A Novel Approach to Characterize Dynamics of ECG-Derived Skin Nerve Activity via Time-Varying Spectral Analysis
Youngsun Kong, Farnoush Baghestani, William D'Angelo, I-Ping Chen, Ki, H. Chon

TL;DR
This paper introduces a time-varying spectral analysis method for ECG-derived skin nerve activity (SKNA) to improve the sensitivity and reliability of sympathetic nervous system assessment during stress and emotional states.
Contribution
It develops a novel TVSKNA index using time-frequency analysis, enhancing accuracy over existing SKNA analysis tools.
Findings
TVSKNA shows significant increases during SNS stimulation.
Higher sensitivity and lower variability compared to previous methods.
Effective in assessing sympathetic activity during stress and emotion.
Abstract
Assessment of the sympathetic nervous system (SNS) is one of the major approaches for studying affective states. Skin nerve activity (SKNA) derived from high-frequency components of electrocardiogram (ECG) signals has been a promising surrogate for assessing the SNS. However, current SKNA analysis tools have shown high variability across study protocols and experiments. Hence, we propose a time-varying spectral approach based on SKNA to assess the SNS with higher sensitivity and reliability. We collected ECG signals at a sampling frequency of 10 KHz from sixteen subjects who underwent various SNS stimulations. Our spectral analysis revealed that frequency bands between 150 - 1,000 Hz showed significant increases in power during SNS stimulations. Using this information, we developed a time-varying index of sympathetic function measurement based on SKNA, termed, Time-Varying Skin Nerve…
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Taxonomy
TopicsNeural dynamics and brain function · ECG Monitoring and Analysis · Neuroscience and Neural Engineering
