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Quantitative EEG in typical absence seizures: a systematic review of spectral and entropy/complexity signatures.

Sep 2026 · Seizure · Vol 143, pp. 65-77 · 0 citations · 41 references
Medicine

Abstract

Background

Typical absence seizures, the defining seizure type of childhood and juvenile absence epilepsy, are characterised by generalised 3 Hz spike-and-wave discharges and brief impairment of consciousness. Quantitative EEG (QEEG) offers spectral and entropy/complexity parameters beyond visual inspection, but the literature is heterogeneous and no syndrome-specific systematic review exists.

Objectives

To synthesise evidence on QEEG spectral and entropy/complexity parameters in typical absence seizures across ictal, interictal, and peri-ictal states, addressing eight synthesis questions.

Methods

We searched PubMed, Scopus, Web of Science, and the Cochrane Library from inception to 8 April 2026. Risk of bias used JBI checklists. Meta-analysis was infeasible owing to heterogeneity; evidence was synthesised through cohort-deduplicated direction-of-effect vote-counting, with GRADE per question.

Results

Twenty-six reports met eligibility (449 patient-records; several reports reused overlapping cohorts, so the synthesis rests on 21 non-overlapping cohorts). Pre-ictal slow-band scalp-spectral elevation was concordant in sign across cohorts, with delta increased in 4 of 5 cohorts. Ictal entropy decreased in 5 of 5 cohorts. A frontal-prominent topographic pattern recurred across four method families and in nine of the twelve cohorts reporting a topographic distribution, though support was proportionally weaker among cohorts with higher-density montages. Interictal patient-versus-control contrasts were heterogeneous, supporting no generalised interictal deficit.

Conclusions

Pre-ictal slow-band elevation and ictal entropy reduction are the most robust QEEG signatures of typical absence seizures. These remain research-grade findings: replication at the level of a usable effect-size estimate, and external validation of a treatment-response classifier, are necessary before any QEEG biomarker can enter clinical use.

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