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Related Concept Videos

Seizures: Classification01:13

Seizures: Classification

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Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
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Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:
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Related Experiment Video

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A machine-learning algorithm for neonatal seizure recognition: a multicentre, randomised, controlled trial.

Andreea M Pavel1, Janet M Rennie2, Linda S de Vries3

  • 1INFANT Research Centre, University College Cork, Cork, Ireland; Department of Paediatrics and Child Health, University College Cork, Cork, Ireland.

The Lancet. Child & Adolescent Health
|August 31, 2020
PubMed
Summary
This summary is machine-generated.

The Algorithm for Neonatal Seizure Recognition (ANSeR) accurately detects neonatal seizures but did not improve individual neonate identification. However, ANSeR improved the recognition of seizure hours in neonates, showing promise for enhanced clinical decision support.

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Area of Science:

  • Neonatal neurology
  • Medical artificial intelligence
  • Clinical decision support systems

Background:

  • Accurate diagnosis of neonatal seizures is challenging despite continuous electroencephalography (cEEG).
  • Automated algorithms can aid in recognizing neonatal seizures.
  • The Algorithm for Neonatal Seizure Recognition (ANSeR) was developed for this purpose.

Purpose of the Study:

  • To assess the diagnostic accuracy of the ANSeR algorithm.
  • To compare the performance of ANSeR-assisted diagnosis versus conventional EEG alone.
  • To evaluate the impact on identifying neonates with seizures and seizure hours.

Main Methods:

  • A multicentre, randomized, controlled trial involving eight neonatal centers.
  • 132 neonates were assigned to cEEG with ANSeR, and 132 to cEEG alone.
  • Primary outcome: diagnostic accuracy (sensitivity, specificity, false detection rate) for neonates and seizure hours.

Main Results:

  • ANSeR did not significantly improve the identification of individual neonates with seizures (sensitivity and specificity were similar).
  • The algorithm significantly improved the recognition of seizure hours (66.0% vs 45.3%).
  • False detection rates were higher with ANSeR (36.6% vs 22.7%).

Conclusions:

  • ANSeR is a safe and accurate tool for neonatal seizure detection.
  • While not enhancing individual neonate identification, ANSeR improves seizure hour recognition.
  • Further research is needed to explore ANSeR's benefits in less experienced centers.