Filtering approach based on empirical mode decomposition improves the assessment of short scale complexity in long QT syndrome type 1 population

Vlasta Bari, Andrea Marchi, Giulia Girardengo, Alfred L. George, Paul A. Brink, Sergio Cerutti, Lia Crotti, Peter J. Schwartz, Alberto Porta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This study assesses the complexity of heart period (HP) and QT variability series through sample entropy (SampEn) in long QT syndrome type 1 individuals. In order to improve signal-to-noise ratio SampEn was evaluated over the original series (SampEn0) and over the residual computed by subtracting the first oscillatory mode identified by empirical mode decomposition (SampEnEMD1R). HP and QT interval were continuously extracted during daytime (2:00-6:00 PM) from 24 hour Holter recordings in 14 non mutation carriers (NMCs) and 34 mutation carriers (MCs) subdivided in 11 asymptomatic (ASYMP) and 23 symptomatic (SYMP). Both NMCs and MCs belonged to the same family line. While SampEn0 did not show differences among the three groups, SampEnEMD1R assessed over the QT series significantly decreased in ASYMP subjects. SampEnEMD1R identified a possible factor (i.e. the lower short scale QT complexity) that might contribute to the different risk profile of the ASYMP group.

Original languageEnglish (US)
Title of host publication2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6671-6674
Number of pages4
ISBN (Electronic)9781424479290
DOIs
StatePublished - Jan 1 2014
Event2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States
Duration: Aug 26 2014Aug 30 2014

Other

Other2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
CountryUnited States
CityChicago
Period8/26/148/30/14

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications
  • Biomedical Engineering

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    Bari, V., Marchi, A., Girardengo, G., George, A. L., Brink, P. A., Cerutti, S., Crotti, L., Schwartz, P. J., & Porta, A. (2014). Filtering approach based on empirical mode decomposition improves the assessment of short scale complexity in long QT syndrome type 1 population. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 (pp. 6671-6674). [6945158] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2014.6945158