Iterative method to detect atrial activations and measure cycle length from electrograms during atrial fibrillation

Jason Ng, Vinod Sehgal, Justin K. Ng, David Gordon, Jeffrey J. Goldberger

Research output: Contribution to journalArticle

20 Scopus citations

Abstract

Atrial fibrillation (AF) electrograms are characterized by varying morphologies, amplitudes, and cycle lengths (CLs), presenting a challenge for automated detection of individual activations and the activation rate. In this study, we evaluate an algorithm to detect activations and measure CLs from AF electrograms. This algorithm iteratively adjusts the detection threshold level until the mean CL converges with the median CL to detect all individual activations. A total of 291 AF electrogram recordings from 13 patients (11 male, 58 ± 10 years old) undergoing AF ablation were obtained. Using manual markings by two independent reviewers as the standard, we compared the cycle length iteration algorithm with a fixed threshold algorithm and dominant frequency (DF) for the estimation of CL. At segment lengths of 10 s, when comparing the algorithm detected to the manually detected activation, the undersensing, oversensing, and total discrepancy rates were 2.4%, 4.6%, and 7.0%, respectively, and with absolute differences in mean and median CLs were 7.9 ± 9.6 ms and 5.6 ± 6.8 ms, respectively. These results outperformed DF and fixed threshold-based measurements. This robust method can be used for CL measurements in either real-time and offline settings and may be useful in the mapping of AF.

Original languageEnglish (US)
Article number6657784
Pages (from-to)273-278
Number of pages6
JournalIEEE Transactions on Biomedical Engineering
Volume61
Issue number2
DOIs
StatePublished - Feb 1 2014
Externally publishedYes

Keywords

  • Biomedical signal processing
  • cardiology
  • electrocardiography
  • fibrillation

ASJC Scopus subject areas

  • Biomedical Engineering

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