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Machine Learning Algorithms in EEG Analysis of Kleefstra Syndrome: Current Evidence and Future Directions.

Katerina D Tzimourta1

  • 1Laboratory of Biomedical Technology and Digital Health, Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece.

Sensors (Basel, Switzerland)
|September 19, 2025
PubMed
Summary

Kleefstra syndrome (KS) research shows limited electroencephalogram (EEG) data, hindering biomarker discovery. Future directions include standardized data collection and machine learning for improved diagnosis and intervention.

Keywords:
9q34.3EEGEHMT1Kleefstra syndromeartificial intelligencebiomarkerselectroencephalographymachine learningrare neurodevelopmental disorders

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Area of Science:

  • Neuroscience
  • Genetics
  • Computational Biology

Background:

  • Kleefstra syndrome (KS) is a rare neurodevelopmental disorder linked to EHMT1 gene mutations.
  • KS commonly presents with intellectual disability, autism spectrum behaviors, and epilepsy.
  • Electroencephalogram (EEG) is a key tool for brain function assessment, but its use in KS is under-characterized.

Purpose of the Study:

  • To review current EEG findings in Kleefstra syndrome.
  • To explore the potential of machine learning (ML) for analyzing EEG data in KS.
  • To propose future research directions for advancing KS diagnostics and treatment.

Main Methods:

  • Systematic review of existing literature on EEG findings in Kleefstra syndrome.
  • Exploration of machine learning applications in related neurodevelopmental disorders.
  • Analysis of challenges and opportunities for ML in KS research.

Main Results:

  • EEG in KS frequently shows nonspecific abnormalities and seizures.
  • No consistent electrophysiological biomarker for KS has been identified.
  • Limited large-scale, public EEG datasets impede ML application in KS.

Conclusions:

  • Standardized EEG data collection and quantitative analysis are crucial.
  • Adapting ML techniques for small datasets is necessary for KS research.
  • A multidisciplinary approach integrating EEG and ML can enhance early diagnosis, monitoring, and personalized interventions for Kleefstra syndrome.