Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Gas Chromatography: Types of Detectors-II

Gas Chromatography: Types of Detectors-II

In gas chromatography, different detectors are employed to meet specific analytical needs. These detectors are often categorized based on their detection mechanisms and the types of compounds they are best suited to analyze. Thermal Conductivity Detectors (TCD), Flame Ionization Detectors (FID), and Electron Capture Detectors (ECD) represent common categories, each with unique operating principles and applications. However, beyond these, several other detectors are designed for more specialized...

You might also read

Related Articles

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

Sort by

Same author

Temporal changes in circulating miRNAs following gestational diabetes diagnosis: a pilot longitudinal study.

Journal of clinical & translational endocrinology·2026

Same author

Epileptogenicity alters intrahippocampal ripple propagation.

medRxiv : the preprint server for health sciences·2026

Same author

Single-port compared with conventional laparoscopic ovarian cystectomy for benign ovarian cysts: a systematic review and meta-analysis.

Frontiers in oncology·2026

Same author

Dance with robot: how employee functional experience diversity influences work wellbeing on human-robot collaboration.

Frontiers in psychology·2026

Same author

Laparoendoscopic single-site surgery compared with conventional laparoscopy for benign adnexal diseases: a systematic review and meta-analysis of randomized controlled trials.

Frontiers in medicine·2026

Same author

Reply to Huang and Dong: Circuit insights unlock translational potential of peripheral electroacupuncture for epilepsy.

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

Same journal

N-Homocysteinylation of Cofilin-1 Aggravates Epileptic Pathophysiology via Disruption of Actin Dynamics.

Neuroscience bulletin·2026

Same journal

Microglial Alkbh5 Deficiency Alleviates Chronic Restraint Stress-Induced Depression-like Behaviors in Mice.

Neuroscience bulletin·2026

Same journal

Correction to: Engineered Alzheimer Organoids Validate the Link Between Intracellular and Soluble p-Tau Biomarkers and Highlight the Contribution of Astrocytic Tau.

Neuroscience bulletin·2026

Same journal

Oligodendroglia as Active Immunomodulators in Demyelinating Diseases.

Neuroscience bulletin·2026

Same journal

Real-Time Monitoring of Anaesthetic-Induced Change of Consciousness States Associated with Brain Biophoton Information Integration.

Neuroscience bulletin·2026

Same journal

From Immune Cells to Matchmakers: How Brain Microglia Control Reproduction.

Neuroscience bulletin·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 11, 2025

Simultaneous Label-Free Autofluorescence Multi-Harmonic Microscopy

Simultaneous Label-Free Autofluorescence Multi-Harmonic Microscopy

Published on: August 29, 2025

An Automatic HFO Detection Method Combining Visual Inspection Features with Multi-Domain Features.

Xiaochen Liu¹, Lingli Hu², Chenglin Xu³

¹College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, China.

Neuroscience Bulletin

|March 26, 2021

Summary

This summary is machine-generated.

This study introduces an automated method for detecting high-frequency oscillations (HFOs), crucial biomarkers for epilepsy, improving diagnostic efficiency. The new approach accurately identifies HFOs in intracranial electroencephalogram (iEEG) data, aiding epilepsy research and treatment.

Keywords:

Automatic detection Combined features Epilepsy HFO

More Related Videos

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

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

Published on: December 6, 2016

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Related Experiment Videos

Last Updated: Nov 11, 2025

Simultaneous Label-Free Autofluorescence Multi-Harmonic Microscopy

Simultaneous Label-Free Autofluorescence Multi-Harmonic Microscopy

Published on: August 29, 2025

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

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

Published on: December 6, 2016

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

High-frequency oscillations (HFOs) are promising biomarkers for tracking epileptic activity and localizing epileptogenic zones.
Manual detection of HFOs in intracranial electroencephalogram (iEEG) data is time-consuming, subjective, and prone to error.
Existing methods struggle to differentiate sporadic HFO events from continuous oscillatory activity.

Purpose of the Study:

To develop an automatic and objective method for detecting epileptic HFOs in iEEG data.
To improve the efficiency and accuracy of HFO detection for epilepsy diagnosis and research.
To address the limitations of manual HFO identification and enhance the analysis of iEEG signals.

Main Methods:

Proposed an automatic epileptic HFO detection method utilizing both visual and non-intuitive multi-domain features.
Incorporated environmental reference features by analyzing iEEG signals adjacent to detected events to mitigate interference from continuous activity.
Developed a MatLab-based HFO detector for multi-channel, long-distance iEEG signal analysis.

Main Results:

The detector achieved over 90% expert-confirmed HFO event detection with a missed-detection rate below 10%.
Demonstrated synchronous improvement in sensitivity and specificity compared to recent related research.
Achieved a balance between a low false-alarm rate and a high detection rate, showing strong performance in precision.

Conclusions:

The developed automatic HFO detection method is effective and reliable for analyzing iEEG data.
This tool can significantly enhance the efficiency of clinical experts in identifying HFO events for epilepsy diagnosis and treatment.
The method offers a valuable auxiliary tool for epilepsy research, improving the speed and objectivity of biomarker analysis.