Modeling EEG Data into Graphs for the Prognostic of Patients in Coma Using Graph Neural Networks

Autor(es)

Nascimento Junior, Odelmo O. ; Assis, Dhara L. C. ; Destro Filho, Joao B. ; Zhao, Liang ; Carneiro, Murillo G.

• Tipo de documento: Artigo Científico
• Fonte: INTELLIGENT SYSTEMS, BRACIS 2024, PT IV; v. 15415, p. 15-pg., 2025-01-01.

Resumo

In this work we present a modeling approach using Graph Neural Networks (GNNs) to support the prognosis of patients in coma (PPC) based on Electroencephalogram (EEG) exams. Coma is a critical medical condition characterized by an extended state of unconsciousness. EEG is an exam that captures the electrical activity of the brain through electrodes positioned on the patient's scalp. It provides valuable information about cerebral functions and can also be used for the monitoring and diagnosis of several diseases. In this study, we investigate four GNN architectures specifically designed for PPC analysis, leveraging the power of convolutional-based layers. In addition, we also develop a modeling strategy to represent EEG sequential data into a graph, named EEGraph, effectively harnessing the spatial positions of electrodes. The main contribution of representing the EEG exam in a graph structure is also to explore all the inherent spatial information the exam has. As it is a highly sensitive exam with generally noisy results on the microvolt scale, its spatial exploration may reveal the potential to improve the PPC task. Experimental results considering real data showed that our proposed architecture outperformed the LSTM model in terms of F1-score and accuracy. Statistical tests evidenced the enhanced predictive performance achieved by exploiting the spatial aspects of EEG data using GNNs. Moreover, the findings highlight the significant potential of GNNs to contribute to the PPC task from EEG data. (AU)

• Processo FAPESP: 24/02783-7 - Aprendizado em Redes para Classificação de Alto Nível de Dados Sequenciais
• Beneficiário: Zhao Liang
• Modalidade de apoio: Auxílio à Pesquisa - Pesquisador Visitante - Brasil
• Processo FAPESP: 19/07665-4 - Centro de Inteligência Artificial
• Beneficiário: Fabio Gagliardi Cozman
• Modalidade de apoio: Auxílio à Pesquisa - Programa eScience e Data Science - Centros de Pesquisa em Engenharia