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

You might also read

Related Articles

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

Sort by

Same author

Dark-field synthetic-aperture digital holographic microscopy with an enhanced numerical aperture.

Optics express·2026

Same author

Complex interplay of neuronal and hormonal gut-brain responses to essential amino acid deficit.

Science (New York, N.Y.)·2026

Same author

Oxidative stress-induced astrocytic collagen biosynthesis drives glial barrier formation and neuronal death in ischemic stroke.

Cell metabolism·2026

Same author

Ultra-High Frequency-Low Intensity Magnetic Stimulation Enhances Functional Recovery in a Rat Model for Chronic Capsule Infarct.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026

Same author

Microbubble cavitation in low-intensity focused ultrasound stimulation: A multi-scale computational study.

Computer methods and programs in biomedicine·2026

Same author

Fetal Bovine Serum Is Essential for Maintaining Viability of Porcine Blastocysts During Extended In Vitro Culture: A Comparative Study With Defined Supplements.

Reproduction in domestic animals = Zuchthygiene·2026

Search research articles

Related Experiment Video

Updated: Nov 7, 2025

Author Spotlight: Unraveling Seizure Dynamics and Novel Therapeutics for Status Epilepticus Using CMOS High-Density Microelectrode Array Systems

Author Spotlight: Unraveling Seizure Dynamics and Novel Therapeutics for Status Epilepticus Using CMOS High-Density Microelectrode Array Systems

Published on: September 27, 2024

Pilot study of a single-channel EEG seizure detection algorithm using machine learning.

Seungjun Ryu^1,2, Seunghyeok Back², Seongju Lee²

¹Department of Pediatric Neurosurgery, Severance Children's Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.

Child'S Nervous System : Chns : Official Journal of the International Society for Pediatric Neurosurgery

|May 3, 2021

Summary

This summary is machine-generated.

A new machine learning algorithm accurately detects neonatal seizures using single-channel electroencephalography (EEG). This advancement offers potential for clinical application in neonatal intensive care units (NICUs) and beyond.

Keywords:

Electroencephalography Machine learning Principal component analysis Seizures

More Related Videos

Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy

Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy

Published on: January 29, 2018

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Published on: June 6, 2015

Related Experiment Videos

Last Updated: Nov 7, 2025

Author Spotlight: Unraveling Seizure Dynamics and Novel Therapeutics for Status Epilepticus Using CMOS High-Density Microelectrode Array Systems

Author Spotlight: Unraveling Seizure Dynamics and Novel Therapeutics for Status Epilepticus Using CMOS High-Density Microelectrode Array Systems

Published on: September 27, 2024

Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy

Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy

Published on: January 29, 2018

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Published on: June 6, 2015

Area of Science:

Neuroscience and Biomedical Engineering
Clinical Neurophysiology

Background:

Neonatal seizures are common emergencies in the neonatal intensive care unit (NICU).
Current seizure detection relies on visual inspection of electroencephalography (EEG) by experts, a process that can be time-consuming and prone to error.
Existing automated algorithms have shown insufficient accuracy for clinical use.

Purpose of the Study:

To develop a novel, accurate machine learning-based algorithm for detecting neonatal seizures.
To overcome the limitations of previous automated seizure detection methods.
To create an algorithm suitable for single-channel EEG, enhancing clinical applicability.

Main Methods:

Utilized EEG recordings from 79 term neonates, with a subset of six patients selected for a pilot study.
Data was annotated by three independent experts and divided into training and testing datasets.
Employed a principal component feature-extracted machine learning approach with optimized parameters.

Main Results:

The developed algorithm achieved an area under the ROC curve score of 0.91.
The algorithm demonstrated efficient decision-making, requiring only 5 seconds of data.
Model performance was optimized by extracting 100 principal components.

Conclusions:

The machine learning-based seizure detection algorithm shows significant potential for clinical implementation.
Its single-channel requirement makes it convenient for use in NICUs, general wards, and home settings.
This algorithm represents a step forward in supporting accurate and efficient neonatal seizure detection.