Biomedical Sciences for Health, University of Milan, Via R. Galeazzi 4, 20161 Milan, Italy
12
IRCCS Galeazzi Orthopedic Institute, Via R. Galeazzi 4, 20161 Milan, Italy
*
Author to whom correspondence should be addressed.
Received: 11 July 2014 / Revised: 20 August 2014 / Accepted: 1 September 2014 / Published: 5 September 2014
(This article belongs to the Special Issue Entropy and Cardiac Physics)
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Abstract

Entropy-based complexity of cardiovascular variability at short time scales is largely dependent on the noise and/or action of neural circuits operating at high frequencies. This study proposes a technique for canceling fast variations from cardiovascular variability, thus limiting the effect of these overwhelming influences on entropy-based complexity. The low-pass filtering approach is based on the computation of the fastest intrinsic mode function via empirical mode decomposition (EMD) and its subtraction from the original variability. Sample entropy was exploited to estimate complexity. The procedure was applied to heart period (HP) and QT (interval from Q-wave onset to T-wave end) variability derived from 24-hour Holter recordings in 14 non-mutation carriers (NMCs) and 34 mutation carriers (MCs) subdivided into 11 asymptomatic MCs (AMCs) and 23 symptomatic MCs (SMCs). All individuals belonged to the same family developing long QT syndrome type 1 (LQT1) via KCNQ1-A341V mutation. We found that complexity indexes computed over EMD-filtered QT variability differentiated AMCs from NMCs and detected the effect of beta-blocker therapy, while complexity indexes calculated over EMD-filtered HP variability separated AMCs from SMCs. The EMD-based filtering method enhanced features of the cardiovascular control that otherwise would have remained hidden by the dominant presence of noise and/or fast physiological variations, thus improving classification in LQT1. View Full-Text
Keywords: heart rate variability; LQT1; EMD; sample entropy; KCNQ1-A341V mutation; beta-blocker therapy; autonomic nervous system; cardiovascular control heart rate variability; LQT1; EMD; sample entropy; KCNQ1-A341V mutation; beta-blocker therapy; autonomic nervous system; cardiovascular control
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Bari, V.; Marchi, A.; De Maria, B.; Girardengo, G.; George, A.L., Jr; Brink, P.A.; Cerutti, S.; Crotti, L.; Schwartz, P.J.; Porta, A. 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. Entropy 2014, 16, 4839-4854.

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