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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.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:
981

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Related Experiment Video

Updated: Nov 19, 2025

Preterm EEG: A Multimodal Neurophysiological Protocol
19:32

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Deep Learning for EEG Seizure Detection in Preterm Infants.

Alison O'Shea1, Rehan Ahmed1, Gordon Lightbody1

  • 1Irish Centre for Maternal and Child Health Research (INFANT), Department of Electrical and Electronic Engineering, University College Cork, Cork, Ireland.

International Journal of Neural Systems
|February 1, 2021
PubMed
Summary

Deep learning models improve neonatal seizure detection in preterm infants. Transfer learning from term infant data significantly enhances accuracy, overcoming challenges with limited preterm data.

Keywords:
Neonatal EEGdeep learningpreterm EEGseizure detectionsupport vector machinetransfer learning

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Area of Science:

  • Neonatal neurology
  • Medical artificial intelligence
  • Signal processing

Background:

  • Electroencephalography (EEG) is crucial for neonatal seizure detection, but interpretation in preterm infants is challenging due to differing morphology and scarce expert availability.
  • Preterm infants have a higher seizure incidence, necessitating specialized detection algorithms, yet annotated preterm EEG data is limited.
  • Existing seizure detection algorithms trained on term infant EEG perform poorly on preterm infants.

Purpose of the Study:

  • To explore and compare novel deep learning (DL) architectures for neonatal seizure detection specifically in preterm infants.
  • To evaluate different training strategies, including using term infant data, preterm infant data, age-specific preterm data, and transfer learning.
  • To address the challenge of limited annotated preterm EEG data for algorithm development.

Main Methods:

  • The study utilized a large database of 575 hours of continuous EEG recordings from preterm infants.
  • Compared a Support Vector Machine (SVM) classifier trained on term infant EEG with DL approaches.
  • Investigated DL models trained on term data, preterm data, and employed transfer learning from a term-trained model.

Main Results:

  • A term-trained SVM algorithm showed significantly lower performance (AUC 88.3%) on preterm infants compared to term infants (AUC 96.6%).
  • Re-training the SVM on preterm data yielded only a marginal improvement (AUC 89.7%).
  • A DL approach achieved a higher initial AUC (93.3%) on preterm data and reached 95.0% using transfer learning, demonstrating effectiveness with minimal preterm data.

Conclusions:

  • Deep learning architectures, particularly with transfer learning, offer a promising solution for accurate neonatal seizure detection in preterm infants.
  • Transfer learning effectively leverages existing models trained on term infant data, overcoming the scarcity of annotated preterm EEG data.
  • The proposed DL approach reduces the need for manual feature engineering and improves detection accuracy in a challenging clinical population.