AI 中文总结
研究可穿戴应用中ECG去噪方法，比较基于模型（如EMD变体、DWT）和基于深度学习（如SDAE、PINN）的技术，在实际采集数据上用多种指标评估，发现DL方法形态重建优，DWT噪声抑制强，二者优势互补。
AI 中文摘要
在可穿戴和太空环境中进行可靠的心电图（ECG）监测，需要对受非平稳肌电图（EMG）干扰的信号进行有效去噪。本文对基于模型和基于深度学习的去噪技术进行了比较评估，用于在实际条件下获取的上臂ECG记录。基于模型的方法包括三种经验模态分解（EMD）变体和一种离散小波变换（DWT）方法，而深度学习（DL）方面则由堆叠去噪自动编码器（SDAE）和物理信息神经网络（PINN）代表。所有方法都在放松和自愿肌肉收缩条件下的实际采集数据上进行评估，使用均方根误差（RMSE）、皮尔逊相关性和峰峰值信噪比（PPSNR）作为性能指标。结果揭示了一个基本的权衡：深度学习方法实现了更好的形态重建，而DWT提供了最强的噪声抑制，突出了可穿戴心脏监测应用的互补优势。
英文摘要
Reliable electrocardiogram (ECG) monitoring in wearable and space environments requires effective denoising of signals corrupted by non-stationary electromyogram (EMG) interference. This paper presents a comparative evaluation of model-based and DL-based denoising techniques for upper-arm ECG recordings acquired under real conditions. The model-based methods include three empirical mode decomposition (EMD) variants and a discrete wavelet transform (DWT) approach, while the deep learning (DL) side is represented by a stacked denoising autoencoder (SDAE) and a physics-informed neural network (PINN). All methods are evaluated on real acquisitions under both relaxed and voluntary muscle contraction conditions, using root mean squared error (RMSE), Pearson correlation, and peak-to-peak signal-to-noise ratio (PPSNR) as performance metrics. Results reveal a fundamental trade-off: DL methods achieve superior morphological reconstruction, while DWT provides the strongest noise suppression, highlighting complementary strengths for wearable cardiac monitoring applications.
CommentsAccepted for presentation at 14th Annual IEEE International Conference on Wireless for Space and Extreme Environments (WISEE 2026)
