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Related Experiment Video

Updated: Jan 29, 2026

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
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Phenotypic Classification of Scalp High-Frequency Oscillations in Absence Epilepsy Based on Multiple Characteristics

Keisuke Maeda1, Himari Tsuboi2, Nami Hosoda2

  • 1Department of Clinical Physiology, Fujita Health University School of Medical Sciences, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake 470-1192, Japan.

Bioengineering (Basel, Switzerland)
|January 28, 2026
PubMed
Summary

Scalp high-frequency oscillations (HFOs) in absence epilepsy (AE) can be classified into distinct phenotypes. These HFO phenotypes are useful EEG biomarkers for detecting seizures and monitoring disease activity in children.

Keywords:
absence epilepsybiomarkerelectroencephalographyhigh-frequency oscillationsk-means clustering analysis

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Area of Science:

  • Neuroscience
  • Epilepsy Research
  • Biomarker Discovery

Background:

  • Scalp high-frequency oscillations (HFOs) show potential as noninvasive biomarkers for epileptogenicity.
  • The diversity and clinical significance of HFO phenotypes in absence epilepsy (AE) require further investigation.

Purpose of the Study:

  • To classify scalp HFOs in pediatric AE using k-means clustering based on morphological characteristics.
  • To assess the distribution of identified HFO phenotypes across electroencephalogram (EEG) epochs and seizure control statuses.
  • To evaluate the predictive value of HFO phenotypes for seizure activity and disease monitoring in AE.

Main Methods:

  • Analysis of scalp EEG recordings from 14 children and adolescents diagnosed with AE.
  • Characterization of 163 scalp HFOs based on frequency, duration, amplitude, and cycle count.
  • Application of k-means clustering on log-transformed amplitude and cycle count to identify HFO phenotypes.

Main Results:

  • Three distinct HFO phenotypes were identified: short duration/low amplitude, low frequency, and long duration/high cycle count.
  • Low-frequency (Cluster 2) and high-cycle-count (Cluster 3) HFO phenotypes significantly predicted ictal HFOs in active AE.
  • The low-frequency HFO phenotype also predicted interictal HFOs in active AE, suggesting its utility in disease monitoring.

Conclusions:

  • Scalp HFO phenotypes exhibit distinct characteristics in pediatric AE.
  • Identified HFO phenotypes serve as valuable EEG-based biomarkers for seizure detection and disease monitoring in AE.
  • These findings may inform and guide treatment strategies for pediatric AE.