Machine learning for ranking f-wave extraction methods in single-lead ECGs

Noam Ben-Moshe, Shany Biton Brimer, Kenta Tsutsui, Mahmoud Suleiman, Leif Sörnmo, Joachim A. Behar

Research output: Contribution to journalArticlepeer-review

Abstract

Introduction: The presence of fibrillatory waves (f-waves) is important in the diagnosis of atrial fibrillation (AF), which has motivated the development of methods for f-wave extraction. We propose a novel approach to benchmarking methods designed for single-lead ECG analysis, building on the hypothesis that better-performing AF classification using features computed from the extracted f-waves implies better-performing extraction. The approach is well-suited for processing large Holter data sets annotated with respect to the presence of AF. Methods: Three data sets with a total of 300 two- or three-lead Holter recordings, performed in the USA, Israel and Japan, were used as well as a simulated single-lead data set. Four existing extraction methods based on either average beat subtraction or principal component analysis (PCA) were evaluated. A random forest classifier was used for window-based AF classification. Performance was measured by the area under the receiver operating characteristic (AUROC). Results: The best performance was found for PCA-based extraction, resulting in AUROCs in the ranges 0.80–0.85, 0.66–0.80, and 0.87–0.92 for the data sets from USA, Israel, and Japan, respectively, when analyzed across leads; the AUROC of the simulated single-lead, noisy data set was 0.98. Conclusions: This study provides a novel approach to evaluating the performance of f-wave extraction methods, offering the advantage of not using ground truth f-waves for evaluation, thus being able to leverage real data sets for evaluation. The code is made open source at github.com/noambenmoshe/fwave.

Original languageEnglish
Article number106817
JournalBiomedical Signal Processing and Control
Volume99
DOIs
StateSubmitted - 1 Jan 2025

Keywords

  • Atrial fibrillation
  • Biomedical signal processing
  • Machine learning
  • Performance evaluation
  • f-wave extraction

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

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