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

Pulse rhythm01:30

Pulse rhythm

Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac muscle...

You might also read

Related Articles

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

Sort by
Same author

Discovering Novel intracranial EEG Biomarkers of Seizure Generating Tissue through Time-Frequency Analysis.

medRxiv : the preprint server for health sciences·2026
Same author

Global Socioeconomic Context and Brain Ageing in Epilepsy: an ENIGMA-Epilepsy study.

medRxiv : the preprint server for health sciences·2026
Same author

Epileptogenicity alters intrahippocampal ripple propagation.

medRxiv : the preprint server for health sciences·2026
Same author

Positron Emission Tomography (PET) in Phenylketonuria: A Systematic Review of Brain Metabolism Beyond Phenylalanine.

American journal of medical genetics. Part A·2026
Same author

Pediatric epilepsy surgery: Global survey of invasive explorations.

Epilepsia·2026
Same author

Distinct Resting-State Functional Connectivity Profiles in ADHD with and without Prenatal Alcohol Exposure.

medRxiv : the preprint server for health sciences·2026
Same journal

Predicting Chemotherapy Response from Staging Laparoscopy Images.

medRxiv : the preprint server for health sciences·2026
Same journal

Development and External Validation of a Machine Learning Model for 10-Year Ischemic Stroke Risk Prediction in Diverse Populations.

medRxiv : the preprint server for health sciences·2026
Same journal

MCH-Guard: Multimodal Machine Learning Framework for Risk Stratification of Cerebral Microhemorrhage Risk in the Alzheimer's Disease Neuroimaging Initiative.

medRxiv : the preprint server for health sciences·2026
Same journal

Genetic and maternal environmental contributions to estimated fetal weight at 20 weeks gestation compared with birthweight.

medRxiv : the preprint server for health sciences·2026
Same journal

Better immediate declarative memory is associated with forgetting during locomotor adaptation in chronic stroke and in older adults.

medRxiv : the preprint server for health sciences·2026
Same journal

An empirical Bayes framework for burden and dispersion association tests helps prioritize rare variants associated with Alzheimer's disease.

medRxiv : the preprint server for health sciences·2026
See all related articles

Related Experiment Video

Updated: Jun 16, 2026

Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

21.3K

Self-Supervised Data-Driven Approach Defines Pathological High-Frequency Oscillations in Human.

Yipeng Zhang1, Atsuro Daida2, Lawrence Liu1

  • 1Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA.

Medrxiv : the Preprint Server for Health Sciences
|July 23, 2024
PubMed
Summary
This summary is machine-generated.

This study defines pathological high-frequency oscillations (HFOs) using deep learning, improving identification of the epileptogenic zone (EZ) and predicting seizure outcomes after epilepsy surgery.

Keywords:
HFOartificial intelligencemachine learningpathological HFOsself-supervised learning

More Related Videos

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
10:22

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Published on: December 6, 2016

20.3K
Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
09:35

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

9.2K

Related Experiment Videos

Last Updated: Jun 16, 2026

Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

21.3K
Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
10:22

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

Published on: December 6, 2016

20.3K
Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
09:35

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

9.2K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Interictal high-frequency oscillations (HFOs) are key biomarkers for the epileptogenic zone (EZ).
  • Distinguishing pathological from physiological HFOs is challenging, limiting clinical use.
  • Objective criteria are needed for reliable HFO classification.

Purpose of the Study:

  • Investigate if signal morphology in intracranial EEG (iEEG) differentiates pathological and physiological HFOs.
  • Develop a deep generative model to simulate mechanism-driven distinctions in HFO morphology.
  • Enhance the clinical application of HFOs for EZ delineation.

Main Methods:

  • Retrospective analysis of 686,410 HFOs from 185 epilepsy patients using iEEG.
  • Variational autoencoder used to learn morphological characteristics from time-frequency plots.
  • Interpretability analysis (latent space disentanglement, time-domain perturbation) to characterize HFOs.

Main Results:

  • Morphologically defined pathological HFOs (mpHFOs) strongly correlated with expert-defined spikes and seizure onset zones (SOZ).
  • Novel pathological features identified: high gamma and ripple band power.
  • mpHFO resection ratio predicted 12-month seizure outcomes effectively, outperforming unclassified HFOs and matching SOZ resection standards.

Conclusions:

  • A data-driven approach provides a novel, explainable definition of pathological HFOs.
  • This method enhances HFOs' potential for precise EZ delineation.
  • Combining mpHFOs with demographic and SOZ data improves seizure outcome prediction accuracy.