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Identification of Seizure Onset Zone from Intracranial EEG Using Source Selection-Based Domain Adaptation.

Keisuke Matsubayashi, Yasushi Iimura, Takumi Mitsuhashi

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

|December 12, 2023

Summary

This summary is machine-generated.

This study introduces a novel method using domain adaptation and source patient selection to improve seizure onset zone (SOZ) identification in focal epilepsy patients. The approach effectively addresses individual EEG differences, enhancing surgical treatment planning.

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Area of Science:

Neuroscience
Medical Imaging
Machine Learning

Background:

Accurate seizure onset zone (SOZ) identification is critical for focal epilepsy surgical treatment.
Automated SOZ identification using electroencephalogram (EEG) data from different patients faces challenges due to individual EEG variability.
Existing methods struggle to overcome the bottleneck of inter-patient differences in EEG signals.

Purpose of the Study:

To develop and evaluate a novel method for automated SOZ identification that addresses individual EEG differences.
To improve the performance of SOZ identification in focal epilepsy patients through domain adaptation and source patient selection.
To enhance the accuracy of pre-surgical planning for epilepsy surgery.

Main Methods:

Proposed a method incorporating domain adaptation and source patient selection for SOZ identification.
Utilized intracranial EEG data from 11 focal cortical dysplasia epilepsy patients for evaluation.
Compared the proposed method against existing approaches lacking domain adaptation and source patient selection.

Main Results:

The proposed method significantly improved SOZ identification performance compared to existing methods.
Domain adaptation and source patient selection effectively mitigated the impact of individual EEG differences.
Analysis suggested that data from patients with residual seizures might negatively impact performance estimation.
Visualizations on MRI images indicated the potential of the method to identify SOZs overlooked by experts.

Conclusions:

The developed method offers a significant advancement in automated SOZ identification for focal epilepsy.
Domain adaptation and source patient selection are crucial for improving cross-patient EEG analysis.
Further research may be needed to account for specific patient data characteristics, such as residual seizures.
The method shows promise in assisting epileptologists in precise SOZ localization for surgical interventions.