Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Seizures: Classification01:13

Seizures: Classification

1.1K
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:
1.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Many AI analysts, one dataset: Navigating the agentic data science multiverse.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Carbon Dioxide During First-Intention High-Frequency Jet Ventilation: A Narrow Therapeutic Window.

Respiratory care·2026
Same author

Correlation of Oxygen Saturation Index with Oxygenation Index in Congenital Diaphragmatic Hernia: in A Secondary Analysis of a Randomized Clinical Trial.

The Journal of pediatrics·2026
Same author

Automated rapid cooling of high-temperature vacuum furnaces for high throughput neutron experimentation.

The Review of scientific instruments·2026
Same author

Very Preterm Infant Retinal Microanatomy at 36 Weeks' Postmenstrual Age and 2-Year Neurodevelopment.

JAMA ophthalmology·2026
Same author

Preterm Infant Stress During Noncontact Handheld Optical Coherence Tomography and Contact Fundus Photography.

JAMA ophthalmology·2026

Related Experiment Video

Updated: Dec 7, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

872

Attention-Based Network for Weak Labels in Neonatal Seizure Detection.

Dmitry Yu Isaev1, Dmitry Tchapyjnikov2, C Michael Cotten3

  • 1Department of Biomedical Engineering, Duke University, Durham, NC, USA.

Proceedings of Machine Learning Research
|September 30, 2020
PubMed
Summary

Detecting neonatal seizures with deep learning is challenging due to imbalanced data and localized activity. This study evaluates models and proposes a channel importance mechanism for improved seizure detection in neonatal intensive care units (NICUs).

More Related Videos

Preterm EEG: A Multimodal Neurophysiological Protocol
19:32

Preterm EEG: A Multimodal Neurophysiological Protocol

Published on: February 18, 2012

28.9K
Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates
05:58

Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates

Published on: September 6, 2017

40.2K

Related Experiment Videos

Last Updated: Dec 7, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

872
Preterm EEG: A Multimodal Neurophysiological Protocol
19:32

Preterm EEG: A Multimodal Neurophysiological Protocol

Published on: February 18, 2012

28.9K
Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates
05:58

Application of an Amplitude-integrated EEG Monitor Cerebral Function Monitor to Neonates

Published on: September 6, 2017

40.2K

Area of Science:

  • Clinical Neurophysiology
  • Artificial Intelligence in Medicine
  • Neonatal Medicine

Background:

  • Seizures are a frequent complication in newborns undergoing therapeutic hypothermia for hypoxic ischemic encephalopathy, necessitating continuous electroencephalographic (EEG) monitoring.
  • Current intermittent review of EEG data leads to delays in seizure detection and treatment, highlighting the need for automated solutions.
  • Deep learning approaches for neonatal seizure detection face challenges including data imbalance and the time-consuming nature of channel-specific annotations.

Purpose of the Study:

  • To assess the impact of different deep learning models and data balancing techniques on neonatal seizure detection from EEG.
  • To propose and evaluate a novel deep learning model that assigns importance levels to EEG channels, acting as a proxy for seizure activity.
  • To provide a preliminary comparison of deep learning model performance against human expert decisions for clinical relevance.

Main Methods:

  • Evaluation of various deep learning architectures and data balancing strategies for neonatal seizure detection using EEG.
  • Development and testing of a model incorporating a channel importance mechanism to better localize and identify seizure activity.
  • Quantitative assessment of the proposed model's performance and its portability across different EEG device layouts without retraining.

Main Results:

  • The study quantitatively assessed the effectiveness of the channel importance mechanism in deep learning-based neonatal seizure detection.
  • The proposed model demonstrated portability across different EEG device configurations, reducing the need for retraining.
  • High Area Under the Curve (AUC) values in deep learning models did not consistently correlate with agreement with human expert raters.

Conclusions:

  • Deep learning models show promise for neonatal seizure detection, but challenges related to data imbalance and channel-specific activity remain.
  • The developed channel importance mechanism offers a potential improvement for automated seizure detection and clinical deployment.
  • Further refinement of deep learning algorithms is crucial to ensure optimal seizure discrimination and alignment with expert clinical judgment.