A Data-Driven Preprocessing Framework for Atrial Fibrillation Intracardiac Electrocardiogram Analysis

Xiangzhen Kong, Vasanth Ravikumar, Siva K. Mulpuru, Henri Roukoz, Elena G. Tolkacheva

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Atrial Fibrillation (AF) is the most common cardiac arrhythmia. Signal-processing approaches are widely used for the analysis of intracardiac electrograms (iEGMs), which are collected during catheter ablation from patients with AF. In order to identify possible targets for ablation therapy, dominant frequency (DF) is widely used and incorporated in electroanatomical mapping systems. Recently, a more robust measure, multiscale frequency (MSF), for iEGM data analysis was adopted and validated. However, before completing any iEGM analysis, a suitable bandpass (BP) filter must be applied to remove noise. Currently, no clear guidelines for BP filter characteristics exist. The lower bound of the BP filter is usually set to 3–5 Hz, while the upper bound ((Formula presented.)) of the BP filter varies from 15 Hz to 50 Hz according to many researchers. This large range of (Formula presented.) subsequently affects the efficiency of further analysis. In this paper, we aimed to develop a data-driven preprocessing framework for iEGM analysis, and validate it based on DF and MSF techniques. To achieve this goal, we optimized the (Formula presented.) using a data-driven approach (DBSCAN clustering) and demonstrated the effects of different (Formula presented.) on subsequent DF and MSF analysis of clinically recorded iEGMs from patients with AF. Our results demonstrated that our preprocessing framework with (Formula presented.) = 15 Hz has the best performance in terms of the highest Dunn index. We further demonstrated that the removal of noisy and contact-loss leads is necessary for performing correct data iEGMs data analysis.

Original languageEnglish (US)
Article number332
JournalEntropy
Volume25
Issue number2
DOIs
StatePublished - Feb 2023

Bibliographical note

Funding Information:
This work was supported by the Minnesota Partnership for Biotechnology and Medical Genomics (MNP #21.30) to E.G.T. and Data Sciences Initiative fellowship from the University of Minnesota to X.K.

Publisher Copyright:
© 2023 by the authors.

Keywords

  • DBSCAN
  • Pearson’s correlation
  • atrial fibrillation
  • bandpass filter
  • catheter ablation
  • earth mover’s distance
  • intracardiac electrograms
  • multiscale frequency

PubMed: MeSH publication types

  • Journal Article

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