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Epilepsy and Seizures: Overview

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Epilepsy is a chronic neurological disease marked by recurrent, unpredictable seizures. These seizures are caused by abnormal electrical discharges in the brain, leading to behavior, sensation, or consciousness alterations. They can also cause transient impairment of awareness, interfering with daily activities.
Various factors can trigger epilepsy, including genetic factors, brain damage, metabolic causes, and unknown etiology. Diagnosis of epilepsy involves electroencephalography (EEG), which...

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Quantitative electroencephalographic measures during postmalarial epileptogenesis.

Rasesh B Joshi¹, Suzanna Mwanza², Hitten P Zaveri³

¹Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA.

|May 7, 2026

Summary

This summary is machine-generated.

Quantitative electroencephalography (EEG) measures can help identify children at risk for developing epilepsy after severe malaria. These findings may aid in early risk stratification for postmalarial epilepsy (PME).

Keywords:

cerebral malaria epileptogenesis malaria postmalarial epilepsy quantitative EEG

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Area of Science:

Neurology
Pediatrics
Infectious Diseases

Background:

Severe malaria with central nervous system involvement is a major cause of acquired pediatric epilepsy globally.
Understanding the neurophysiological changes in postmalarial epileptogenesis is crucial for identifying at-risk individuals.

Purpose of the Study:

To investigate quantitative electroencephalographic (EEG) measures for predicting postmalarial epilepsy (PME) in children.
To identify differences in EEG patterns between children who develop PME and those who do not.

Main Methods:

A prospective observational study enrolled 186 children (6 months–11 years) with confirmed central nervous system malaria in Zambia.
EEG data were collected during acute illness and at 1, 6, and 12 months postdischarge for 26 patients diagnosed with PME and controls.
Quantitative EEG measures including relative power, magnitude-squared coherence (MSC), and approximate entropy (ApEn) were analyzed.

Main Results:

Admission relative gamma power was higher in the PME group, while alpha and beta power increased over time in both groups, with higher values in the non-epilepsy group at 12 months.
Approximate entropy (ApEn) was higher in the non-epilepsy group at admission and 1- and 6-month follow-ups.
Reduced beta and gamma magnitude-squared coherence (MSC) at admission and 1-month follow-up distinguished the epilepsy group, with reappearance at 12 months.

Conclusions:

Quantitative EEG measures, including relative power, ApEn, and MSC, show potential for differentiating children who develop PME from those who do not.
These EEG biomarkers may be valuable for the early risk stratification of children with severe malaria, enabling timely intervention.