# Study on brain functional networks and wearable device monitoring in children with SeLECTs and high spike-wave index (SWI >50%)

## TL;DR

This study explores brain network changes and physiological signals in children with SeLECTs using wearable devices and EEG to better understand epilepsy severity.

## Contribution

Integrates wearable device data with EEG to analyze brain networks and physiological signals in SeLECTs patients with high SWI.

## Key findings

- Strong functional connectivity in centrotemporal regions across all frequency bands, especially delta.
- Reduced global and local brain network efficiency with higher SWI levels.
- EDA signals show significant sensitivity to autonomic stress responses and correlate negatively with SWI.

## Abstract

Self-limited epilepsy with centrotemporal spikes (SeLECTs) represents a common idiopathic focal epilepsy syndrome in childhood. Although most patients demonstrate a favorable prognosis, some patients develop ESES. ESES is associated with poorer neuropsychological prognosis. This association challenges the “benign” classification of SeLECTs. Currently, the diagnostic threshold for ESES remains controversial. Moreover, traditional long-term video-EEG monitoring presents certain limitations.

The research utilizes the “Biovital-P1” software integrated with Oppo smart bands to collect multimodal physiological signals. Simultaneously, a 21-channel digital EEG system acquires electroencephalographic data. The study constructs brain networks through DTF analysis. Additionally, it performs preprocessing and feature extraction on multimodal physiological signals (ACC, EDA, PPG).

The results demonstrate strong functional connectivity in the centrotemporal region in all frequency bands and the delta band. However, as SWI levels increase, the brain network's global and local efficiency significantly reduces. Analyzing multimodal physiological signals reveals statistically significant differences in ACC and PPG signal time-domain features (Maximum, Minimum, Peak) among different SWI groups. Multiple characteristic parameters of EDA signals also show significant intergroup differences. Notably, EDA signals exhibit excellent sensitivity in reflecting stress responses of the autonomic nervous system. The characteristic features of EDA signals demonstrate a significant negative correlation with SWI levels.