NERULA: A Dual-Pathway Self-Supervised Learning Framework for Electrocardiogram Signal Analysis

TL;DR

NERULA is a novel self-supervised learning framework for single-lead ECG analysis that combines reconstruction and non-contrastive pathways, improving robustness and performance in various cardiac and activity recognition tasks.

Contribution

It introduces a dual-pathway architecture with a 50% masking strategy, integrating generative and discriminative learning for enhanced ECG feature extraction.

Findings

Outperforms state-of-the-art self-supervised benchmarks

Achieves superior results in arrhythmia and gender classification

Demonstrates robustness against incomplete or corrupted data

Abstract

Electrocardiogram (ECG) signals are critical for diagnosing heart conditions and capturing detailed cardiac patterns. As wearable single-lead ECG devices become more common, efficient analysis methods are essential. We present NERULA (Non-contrastive ECG and Reconstruction Unsupervised Learning Algorithm), a self-supervised framework designed for single-lead ECG signals. NERULA's dual-pathway architecture combines ECG reconstruction and non-contrastive learning to extract detailed cardiac features. Our 50% masking strategy, using both masked and inverse-masked signals, enhances model robustness against real-world incomplete or corrupted data. The non-contrastive pathway aligns representations of masked and inverse-masked signals, while the reconstruction pathway comprehends and reconstructs missing features. We show that combining generative and discriminative paths into the training spectrum leads to better results by outperforming state-of-the-art self-supervised learning benchmarks in various tasks, demonstrating superior performance in ECG analysis, including arrhythmia classification, gender classification, age regression, and human activity recognition. NERULA's dual-pathway design offers a robust, efficient solution for comprehensive ECG signal interpretation.