Anomaly detection of EEG in patients with epilepsy based on graph deviation network

Ming Chen; Changyu Liu; Huaijin Gao; Haomin Tao; Siyuan Lin

doi:10.1117/12.3004116

11 October 2023 Anomaly detection of EEG in patients with epilepsy based on graph deviation network

Ming Chen, Changyu Liu, Huaijin Gao, Haomin Tao, Siyuan Lin

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128001Y (2023) https://doi.org/10.1117/12.3004116
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

A chronic neurological condition called epilepsy affects about 50 million people globally. Although there has been a lot of study on deep neural networks to identify seizures, it is still difficult to precisely define the structural relations between electrodes in EEG signals. The difficulty involved may lead to less accurate prediction. We provide a novel method that combines structured learning methods with graph neural networks and incorporates graph convolution to provide explanations for detected anomalies in EEG signals in order to overcome this constraint. Our investigations on the CHB-MIT dataset show that our approach is more effective and efficient than baseline methods and conventional neural network models at identifying the onset of seizures.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ming Chen, Changyu Liu, Huaijin Gao, Haomin Tao, and Siyuan Lin "Anomaly detection of EEG in patients with epilepsy based on graph deviation network", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128001Y (11 October 2023); https://doi.org/10.1117/12.3004116

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