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-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 the...
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| Format: | Article |
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MDPI AG
2014-09-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/16/9/4839 |
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| author | Vlasta Bari Andrea Marchi Beatrice De Maria Giulia Girardengo Alfred L. George Paul A. Brink Sergio Cerutti Lia Crotti Peter J. Schwartz Alberto Porta |
| author_facet | Vlasta Bari Andrea Marchi Beatrice De Maria Giulia Girardengo Alfred L. George Paul A. Brink Sergio Cerutti Lia Crotti Peter J. Schwartz Alberto Porta |
| author_sort | Vlasta Bari |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T18:02:47Z |
| format | Article |
| id | doaj.art-20bda9fe96f944d795b717aad00a9904 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T18:02:47Z |
| publishDate | 2014-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-20bda9fe96f944d795b717aad00a99042022-12-22T04:10:25ZengMDPI AGEntropy1099-43002014-09-011694839485410.3390/e16094839e16094839Low-Pass Filtering Approach via Empirical Mode Decomposition Improves Short-Scale Entropy-Based Complexity Estimation of QT Interval Variability in Long QT Syndrome Type 1 PatientsVlasta Bari0Andrea Marchi1Beatrice De Maria2Giulia Girardengo3Alfred L. George4Paul A. Brink5Sergio Cerutti6Lia Crotti7Peter J. Schwartz8Alberto Porta9Department of Cardiothoracic, Vascular Anesthesia and Intensive Care, IRCCS Policlinico San Donato, Via Morandi 30, 20097 San Donato Milanese, Milan, ItalyDepartment of Anesthesia and Intensive Care Unit, Humanitas Clinical and Research Center, Via Manzoni 56, 20089 Rozzano, ItalyIRCCS Maugeri Foundation, 20138 Milan, ItalyCenter for Cardiac Arrhythmias of Genetic Origin, IRCCS Istituto Auxologico Italiano, Centro Diagnostico San Carlo, Via Pier Lombardo 22, 20135 Milan, ItalyDepartment of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USADepartment of Internal Medicine, University of Stellenbosch, Matieland, 7602, Stellenbosch, South AfricaDepartment of Electronics, Information, and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, ItalyCenter for Cardiac Arrhythmias of Genetic Origin, IRCCS Istituto Auxologico Italiano, Centro Diagnostico San Carlo, Via Pier Lombardo 22, 20135 Milan, ItalyCenter for Cardiac Arrhythmias of Genetic Origin, IRCCS Istituto Auxologico Italiano, Centro Diagnostico San Carlo, Via Pier Lombardo 22, 20135 Milan, ItalyDepartment of Biomedical Sciences for Health, University of Milan, Via R. Galeazzi 4, 20161 Milan, ItalyEntropy-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.http://www.mdpi.com/1099-4300/16/9/4839heart rate variabilityLQT1EMDsample entropyKCNQ1-A341V mutationbeta-blocker therapyautonomic nervous systemcardiovascular control |
| spellingShingle | Vlasta Bari Andrea Marchi Beatrice De Maria Giulia Girardengo Alfred L. George Paul A. Brink Sergio Cerutti Lia Crotti Peter J. Schwartz Alberto Porta 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 heart rate variability LQT1 EMD sample entropy KCNQ1-A341V mutation beta-blocker therapy autonomic nervous system cardiovascular control |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | heart rate variability LQT1 EMD sample entropy KCNQ1-A341V mutation beta-blocker therapy autonomic nervous system cardiovascular control |
| url | http://www.mdpi.com/1099-4300/16/9/4839 |
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