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

Search research articles

Related Experiment Videos

Multichannel-based newborn EEG seizure detection using time-frequency matched filter.

M S Khlif¹, M Mesbah, B Boashash

¹Perinatal Research Centre, University of Queensland, Australia.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|November 16, 2007

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

Selection of monosomic addition plants in offspring families using repetitive DNA probes in Beta L.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2013

Same author

Effective implementation of time-frequency matched filter with adapted pre and postprocessing for data-dependent detection of newborn seizures.

Medical engineering & physics·2013

Same author

Hemato-immunological responses of Heros severus fed diets supplemented with different levels of Dunaliella salina.

Fish physiology and biochemistry·2013

Same author

Boosted selective internal radiation therapy with 90Y-loaded glass microspheres (B-SIRT) for hepatocellular carcinoma patients: a new personalized promising concept.

European journal of nuclear medicine and molecular imaging·2013

Same author

Kalman filter-based time-varying cortical connectivity analysis of newborn EEG.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2012

Same author

Accelerometer-based fetal movement detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2012

Same journal

Analysis of End-Tidal CO2 Variability During Plateau Waves Episodes: An Information Theoretic Approach<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

AI and Tomosynthesis for Breast Cancer Molecular Subtyping: A step toward precision medicine<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

Towards Sustainable Protein Recovery from Biological Waste: Assessing Polyethersulfone-based Microfiltration.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

Analysis of the cardiovascular response to standardized polymicrobial peritonitis experimental model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

Automated Wrist Ultrasound Image Bone Enhancement and Segmentation Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

See all related articles

This study introduces a time-frequency method for detecting seizures in newborns using electroencephalogram (EEG) data. The approach utilizes a matched filter and geometrical correlation to accurately identify seizure events, improving upon traditional methods.

Area of Science:

Biomedical Engineering
Neuroscience
Signal Processing

Background:

Seizure detection in adults is straightforward, but subtle or absent clinical signs in newborns necessitate advanced diagnostic tools.
The electroencephalogram (EEG) is the primary method for neonatal seizure detection due to the limitations of clinical observation.
Non-stationary and multicomponent EEG signals require sophisticated analysis techniques like time-frequency (TF) methods.

Purpose of the Study:

To develop and present an effective time-frequency (TF) based method for detecting seizures in newborn EEG signals.
To improve the accuracy and reliability of automated seizure detection in neonates.
To leverage characteristic TF signatures of seizures for enhanced diagnostic capabilities.

Main Methods:

Related Experiment Videos

Utilized time-frequency (TF) representation to analyze the complex nature of EEG signals.
Developed a TF matched filter specifically designed for newborn EEG seizure detection.
Implemented a data-dependent thresholding strategy based on the EEG background activity.
Employed multichannel geometrical correlation with an incidence matrix concept to refine detection performance.

Main Results:

The proposed TF method successfully extracts seizure-characteristic TF signatures from newborn EEG.
The data-dependent thresholding and multichannel correlation significantly enhance detector performance.
The method provides a reliable approach for distinguishing between seizure and non-seizure events in neonatal EEG.

Conclusions:

Time-frequency analysis is highly suitable for detecting subtle seizures in neonatal EEG.
The presented TF matched filter method, combined with geometrical correlation, offers a robust solution for automated newborn seizure detection.
This approach holds promise for improving clinical diagnosis and management of seizures in neonates.