A simple and reliable electroencephalogram montage for detecting and managing electrographic seizures in children with acute encephalitis and encephalopathy.

BACKGROUND

Continuous electroencephalogram (EEG) monitoring (C-EEG) is crucial for the early diagnosis and treatment of electrographic seizures (ESz) in children with acute encephalitis and encephalopathy. However, in Japan, like many other countries, C-EEG is not widely accessible due to reliance on specialized physicians and technicians to apply the international 10-20 system for full-electrode EEG (FE-EEG). Therefore, this study aimed to evaluate a simple and reliable reduced EEG montage.

METHODS

We retrospectively analyzed 83 patients presenting with fever and status epilepticus or fever and impaired consciousness who underwent C-EEG using FE-EEG between 2015 and 2020. After excluding 41 patients diagnosed with febrile status epilepticus, 42 diagnosed with acute encephalitis or encephalopathy were included. Of these, two patients were excluded because their EEGs demonstrated an ictal-interictal continuum, precluding accurate seizure counting. Finally, 15 patients who had more than five ESz were included in this retrospective study. We re-formatted the EEG data recorded with FE-EEG into three reduced montages: double-distance reduced EEG (RE-EEG), Hairline, and amplitude-integrated EEG (aEEG), comprises 4, 3, and 1 EEG channel(s) on each hemisphere, respectively. The ESz detection rates of these reduced montages were compared using Poisson regression analysis.

RESULTS

Among 42 patients with acute encephalitis or encephalopathy, 15 exhibited ESz more than five times. RE-EEG detected ESz with no significant difference compared to FE-EEG. Furthermore, RE-EEG demonstrated a significantly higher ESz detection rate than the Hairline and aEEG montages.

CONCLUSION

RE-EEG offered a reliable seizure detection rate exceeding 90 %, except for one case. Beyond detecting ESz, RE-EEG could serve as a quick, convenient, and temporary monitor for brain function, offering significant potential for implementation in resource-limited healthcare settings.