Name
Abstract | ||
|---|---|---|
The common age-dependent West syndrome can be diagnosed accurately by electroencephalogram (EEG), but its pathogenesis and evolution remain unclear. Existing research mainly aims at the study of West seizure markers in time/frequency domain, while less literature uses a graph-theoretic approach to analyze changes among different brain regions. In this paper, the scalp EEG based functional connectivity (including Correlation, Coherence, Time Frequency Cross Mutual Information, Phase-Locking Value, Phase Lag Index, Weighted Phase Lag Index) and network topology parameters (including Clustering coefficient, Feature path length, Global efficiency, and Local efficiency) are comprehensively studied for the prognostic analysis of the West episode cycle. The scalp EEGs of 15 children with clinically diagnosed string spasticity seizures are used for prospective study, where the signal is divided into pre-seizure, seizure, and post-seizure states in 5 typical brain wave rhythm frequency bands (δ (1–4 Hz), θ (4–8 Hz), α (8–13 Hz), β (13–30 Hz), and γ (30–80 Hz)) for functional connectivity analysis. The study shows that recurrent West seizures weaken connections between brain regions responsible for cognition and intelligence, while brain regions responsible for information synergy and visual reception have greater variability in connectivity during seizures. It is observed that the changes in β and γ frequency bands of the multiband brain network connectivity patterns calculated by Corr and WPLI can be preliminarily used as judgment of seizure cycle changes in West syndrome. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.neunet.2022.05.029 | Neural Networks |
Keywords | DocType | Volume |
West epilepsy syndrome,Scalp EEG,Functional connection,Brain network,Network topology | Journal | 153 |
Issue | ISSN | Citations |
1 | 0893-6080 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Runze Zheng | 1 | 1 | 0.71 |
| Yuanmeng Feng | 2 | 1 | 0.71 |
| Tianlei Wang | 3 | 34 | 9.77 |
| Jiuwen Cao | 4 | 178 | 18.99 |
| Duanpo Wu | 5 | 0 | 0.34 |
| Tiejia Jiang | 6 | 3 | 1.49 |
| Feng Gao | 7 | 30 | 7.31 |