Intraoperative Electrocorticography Signal Synthesis to Improve the Classification of Epileptiform Tissue

Leonor Almeida, Sem Hoogteijling, Sem Hoogteijling, Inês Silveira, Dania Furk, Irene Heijink, Irene Heijink, Maryse Van’T. Klooster, Hugo Gamboa, Luís Silva, Maeike Zijlmans, Maeike Zijlmans

2025

Abstract

Epilepsy surgery is a viable option for treating drug-resistant cases where anti-seizure medications fail, but accurately localizing epileptic tissue remains challenging. This process can be guided by the visual assessment of intraoperative electrocorticography (ioECoG). Data scarcity limits developing machine learning (ML) models for automatic epileptic tissue classification. To address this, we propose a generative model based on Generative Adversarial Networks (GANs) to synthesize realistic ioECoG signals. Our approach identified three distinct ioECoG patterns using Agglomerative Clustering, which guided training individual Deep Convolutional Wasserstein GANs with Gradient Penalty (DCwGAN-GP). Synthetic data (SD) was evaluated across multiple dimensions: fidelity using temporal (e.g., Wasserstein distance (WD)), frequency and time-frequency metrics; diversity through dimensionality reduction; and utility by comparing ML performance with and without SD. It replicated temporal and frequency characteristics of real signals (fidelity), though lacked variability (diversity) due to potential data misclassifications. Specifically, the WD between real and synthetic signals outperformed literature benchmarks (i.e., 0.043 ± 0.025 vs. 0.078). Classifiers trained on a combination of real and SD achieved 88% accuracy, compared to 85% with real data alone. These results demonstrate the potential of SD to replicate real signals, address data scarcity, augment ioECoG datasets, and advance ML-based epilepsy surgery research.

Paper Citation

in Harvard Style

Almeida L., Hoogteijling S., Silveira I., Furk D., Heijink I., Klooster M., Gamboa H., Silva L. and Zijlmans M. (2025). Intraoperative Electrocorticography Signal Synthesis to Improve the Classification of Epileptiform Tissue. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: SyntBioGen; ISBN 978-989-758-731-3, SciTePress, pages 1141-1153. DOI: 10.5220/0013398500003911

in Bibtex Style

@conference{syntbiogen25,
author={Leonor Almeida and Sem Hoogteijling and Inês Silveira and Dania Furk and Irene Heijink and Maryse Klooster and Hugo Gamboa and Luís Silva and Maeike Zijlmans},
title={Intraoperative Electrocorticography Signal Synthesis to Improve the Classification of Epileptiform Tissue},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: SyntBioGen},
year={2025},
pages={1141-1153},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013398500003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: SyntBioGen
TI - Intraoperative Electrocorticography Signal Synthesis to Improve the Classification of Epileptiform Tissue
SN - 978-989-758-731-3
AU - Almeida L.
AU - Hoogteijling S.
AU - Silveira I.
AU - Furk D.
AU - Heijink I.
AU - Klooster M.
AU - Gamboa H.
AU - Silva L.
AU - Zijlmans M.
PY - 2025
SP - 1141
EP - 1153
DO - 10.5220/0013398500003911
PB - SciTePress