Tuning In to Nerve Activity: Audio-Inspired Features for Sympathetic State Detection

Farnoush Baghestani, Yashvi Gupta, Imra Asif, Jihye Moon, Youngsun Kong, Ki Chon

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Keywords: SKNA, ECG, audio features, sympathetic activation, autonomic nervous system.

Abstract: We investigate the use of audio-inspired features to characterize ECG-derived skin sympathetic nerve activity (SKNA). Building on concepts from audio signal processing, we designed a set of SKNA-optimized spectral and energy-related features tailored to the transient, burst-like nature of sympathetic activation. High-frequency ECG signals were recorded from 20 participants during rest and during cognitive stress induced by the Stroop Color and Word Test. Applied to this two-phase protocol, the proposed features captured consistent differences between baseline and stress, achieving robust classification performance using support vector machines with leave-one-subject-out cross-validation. Results demonstrate that audio-inspired features, when physiologically constrained, provide a powerful and interpretable representation of SKNA dynamics, highlighting their promise for noninvasive assessment of autonomic function.

Track: 3. Signal processing, machine learning, deep learning, and decision-support algorithms for digital and computational health

NominateReviewer: Farnoush Baghestani

Submission Number: 112