ArticleCNS neuroscience & therapeutics2025
Early Prediction of the Evolution of Self-Limited Epilepsy With Centrotemporal Spikes to Epileptic Encephalopathy With Spike-and-Wave Activation in Sleep: A Prediction Model Construction Based on Quantitative Electroencephalography Characteristics.
Article in CNS neuroscience & therapeutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Salience network hub dysfunction is associated with cognitive impairment in self-limited epilepsy with centrotemporal spikes: An OPM-MEG study.Epilepsia open · 2026Article
- Altered Glymphatic-Related MRI Metrics and Brain Network Reorganization in Drug-Naive Children With Self-Limited Epilepsy With Centrotemporal Spikes.CNS neuroscience & therapeutics · 2026Article
- Article
- Quantitative EEG analysis related to coma and prognosis in acute severe cerebral infarction.European journal of medical research · 2025Article
- Analysis of influencing factors for cognitive impairment in children with self-limited epilepsy with centrotemporal spikes.Frontiers in neurologyArticle
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Authors and funding
10 authors.
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Abstract
aimsTo predict the progression of children with self-limited epilepsy with centrotemporal spikes (SeLECTS) to epileptic encephalopathy with spike-and-wave activation in sleep (EE-SWAS).
methodsWe conducted a retrospective analysis of early clinical and electroencephalography (EEG) data. Clinical parameters included demographic and epilepsy-related characteristics. EEG were qualitatively (localization, lateralization, synchrony, non-Rolandic discharges, nondipole spikes, multiple spikes, focal slow-wave activity) and quantitatively (spike-wave index [SWI], spike-wave frequency [SWF], power spectral density [PSD], phase-locking value [PLV], phase lag index [PLI], weighted phase lag index [wPLI], characteristic path length [CPL], clustering coefficient [CC], small-worldness [Sigma]) analyzed. A logistic regression-based prediction model was further formulated and evaluated.
resultsThis study included 50 children with seizure-free typical SeLECTS and 76 who developed EE-SWAS. Multivariable logistic regression revealed that early EEG features-SWF, relative PSD in the alpha band, wPLI and CPL in the delta band-were associated with the risk of encephalopathic transformation. The model demonstrated good performance with an area under the curve of 0.817 (95% confidence interval 0.736-0.898). The model showed a good fit and clinical benefit.
conclusionInitial quantitative EEG characteristics of SeLECTS can predict the development of EE-SWAS, suggesting distinct disease characteristics and pathogeneses in children at risk of encephalopathic transformation.
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