NERULA：一种用于心电图信号分析的双路径自监督学习框架
NERULA: A Dual-Pathway Self-Supervised Learning Framework for Electrocardiogram Signal Analysis
AI总结：
本文提出一种用于单导联心电图信号分析的双路径自监督学习框架NERULA，通过结合非对比学习与ECG重建及50%掩码策略，在心律失常分类等多项任务中优于现有基准，提供鲁棒高效的信号解释方案。
摘要：
心电图（ECG）信号对于诊断心脏疾病和捕捉详细的心脏模式至关重要。随着可穿戴单导联ECG设备的普及，高效的分析方法变得必不可少。我们提出了NERULA（非对比ECG与重建无监督学习算法），这是一种专为单导联ECG信号设计的自监督框架。NERULA的双路径架构结合了ECG重建和非对比学习，以提取详细的心脏特征。我们采用50%的掩码策略，同时使用掩码和逆掩码信号，增强了模型对现实世界中不完整或损坏数据的鲁棒性。非对比路径对齐掩码和逆掩码信号的表示，而重建路径则理解并重建缺失的特征。我们展示了将生成和判别路径结合到训练范围内会带来更好的结果，它在各种任务中优于最先进的自监督学习基准，在ECG分析中展现出卓越的性能，包括心律失常分类、性别分类、年龄回归和人类活动识别。NERULA的双路径设计为全面的ECG信号解释提供了鲁棒且高效的解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
