Machine Learning for Ranking f-wave Extraction Methods in Single-Lead ECGs

TL;DR

This paper introduces a new benchmarking approach for f-wave extraction methods in single-lead ECGs, using AF classification performance as a proxy, and demonstrates PCA-based methods outperform others across multiple datasets.

Contribution

The study proposes a ground-truth-independent benchmarking framework for f-wave extraction methods based on AF classification accuracy, validated on diverse real and simulated datasets.

Findings

PCA-based extraction achieved highest AUROC scores across datasets.

The approach enables evaluation without needing ground truth f-waves.

Open-source code is provided for reproducibility.

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 0.77--0.83, 0.62--0.78, and 0.87--0.89 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 open source (following publication).