NERULA: A Dual-Pathway Self-Supervised Learning Framework for Electrocardiogram Signal Analysis
This provides a robust solution for interpreting ECG signals in healthcare applications, but it appears incremental as it builds on existing self-supervised learning methods.
The paper tackled the problem of analyzing single-lead ECG signals from wearable devices by introducing NERULA, a self-supervised learning framework that combines reconstruction and non-contrastive learning pathways, and it outperformed state-of-the-art benchmarks in tasks like arrhythmia classification, gender classification, age regression, and human activity recognition.
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.