Towards Continuous Skin Sympathetic Nerve Activity Monitoring: Removing Muscle Noise
This work addresses a challenge for researchers and clinicians aiming to monitor sympathetic nervous system dynamics in real-world settings, representing an incremental improvement in noise removal for SKNA analysis.
The study tackled the problem of muscle noise artifacts interfering with skin sympathetic nerve activity (SKNA) monitoring by proposing a deep convolutional neural network to detect and remove this noise, achieving an average accuracy of 89.85% in classification across subjects.
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.