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

• URL: http://arxiv.org/abs/2405.19348v1
• Date: Tue, 21 May 2024 14:01:57 GMT
• Title: NERULA: A Dual-Pathway Self-Supervised Learning Framework for Electrocardiogram Signal Analysis
• Authors: Gouthamaan Manimaran, Sadasivan Puthusserypady, Helena Domínguez, Adrian Atienza, Jakob E. Bardram,
• Abstract summary: We present NERULA, 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. 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.
• Score: 5.8961928852930034
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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