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 Video

Updated: Jun 26, 2026

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

Comparing a supervised and an unsupervised classification method for burst detection in neonatal EEG.

J Löfhede1, J Degerman, N Löfgren

  • 1School of Engineering, University College of Boraş, Sweden. johan.lofhede@hb.se

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 24, 2009
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

Resuscitative endovascular occlusion of the aorta restores cerebral metabolic markers of ischaemia induced by haemorrhagic shock.

European journal of trauma and emergency surgery : official publication of the European Trauma Society·2026
Same author

Cerebral glycerol during haemorrhagic shock in normal and raised intracranial pressure resuscitated with total REBOA: an experimental porcine study.

European journal of trauma and emergency surgery : official publication of the European Trauma Society·2026
Same author

Cerebral haemodynamics and intracranial pressure during haemorrhagic shock and resuscitation with total endovascular balloon occlusion of the aorta in an animal model.

European journal of trauma and emergency surgery : official publication of the European Trauma Society·2024
Same author

Post-surgical effects on language in patients with presumed low-grade glioma.

Acta neurologica Scandinavica·2017
Same author

Systemic blockade of ACVR2B ligands prevents chemotherapy-induced muscle wasting by restoring muscle protein synthesis without affecting oxidative capacity or atrogenes.

Scientific reports·2016
Same author

Investigating the Mini-BESTest's construct validity in elderly with Parkinson's disease.

Acta neurologica Scandinavica·2016

Hidden Markov Models (HMM) and Support Vector Machines (SVM) showed similar performance in classifying neonatal EEG burst-suppression patterns. This comparison aids in understanding EEG analysis for infants with perinatal asphyxia.

Area of Science:

  • Neonatal electroencephalography (EEG) analysis
  • Machine learning applications in neuroscience
  • Medical signal processing

Background:

  • Burst-suppression patterns in neonatal EEG are critical indicators of brain health, particularly in infants with perinatal asphyxia.
  • Accurate classification of these patterns is essential for timely diagnosis and intervention.
  • Traditional visual inspection, while expert-driven, can be subjective and time-consuming.

Purpose of the Study:

  • To compare the classification performance of Hidden Markov Models (HMM) and Support Vector Machines (SVM) for neonatal EEG burst-suppression.
  • To evaluate the efficacy of unsupervised (HMM) and supervised (SVM) learning approaches in this context.
  • To provide quantitative performance metrics for automated EEG analysis.

Main Methods:

More Related Videos

Preterm EEG: A Multimodal Neurophysiological Protocol
19:32

Preterm EEG: A Multimodal Neurophysiological Protocol

Published on: February 18, 2012

Amplitude-Integrated EEG in Infants at Risk of Hypoxic-Ischemic Encephalopathy: A Feasibility Study in Road and Air Transport in Western Australia
05:15

Amplitude-Integrated EEG in Infants at Risk of Hypoxic-Ischemic Encephalopathy: A Feasibility Study in Road and Air Transport in Western Australia

Published on: June 21, 2024

Related Experiment Videos

Last Updated: Jun 26, 2026

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

Preterm EEG: A Multimodal Neurophysiological Protocol
19:32

Preterm EEG: A Multimodal Neurophysiological Protocol

Published on: February 18, 2012

Amplitude-Integrated EEG in Infants at Risk of Hypoxic-Ischemic Encephalopathy: A Feasibility Study in Road and Air Transport in Western Australia
05:15

Amplitude-Integrated EEG in Infants at Risk of Hypoxic-Ischemic Encephalopathy: A Feasibility Study in Road and Air Transport in Western Australia

Published on: June 21, 2024

  • Extracted five feature signals from EEG data of six infants experiencing perinatal asphyxia.
  • Employed unsupervised learning for HMM and supervised learning for SVM, using expert visual inspection as the gold standard.
  • Utilized Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) for performance evaluation.

Main Results:

  • Both HMM and SVM demonstrated comparable performance in classifying burst and suppression patterns.
  • The quantitative analysis (AUC) supported the similar efficacy of both machine learning models.
  • The study validates the potential of automated methods for neonatal EEG interpretation.

Conclusions:

  • Support Vector Machines and Hidden Markov Models offer similar capabilities for classifying neonatal EEG burst-suppression.
  • These machine learning approaches provide a viable, objective alternative or adjunct to expert visual EEG interpretation.
  • Further research can refine these models for improved clinical application in neonatal care.