NERULA: A Dual-Pathway Self-Supervised Learning Framework for Electrocardiogram Signal Analysis
<|reference_start|>NERULA: A Dual-Pathway Self-Supervised Learning Framework for Electrocardiogram Signal Analysis: 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.<|reference_end|>
@article{manimaran2024nerula:,
    title={NERULA: A Dual-Pathway Self-Supervised Learning Framework for
  Electrocardiogram Signal Analysis},
    author={Gouthamaan Manimaran, Sadasivan Puthusserypady, Helena Dom'inguez,
  Adrian Atienza, Jakob E. Bardram},
    journal={arXiv preprint arXiv:2405.19348},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.19348},
    primaryClass={eess.SP cs.LG}
}