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Voltage-based automated detection of postictal generalized electroencephalographic suppression: Algorithm development

L Brian Hickman1, R Edward Hogan2, Alyssa K Labonte3

  • 1Department of Internal Medicine, University of California, Irvine School of Medicine, Orange, CA, USA; Department of Anesthesiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.

Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology
|November 2, 2020
PubMed
Summary
This summary is machine-generated.

An automated algorithm effectively detects postictal generalized electroencephalographic suppression (PGES) after electroconvulsive therapy (ECT). This tool shows high accuracy, comparable to expert epileptologists, aiding in epilepsy research.

Keywords:
AlgorithmsElectroconvulsive Therapy (ECT)Major depressive disorderPostictal generalized EEG suppressionSeizureSudden unexplained death in epilepsy

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Area of Science:

  • Neuroscience
  • Epileptology
  • Medical Technology

Background:

  • Postictal generalized electroencephalographic suppression (PGES) is a critical EEG pattern following generalized seizures.
  • PGES is linked to sudden unexplained death in epilepsy (SUDEP) and electroconvulsive therapy (ECT) efficacy.
  • Accurate quantification of PGES is essential for clinical research and patient outcomes.

Purpose of the Study:

  • To develop and validate a voltage-based algorithm for automated PGES detection.
  • To assess the algorithm's performance against expert epileptologist interpretations.
  • To determine the utility of algorithmic PGES detection in clinical practice.

Main Methods:

  • A voltage-based algorithm was created to mimic expert epileptologist criteria for PGES detection.
  • The algorithm was validated using postictal EEG data from patients undergoing ECT.
  • Algorithm performance was compared to the consensus of four clinical epileptologists.

Main Results:

  • The algorithm demonstrated high discriminability (C-statistic: 0.91) compared to expert consensus.
  • Peak agreement between the algorithm and expert consensus was substantial (Cohen's Kappa: 0.65).
  • Interrater agreement between the algorithm and individual epileptologists matched that among experts.

Conclusions:

  • An automated, voltage-based algorithm can reliably detect PGES following ECT.
  • The algorithm's discriminative ability is comparable to that of expert epileptologists.
  • Algorithmic PGES detection can enhance the precision of correlating this biomarker with clinical outcomes.