Deep learning for EEG-based prognostication after cardiac arrest: from current research to future clinical applications

Frontiers in Neurology

Neurology

Frederic Zubler¹  Athina Tzovara² 

[1] Department of Neurology, Spitalzentrum Biel, University of Bern, Biel/Bienne, Switzerland;Institute of Computer Science, University of Bern, Bern, Switzerland;Department of Neurology, Zentrum für Experimentelle Neurologie and Sleep Wake Epilepsy Center—Neurotec, Inselspital University Hospital Bern, Bern, Switzerland;

关键词: deep learning;    EEG;    prognostication;    coma;    cardiac arrest;   

DOI  :  10.3389/fneur.2023.1183810

 received in 2023-03-10, accepted in 2023-07-03,  发布年份 2023

来源: Frontiers

【 摘 要 】

Outcome prognostication in comatose patients after cardiac arrest (CA) remains to date a challenge. The major determinant of clinical outcome is the post-hypoxic/ischemic encephalopathy. Electroencephalography (EEG) is routinely used to assess neural functions in comatose patients. Currently, EEG-based outcome prognosis relies on visual evaluation by medical experts, which is time consuming, prone to subjectivity, and oblivious to complex patterns. The field of deep learning has given rise to powerful algorithms for detecting patterns in large amounts of data. Analyzing EEG signals of coma patients with deep neural networks with the goal of assisting in outcome prognosis is therefore a natural application of these algorithms. Here, we provide the first narrative literature review on the use of deep learning for prognostication after CA. Existing studies show overall high performance in predicting outcome, relying either on spontaneous or on auditory evoked EEG signals. Moreover, the literature is concerned with algorithmic interpretability, and has shown that largely, deep neural networks base their decisions on clinically or neurophysiologically meaningful features. We conclude this review by discussing considerations that the fields of artificial intelligence and neurology will need to jointly address in the future, in order for deep learning algorithms to break the publication barrier, and to be integrated in clinical practice.

【 授权许可】

Unknown   
Copyright © 2023 Zubler and Tzovara.