Abstract
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.
Abstract (translated)
持续监测无创性皮肤交感神经活动（SKNA）有望帮助我们理解在各种生理和病理条件下交感神经系统（SNS）的动态变化。然而，肌肉噪声伪影在准确分析SKNA时构成了一大挑战，尤其是在现实场景中。本研究提出采用深度卷积神经网络（CNN）的方法来检测并去除通过ECG电极收集到的SKNA记录中的肌肉噪声。十二名健康受试者参与了涉及认知压力诱发和自愿肌肉运动控制实验协议，并在此期间采集了SKNA数据。功率谱分析显示，在500-1000 Hz的SKNA频率范围内存在显著的肌肉噪声干扰。研究团队训练了一个2D CNN模型，通过对数据段的频谱图进行分类来识别基线、由压力诱发的SKNA以及受肌肉噪声污染的时间段，所有受试者平均准确率达到89.85%。我们的发现强调了在实现精确SKNA监测方面解决肌肉噪声问题的重要性，并朝着开发适用于现实世界应用的可穿戴SKNA传感器迈进了一步。
URL
https://arxiv.org/abs/2410.21319
