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 Experiment Videos

Electrocardiogram based neonatal seizure detection.

Barry R Greene1, Philip de Chazal, Geraldine B Boylan

  • 1School of Electrical, Electronic & Mechanical Engineering, University College Dublin, Ireland. barry.greene@ee.ucd.ie

IEEE Transactions on Bio-Medical Engineering
|April 5, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Comparison of oxygen desaturation area-based methods in predicting cardiovascular disease-related mortality outcomes.

Frontiers in network physiology·2026
Same author

Generalizability of symptom-based subtypes of moderate to severe OSA patients within and between ethnic groups.

Annals of the American Thoracic Society·2026
Same author

Amplitude integrated electroencephalography for neonatal seizure detection: an alternative opinion to the 2025 Cochrane review.

Pediatric research·2026
Same author

Physician-Directed Patient Self-Management Using an Implantable IVC Congestion Sensor: Insights From FUTURE-HFII.

JACC. Heart failure·2026
Same author

Long-term Safety and Performance of an Implantable IVC Sensor for Congestion-Guided Management in Heart Failure: 12-Month Results from the FUTURE-HF Trial Portfolio.

European journal of heart failure·2026
Same author

Systematic review of terminology, definitions, and eligibility criteria in trials of neonatal encephalopathy, hypoxic-ischemic encephalopathy, and perinatal asphyxia.

Pediatric research·2026
Same journal

Assessment of skin stiffness in systemic sclerosis using optical coherence elastography: A comparative study with histology and clinical parameters.

IEEE transactions on bio-medical engineering·2026
Same journal

Modeling Dyadic Interdependence in Endocrine Functioning: A Multilevel Machine Learning Study of Adults with Cancer and Their Caregivers.

IEEE transactions on bio-medical engineering·2026
Same journal

A Kalman Filter-Based Framework for Granger Causality Assessment: Application in Tracking Maternal-Fetal Heart Rate Coupling.

IEEE transactions on bio-medical engineering·2026
Same journal

Enhancing Volumetric Imaging in Linear-Array Photoacoustic Tomography: multiview fusion with deep learning.

IEEE transactions on bio-medical engineering·2026
Same journal

Robust Rule-based Heuristic Assistance Strategy for a Semi-Active Shoulder Exoskeleton Used in Overhead Work.

IEEE transactions on bio-medical engineering·2026
Same journal

Highly Accelerated 1-mm Isotropic 3D Chemical Exchange Saturation Transfer MRI Using Wave-Co-CAIPI at 5 Tesla.

IEEE transactions on bio-medical engineering·2026
See all related articles

This study introduces a new electrocardiogram (ECG) method for detecting neonatal seizures. The ECG-based system achieved comparable accuracy to EEG methods, offering a simpler seizure detection approach.

Area of Science:

  • Biomedical Engineering
  • Neonatal Medicine
  • Signal Processing

Background:

  • Neonatal seizures are a critical concern requiring accurate detection.
  • Electroencephalography (EEG) is the standard for seizure detection but has acquisition challenges.
  • Electrocardiogram (ECG) signals offer a potentially simpler alternative for monitoring.

Purpose of the Study:

  • To develop and evaluate an automated method for neonatal seizure detection using ECG signals.
  • To compare the performance of the ECG-based method against established EEG-based systems.

Main Methods:

  • Utilized a database of eight neonatal ECG recordings.
  • Developed an automated system to classify 1-minute epochs as "seizure" or "nonseizure".
  • Employed a linear discriminant classifier analyzing 41 heartbeat timing interval features.

Related Experiment Videos

Main Results:

  • Patient-specific analysis yielded an average accuracy of 70.5% (62.2% sensitivity, 71.8% specificity).
  • Patient-independent analysis achieved 68.3% accuracy (54.6% sensitivity, 77.3% specificity).
  • Adjusting the decision threshold improved sensitivity to 78.4% but reduced specificity to 51.6%.

Conclusions:

  • The developed ECG-based method demonstrates comparable performance to EEG-based neonatal seizure detection.
  • ECG offers a promising, easier-to-acquire alternative for neonatal seizure detection.
  • Further refinement may enhance sensitivity and specificity for clinical application.