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Related Concept Videos

Epilepsy and Seizures: Overview01:24

Epilepsy and Seizures: Overview

1.1K
Epilepsy is a chronic neurological disease marked by recurrent, unpredictable seizures. These seizures are caused by abnormal electrical discharges in the brain, leading to behavior, sensation, or consciousness alterations. They can also cause transient impairment of awareness, interfering with daily activities.
Various factors can trigger epilepsy, including genetic factors, brain damage, metabolic causes, and unknown etiology. Diagnosis of epilepsy involves electroencephalography (EEG), which...
1.1K
Seizures: Classification01:13

Seizures: Classification

1.3K
Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:
1.3K

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Related Experiment Video

Updated: Jan 9, 2026

Stereo-Electro-Encephalo-Graphy SEEG With Robotic Assistance in the Presurgical Evaluation of Medical Refractory Epilepsy: A Technical Note
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Stereo-Electro-Encephalo-Graphy SEEG With Robotic Assistance in the Presurgical Evaluation of Medical Refractory Epilepsy: A Technical Note

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Integration of epilepsy surgery and automatic seizure onset identification algorithm based on seizure patterns using

H Hamasaki, K Fujiwara, T Ishizaki

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary

    A new algorithm, TAILOR, automatically detects seizure onset using power spectrum patterns. This tailored approach aids in identifying the epileptogenic zone for improved epilepsy surgery outcomes.

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    A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
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    Area of Science:

    • Neuroscience
    • Medical Technology
    • Computational Biology

    Background:

    • Accurate identification of the epileptogenic zone (EZ) is critical for successful epilepsy surgery.
    • Stereotactic intracranial electroencephalography (SEEG) aids in pre-surgical assessment of epileptic networks.
    • Current SEEG analysis relies on visual interpretation and lacks objective quantitative methods for surgical planning, especially for High-Frequency Oscillations (HFOs).

    Purpose of the Study:

    • To introduce TAILOR (Tailored Algorithm for Ictal Localization and Onset pRediction), a novel automated algorithm for detecting seizure onset.
    • To enable quantitative estimation of epileptic networks based on precise seizure onset determination.
    • To enhance the accuracy of surgical planning in epilepsy treatment.

    Main Methods:

    • Development of an automated algorithm, TAILOR, for seizure onset detection.
    • Utilizing patient-specific power spectrum patterns for high temporal resolution onset prediction.
    • Retrospective analysis of clinical SEEG data using the TAILOR algorithm.

    Main Results:

    • TAILOR determined the order of seizure onsets with high temporal resolution.
    • The algorithm successfully ranked actual surgical areas first in six out of eight retrospectively analyzed cases.
    • Epileptic networks were estimated based on the detailed seizure onset orders identified by TAILOR.

    Conclusions:

    • TAILOR provides an automated, quantitative method for seizure onset detection using power spectrum patterns.
    • The algorithm's ability to estimate epileptic networks based on seizure onset order shows promise for improving epilepsy treatment accuracy.
    • This approach is anticipated to enhance the precision of epilepsy surgery and patient outcomes.