用深度学习去除心电图中的肌肉干扰，保留神经信号用于压力检测。
Autoencoder-Based Denoising of Muscle Artifacts in ECG to Preserve Skin Nerve Activity (SKNA) for Cognitive Stress Detection
- 用一维卷积自编码器加LSTM瓶颈结构重建干净神经信号。
- 信噪比提升9.65 dB，与干净信号相关性达0.72，准确率超91%。
- 适合在运动环境中做真实场景下的压力神经监测。
交感神经系统（SNS）在调节应激反应和维持生理稳定中起核心作用，其失调与心血管疾病、焦虑障碍等多种病症相关。从高频心电图（ECG）中提取的皮肤神经活动（SKNA）为非侵入式观测SNS动态提供了窗口，但极易受肌电（EMG）干扰。传统基于固定频段（如500–1000 Hz）带通滤波的方法在持续肌肉活动时因EMG与SKNA频谱重叠而失效。本文提出一种轻量级一维卷积自编码器结合长短期记忆（LSTM）瓶颈的去噪方法，从被EMG污染的记录中重建干净的SKNA。基于认知压力实验的干净SKNA数据与混沌肌肉刺激产生的EMG噪声，模拟了-4 dB和-8 dB信噪比的真实干扰水平，并在留一被试交叉验证框架下训练模型。结果表明，该方法将信噪比提升最高达9.65 dB，跨相关性由0.40增至0.72，恢复了近似干净的爆发型SKNA特征（AUROC ≥ 0.96）。在严重噪声条件下，基线与交感激活（认知压力）分类准确率达91–98%，与干净数据相当。证明深度学习重构可在强EMG干扰下保留关键生理爆发特征，实现自然环境中更鲁棒的SKNA监测。
原文摘要 · Abstract (English)
The sympathetic nervous system (SNS) plays a central role in regulating the body's responses to stress and maintaining physiological stability. Its dysregulation is associated with a wide range of conditions, from cardiovascular disease to anxiety disorders. Skin nerve activity (SKNA) extracted from high-frequency electrocardiogram (ECG) recordings provides a noninvasive window into SNS dynamics, but its measurement is highly susceptible to electromyographic (EMG) contamination. Traditional preprocessing based on bandpass filtering within a fixed range (e.g., 500--1000 Hz) is susceptible to overlapping EMG and SKNA spectral components, especially during sustained muscle activity. We present a denoising approach using a lightweight one-dimensional convolutional autoencoder with a long short-term memory (LSTM) bottleneck to reconstruct clean SKNA from EMG-contaminated recordings. Using clean ECG-derived SKNA data from cognitive stress experiments and EMG noise from chaotic muscle stimulation recordings, we simulated contamination at realistic noise levels (--4 dB, --8 dB signal-to-noise ratio) and trained the model in the leave-one-subject-out cross-validation framework. The method improved signal-to-noise ratio by up to 9.65 dB, increased cross correlation with clean SKNA from 0.40 to 0.72, and restored burst-based SKNA features to near-clean discriminability (AUROC $\geq$ 0.96). Classification of baseline versus sympathetic stimulation (cognitive stress) conditions reached accuracies of 91--98\% across severe noise levels, comparable to clean data. These results demonstrate that deep learning--based reconstruction can preserve physiologically relevant sympathetic bursts during substantial EMG interference, enabling more robust SKNA monitoring in naturalistic, movement-rich environments.
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