A Class-Imbalance Aware and Explainable Spatio-Temporal Graph Attention Network for Neonatal Seizure Detection

Author:

Raeisi Khadijeh1,Khazaei Mohammad1,Tamburro Gabriella2,Croce Pierpaolo2,Comani Silvia2,Zappasodi Filippo3

Affiliation:

1. Department of Neuroscience, Imaging and Clinical Sciences, Universita Gabriele d’Annunzio, Chieti 66100, Italy

2. Department of Neuroscience, Imaging and Clinical Sciences-Behavioral Imaging and Neural Dynamics Center, Universita Gabriele d’Annunzio, Chieti 66100, Italy

3. Department of Neuroscience, Imaging and Clinical Sciences-Behavioral, Imaging and Neural Dynamics Center-Institute for, Advanced Biomedical Technologies, Universita Gabriele d’Annunzio, Chieti 66100, Italy

Abstract

Seizures are the most prevalent clinical indication of neurological disorders in neonates. In this study, a class-imbalance aware and explainable deep learning approach based on Convolutional Neural Networks (CNNs) and Graph Attention Networks (GATs) is proposed for the accurate automated detection of neonatal seizures. The proposed model integrates the temporal information of EEG signals with the spatial information on the EEG channels through the graph representation of the multi-channel EEG segments. One-dimensional CNNs are used to automatically develop a feature set that accurately represents the differences between seizure and nonseizure epochs in the time domain. By employing GAT, the attention mechanism is utilized to emphasize the critical channel pairs and information flow among brain regions. GAT coefficients were then used to empirically visualize the important regions during the seizure and nonseizure epochs, which can provide valuable insight into the location of seizures in the neonatal brain. Additionally, to tackle the severe class imbalance in the neonatal seizure dataset using under-sampling and focal loss techniques are used. Overall, the final Spatio-Temporal Graph Attention Network (ST-GAT) outperformed previous benchmarked methods with a mean AUC of 96.6% and Kappa of 0.88, demonstrating its high accuracy and potential for clinical applications.

Funder

European Union

INtegrating Functional Assessment measures for Neonatal Safeguard

Publisher

World Scientific Pub Co Pte Ltd

Subject

Computer Networks and Communications,General Medicine

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