Non-linear frequency modulation and phase coupling in ECG during ventricular fibrillation

Abhijit Patwardhan, Sachin Moghe, Ke Wang, Fabio Leonelli

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In the present study we quantified the degree of changes in the dominant frequencies during ventricular fibrillation using Wigner transforms. We used auto-bispectra to quantify phase coupling between the different dominant rhythms. Results of 427 trials in 10 dogs showed substantial frequency modulation within ECGs, upto ±14% from the mean dominant frequency. Bispectra were non-zero in more than 99% of the trials (p<0.05) with peaks around the dominant frequencies (8.8 Hz). These results show that during ventricular fibrillation there is substantial frequency modulation of the dominant rhythms and these rhythms are phase coupled. It is possible that the frequency modulation produces quadratic terms which lead to the observed phase coupling. The assessment of degree of frequency modulation and phase coupling may be useful to quantify the degree of organization or of polymorphism.

Original languageEnglish
Title of host publicationAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Pages307
Number of pages1
StatePublished - 1999
EventProceedings of the 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Fall Meeting of the Biomedical Engineering Society (1st Joint BMES / EMBS) - Atlanta, GA, USA
Duration: Oct 13 1999Oct 16 1999

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume1
ISSN (Print)0589-1019

Conference

ConferenceProceedings of the 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Fall Meeting of the Biomedical Engineering Society (1st Joint BMES / EMBS)
CityAtlanta, GA, USA
Period10/13/9910/16/99

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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