Using Explainable AI for EEG-based Reduced Montage Neonatal Seizure Detection

• URL: http://arxiv.org/abs/2406.16908v3
• Date: Wed, 14 Aug 2024 11:07:41 GMT
• Title: Using Explainable AI for EEG-based Reduced Montage Neonatal Seizure Detection
• Authors: Dinuka Sandun Udayantha, Kavindu Weerasinghe, Nima Wickramasinghe, Akila Abeyratne, Kithmin Wickremasinghe, Jithangi Wanigasinghe, Anjula De Silva, Chamira U. S. Edussooriya,
• Abstract summary: The gold-standard for neonatal seizure detection currently relies on continuous video-EEG monitoring. A novel explainable deep learning model to automate the neonatal seizure detection process with a reduced EEG montage is proposed. The presented model achieves an absolute improvement of 8.31% and 42.86% in area under curve (AUC) and recall, respectively.
• Score: 2.206534289238751
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The neonatal period is the most vulnerable time for the development of seizures. Seizures in the immature brain lead to detrimental consequences, therefore require early diagnosis. The gold-standard for neonatal seizure detection currently relies on continuous video-EEG monitoring; which involves recording multi-channel electroencephalogram (EEG) alongside real-time video monitoring within a neonatal intensive care unit (NICU). However, video-EEG monitoring technology requires clinical expertise and is often limited to technologically advanced and resourceful settings. Cost-effective new techniques could help the medical fraternity make an accurate diagnosis and advocate treatment without delay. In this work, a novel explainable deep learning model to automate the neonatal seizure detection process with a reduced EEG montage is proposed, which employs convolutional nets, graph attention layers, and fully connected layers. Beyond its ability to detect seizures in real-time with a reduced montage, this model offers the unique advantage of real-time interpretability. By evaluating the performance on the Zenodo dataset with 10-fold cross-validation, the presented model achieves an absolute improvement of 8.31% and 42.86% in area under curve (AUC) and recall, respectively.

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