ArticleHuman brain mapping2019
EEG-based outcome prediction after cardiac arrest with convolutional neural networks: Performance and visualization of discriminative features.
Article in Human brain mapping, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 70 citations in OpenAlex.
- Electroencephalogram-based machine learning models to predict neurologic outcome after cardiac arrest: A systematic review.Resuscitation · 2024Pooled it
- EEG-based machine-learning prognostication in comatose patients with indeterminate outcome after cardiac arrest.Critical care (London, England) · 2026Observational
- Diagnosis of disorders of consciousness using nonlinear feature derived EEG topographic maps via deep learning.Scientific reports · 2026Article
- Artificial intelligence in resuscitation: a scoping review.Resuscitation plus · 2025Review
- What does my network learn? Assessing interpretability of deep learning for EEG.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Using artificial intelligence to optimize anti-seizure treatment and EEG-guided decisions in severe brain injury.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025Review
- Computer-assisted analysis of routine EEG to identify hidden biomarkers of epilepsy: A systematic review.Computational and structural biotechnology journal · 2024Review
- Sex-related patterns in the electroencephalogram and their relevance in machine learning classifiers.Human brain mapping · 2023Article
- AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges.Resuscitation plus · 2023Review
- Artificial Intelligence in Resuscitation: A Scoping Review.Journal of clinical medicine · 2023Article
- Auditory stimulation and deep learning predict awakening from coma after cardiac arrest.Brain : a journal of neurology · 2023Article
- Deep learning for EEG-based prognostication after cardiac arrest: from current research to future clinical applications.Frontiers in neurology · 2023Review
- Unraveling Spatial-Spectral Dynamics of Speech Categorization Speed Using Convolutional Neural Networks.Brain sciences · 2022Article
- Revisiting EEG as part of the multidisciplinary approach to post-cardiac arrest care and prognostication: A review.Resuscitation plus · 2022Review
- Deep learning in neuroimaging data analysis: Applications, challenges, and solutions.Frontiers in neuroimaging · 2022Review
- Objective speech intelligibility prediction using a deep learning model with continuous speech-evoked cortical auditory responses.Frontiers in neuroscience · 2022Article
- Accurate age classification using manual method and deep convolutional neural network based on orthopantomogram images.International journal of legal medicine · 2021Article
- Targeted temperature management and early neuro-prognostication after cardiac arrest.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2021Review
- Standardized visual EEG features predict outcome in patients with acute consciousness impairment of various etiologies.Critical care (London, England) · 2020Article
- Novel approaches to prediction in severe brain injury.Current opinion in neurology · 2020Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Prognostication for comatose patients after cardiac arrest is a difficult but essential task. Currently, visual interpretation of electroencephalogram (EEG) is one of the main modality used in outcome prediction. There is a growing interest in computer-assisted EEG interpretation, either to overcome the possible subjectivity of visual interpretation, or to identify complex features of the EEG signal. We used a one-dimensional convolutional neural network (CNN) to predict functional outcome based on 19-channel-EEG recorded from 267 adult comatose patients during targeted temperature management after CA. The area under the receiver operating characteristic curve (AUC) on the test set was 0.885. Interestingly, model architecture and fine-tuning only played a marginal role in classification performance. We then used gradient-weighted class activation mapping (Grad-CAM) as visualization technique to identify which EEG features were used by the network to classify an EEG epoch as favorable or unfavorable outcome, and also to understand failures of the network. Grad-CAM showed that the network relied on similar features than classical visual analysis for predicting unfavorable outcome (suppressed background, epileptiform transients). This study confirms that CNNs are promising models for EEG-based prognostication in comatose patients, and that Grad-CAM can provide explanation for the models' decision-making, which is of utmost importance for future use of deep learning models in a clinical setting.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.