TolerantECG: A Foundation Model for Imperfect Electrocardiogram

TL;DR

TolerantECG is a robust foundation model for ECG analysis that maintains high diagnostic performance despite noise or missing leads, leveraging contrastive and self-supervised learning techniques.

Contribution

It introduces TolerantECG, a novel ECG foundation model trained to handle noisy and incomplete signals using combined contrastive and self-supervised learning.

Findings

Outperforms existing models on PTB-XL dataset across various conditions.

Achieves highest performance on MIT-BIH Arrhythmia Database.

Demonstrates robustness to noise and lead-missing scenarios.

Abstract

The electrocardiogram (ECG) is an essential and effective tool for diagnosing heart diseases. However, its effectiveness can be compromised by noise or unavailability of one or more leads of the standard 12-lead recordings, resulting in diagnostic errors or uncertainty. To address these challenges, we propose TolerantECG, a foundation model for ECG signals that is robust to noise and capable of functioning with arbitrary subsets of the standard 12-lead ECG. TolerantECG training combines contrastive and self-supervised learning frameworks to jointly learn ECG signal representations alongside their corresponding knowledge-retrieval-based text report descriptions and corrupted or lead-missing signals. Comprehensive benchmarking results demonstrate that TolerantECG consistently ranks as the best or second-best performer across various ECG signal conditions and class levels in the PTB-XL dataset, and achieves the highest performance on the MIT-BIH Arrhythmia Database.