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Related Experiment Video

Updated: May 25, 2026

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

An algorithm for detecting seizure termination in scalp EEG.

Ali Shoeb¹, Alaa Kharbouch, Jacqueline Soegaard

¹Massachusetts General Hospital, Boston, USA. ashoeb@partners.org

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

|January 19, 2012

Summary

This summary is machine-generated.

This study introduces a novel algorithm for detecting the end of seizure activity in electroencephalogram (EEG) recordings. The developed seizure detection method accurately identifies seizure termination, aiding in clinical applications.

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Area of Science:

Neurology
Biomedical Engineering
Signal Processing

Background:

Limited algorithms exist for detecting seizure cessation in scalp electroencephalogram (EEG).
Accurate seizure termination detection is crucial for estimating seizure duration and managing postictal symptoms.

Purpose of the Study:

To present a novel algorithm for detecting the termination of seizure activity in EEG.
To evaluate the performance of the seizure end detection method on a public dataset.

Main Methods:

Development of an algorithm to identify the cessation of seizure activity from scalp EEG data.
Testing the algorithm on 133 seizures from a public EEG database.
Pairing the seizure end detector with a seizure onset detector to estimate seizure duration.

Main Results:

The algorithm successfully detected the end of 132 out of 133 seizures.
The mean absolute error in detecting seizure termination was 10.3 ± 5.5 seconds compared to electroencephalographer annotations.
Automatic seizure duration estimation was achieved within a 15-second error margin for 85% of seizures.

Conclusions:

The developed algorithm demonstrates high accuracy in detecting seizure termination from EEG.
This method has the potential to significantly improve automated seizure monitoring and duration estimation in clinical practice.