ArticleBrain : a journal of neurology2023
Auditory stimulation and deep learning predict awakening from coma after cardiac arrest.
Article in Brain : a journal of neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses 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.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 38 citations in OpenAlex.
- Computational predictive processing models of consciousness: a systematic review of non-invasive brain signal analysis in disorders of consciousness.Frontiers in computational neuroscience · 2026Pooled it
- Electroencephalogram-based machine learning models to predict neurologic outcome after cardiac arrest: A systematic review.Resuscitation · 2024Pooled it
- [A method for predicting neurological outcomes after cardiac arrest based on time-frequency domain feature fusion].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Neural Response to Familiar Names Predicts Outcome of Comatose ICU Patients: A Prospective Observational Cohort Study.Nature communications · 2026Observational
- Ambient Music Decreases EEG Burst-Suppression Ratio During General Anesthesia in Rats.Sensors (Basel, Switzerland) · 2026Article
- Diagnosis of disorders of consciousness using nonlinear feature derived EEG topographic maps via deep learning.Scientific reports · 2026Article
- Adaptive long-range modeling of EEG and ECG with Mamba and dynamic graph learning.Scientific reports · 2025Article
- Pediatric cardiac arrest outcome prediction using data-driven machine learning of early quantitative electroencephalogram (qEEG) features.Resuscitation · 2025Article
- Toward a Universal Map of EEG: A Semantic, Low-Dimensional Manifold for EEG Classification, Clustering, and Prognostication.Annals of neurology · 2025Article
- Functional neuroimaging in disorders of consciousness: towards clinical implementation.Brain : a journal of neurology · 2025Review
- Cortical signatures linked to behavior quantitatively track arousal levels.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Reconstructing Covert Consciousness: Neural Decoding as a Novel Consciousness Assessment.Neurology · 2025Review
- Decoding Analyses Show Dynamic Waxing and Waning of Event-Related Potentials in Coma Patients.Brain sciences · 2025Article
- Parallel EEG-fNIRS assessments of covert cognition in behaviorally non-responsive ICU patients: A multi-task feasibility study in a case of acute motor sensory axonal neuropathy.Journal of neurology · 2025Article
- Can artificial intelligence improve the diagnosis and prognosis of disorders of consciousness? A scoping review.Frontiers in artificial intelligence · 2025Review
- 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
- What does my network learn? Assessing interpretability of deep learning for EEG.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Revolutionizing treatment for disorders of consciousness: a multidisciplinary review of advancements in deep brain stimulation.Military Medical Research · 2024Review
- AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges.Resuscitation plus · 2023Review
- Deep learning for EEG-based prognostication after cardiac arrest: from current research to future clinical applications.Frontiers in neurology · 2023Review
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
Abstract
Assessing the integrity of neural functions in coma after cardiac arrest remains an open challenge. Prognostication of coma outcome relies mainly on visual expert scoring of physiological signals, which is prone to subjectivity and leaves a considerable number of patients in a 'grey zone', with uncertain prognosis. Quantitative analysis of EEG responses to auditory stimuli can provide a window into neural functions in coma and information about patients' chances of awakening. However, responses to standardized auditory stimulation are far from being used in a clinical routine due to heterogeneous and cumbersome protocols. Here, we hypothesize that convolutional neural networks can assist in extracting interpretable patterns of EEG responses to auditory stimuli during the first day of coma that are predictive of patients' chances of awakening and survival at 3 months. We used convolutional neural networks (CNNs) to model single-trial EEG responses to auditory stimuli in the first day of coma, under standardized sedation and targeted temperature management, in a multicentre and multiprotocol patient cohort and predict outcome at 3 months. The use of CNNs resulted in a positive predictive power for predicting awakening of 0.83 ± 0.04 and 0.81 ± 0.06 and an area under the curve in predicting outcome of 0.69 ± 0.05 and 0.70 ± 0.05, for patients undergoing therapeutic hypothermia and normothermia, respectively. These results also persisted in a subset of patients that were in a clinical 'grey zone'. The network's confidence in predicting outcome was based on interpretable features: it strongly correlated to the neural synchrony and complexity of EEG responses and was modulated by independent clinical evaluations, such as the EEG reactivity, background burst-suppression or motor responses. Our results highlight the strong potential of interpretable deep learning algorithms in combination with auditory stimulation to improve prognostication of coma outcome.
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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.