Rotor pivot point identification with intrinsic mode function complexity index using empirical mode decomposition

Shivaram P. Arunachalam, Siva Mulpuru, Paul Andrew Friedman, Elena G. Tolkacheva

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

1 Scopus citations

Abstract

Atrial Fibrillation (AF), a most common cardiac arrhythmia affects more than 2.3 million people in the US and associated with increased risk of stroke, heart failure and death. Cather ablation to treat paroxysmal AF patients is somewhat successful with challenges remaining to accurately identify the active sites for persistent AF patients which may occur outside the pulmonary vein (PV) region due to inadequate cardiac mapping systems. In this work, the authors propose an Empirical Mode Decomposition (EMD) approach using multi-scale entropy estimates of the intrinsic mode functions as a complexity measure to accurately identify pivot point of the rotor that were induced in ex-vivo isolated rabbit heart with Ventricular Tachycardia (VT). The new approach using EMD demonstrated successful identification of the rotor core region providing better contrast relative to the periphery region. Validation of the EMD approach using intra-atrial electrograms from paroxysmal and persistent AF patients with rotors is required to accurately identify the rotor pivot point to guide AF ablation.

Original languageEnglish (US)
Title of host publication2016 IEEE EMBS International Student Conference: Expanding the Boundaries of Biomedical Engineering and Healthcare, ISC 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509009350
DOIs
StatePublished - Jul 8 2016
Event2016 IEEE EMBS International Student Conference, ISC 2016 - Ottawa, Canada
Duration: May 29 2016May 31 2016

Other

Other2016 IEEE EMBS International Student Conference, ISC 2016
CountryCanada
CityOttawa
Period5/29/165/31/16

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ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Health(social science)
  • Biomedical Engineering

Cite this

Arunachalam, S. P., Mulpuru, S., Friedman, P. A., & Tolkacheva, E. G. (2016). Rotor pivot point identification with intrinsic mode function complexity index using empirical mode decomposition. In 2016 IEEE EMBS International Student Conference: Expanding the Boundaries of Biomedical Engineering and Healthcare, ISC 2016 - Proceedings [7508597] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBSISC.2016.7508597