Electrocardiogram-driven depth of anesthesia monitoring: leveraging autonomic signatures and graph neural networks for low-cost, accurate identification

Precise assessment of anesthetic depth is essential for guaranteeing patient safety and optimal medication delivery during surgical interventions. This article presents a framework based on Graph Neural Networks (GNN) for the automatic categorization of anesthesia depth utilizing electrocardiogram (...

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Detalhes bibliográficos

Principais autores:	Mingjin Weng, Jiaming Liu, Cheng Ding
Formato:	Artigo
Idioma:	Inglês
Publicado em:	PeerJ Inc. 2026-07-01
coleção:	PeerJ Computer Science
Assuntos:	Graph neural network Depth of anesthesia Electrocardiogram Autonomic nervous system Feature engineering Bispectral index
Acesso em linha:	https://peerj.com/articles/cs-3999.pdf
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