用深度学习去除肌电干扰，让皮肤交感神经信号更准确。
Towards Continuous Skin Sympathetic Nerve Activity Monitoring: Removing Muscle Noise
- 用2D卷积神经网络分析频谱图，识别肌电噪声
- 在12人实验中达到89.85%的分类准确率
- 为可穿戴设备实现真实场景下的连续监测铺路
连续非侵入式皮肤交感神经活动（SKNA）监测有望揭示生理与病理条件下交感神经系统（SNS）的动态变化。然而，在真实场景中，肌肉噪声对SKNA分析构成挑战。本研究提出一种基于深度卷积神经网络（CNN）的方法，从通过心电电极获取的SKNA记录中检测并去除肌肉噪声。12名健康受试者参与了认知压力诱导和自主肌肉运动的控制实验，采集了SKNA数据。功率谱分析显示，肌肉噪声显著干扰了SKNA频段（500–1000 Hz）。训练一个2D CNN模型对数据片段的频谱图进行分类，分为基线、应激诱发的SKNA和肌电污染时段，所有受试者平均准确率达89.85%。研究强调了消除肌电噪声对准确监测SKNA的重要性，推动可穿戴式SKNA传感器在现实应用中的发展。
原文摘要 · Abstract (English)
Continuous monitoring of non-invasive skin sympathetic nerve activity (SKNA) holds promise for understanding the sympathetic nervous system (SNS) dynamics in various physiological and pathological conditions. However, muscle noise artifacts present a challenge in accurate SKNA analysis, particularly in real-life scenarios. This study proposes a deep convolutional neural network (CNN) approach to detect and remove muscle noise from SKNA recordings obtained via ECG electrodes. Twelve healthy participants underwent controlled experimental protocols involving cognitive stress induction and voluntary muscle movements, while collecting SKNA data. Power spectral analysis revealed significant muscle noise interference within the SKNA frequency band (500-1000 Hz). A 2D CNN model was trained on the spectrograms of the data segments to classify them into baseline, stress-induced SKNA, and muscle noise-contaminated periods, achieving an average accuracy of 89.85% across all subjects. Our findings underscore the importance of addressing muscle noise for accurate SKNA monitoring, advancing towards wearable SKNA sensors for real-world applications.
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