ArticleResuscitation2021
Predicting neurological outcome in comatose patients after cardiac arrest with multiscale deep neural networks.
Article in Resuscitation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- 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
- Ambient Music Decreases EEG Burst-Suppression Ratio During General Anesthesia in Rats.Sensors (Basel, Switzerland) · 2026Article
- Automating Critical Care EEG: Enhancing Accuracy, Efficiency, and Patient Outcomes in Critical Care.Epilepsy currents · 2026Article
- Multimodal Deep Learning for ARDS Detection.medRxiv : the preprint server for health sciences · 2025Article
- Review of multimodal machine learning approaches in healthcare.An international journal on information fusion · 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
- Application of multi-feature-based machine learning models to predict neurological outcomes of cardiac arrest.Resuscitation plus · 2024Review
- Role of artificial intelligence in predicting neurological outcomes in postcardiac resuscitation.Annals of medicine and surgery (2012) · 2024Review
- Drop the shortcuts: image augmentation improves fairness and decreases AI detection of race and other demographics from medical images.EBioMedicine · 2024Article
- AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges.Resuscitation plus · 2023Review
- Neurophysiology State Dynamics Underlying Acute Neurologic Recovery After Cardiac Arrest.Neurology · 2023Article
- Deep learning for EEG-based prognostication after cardiac arrest: from current research to future clinical applications.Frontiers in neurology · 2023Review
- Continuous monitoring of neonatal cortical activity: A major step forward.Cell reports. Medicine · 2022Article
- Towards modern post-coma care based on neuroscientific evidence.International journal of clinical and health psychology : IJCHPArticle
Corrections and comments
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Authors and funding
18 authors at 13 institutions in 3 countries.
Funding
Abstract
objectiveElectroencephalography (EEG) is an important tool for neurological outcome prediction after cardiac arrest. However, the complexity of continuous EEG data limits timely and accurate interpretation by clinicians. We develop a deep neural network (DNN) model to leverage complex EEG trends for early and accurate assessment of cardiac arrest coma recovery likelihood.
methodsWe developed a multiscale DNN combining convolutional neural networks (CNN) and recurrent neural networks (long short-term memory [LSTM]) using EEG and demographic information (age, gender, shockable rhythm) from a multicenter cohort of 1,038 cardiac arrest patients. The CNN learns EEG feature representations while the multiscale LSTM captures short-term and long-term EEG dynamics on multiple time scales. Poor outcome is defined as a Cerebral Performance Category (CPC) score of 3-5 and good outcome as CPC score 1-2 at 3-6 months after cardiac arrest. Performance is evaluated using area under the receiver operating characteristic curve (AUC) and calibration error.
resultsModel performance increased with EEG duration, with AUC increasing from 0.83 (95% Confidence Interval [CI] 0.79-0.87 at 12h to 0.91 (95%CI 0.88-0.93) at 66h. Sensitivity of good and poor outcome prediction was 77% and 75% at a specificity of 90%, respectively. Sensitivity of poor outcome was 50% at a specificity of 99%. Predicted probability was well matched to the observation frequency of poor outcomes, with a calibration error of 0.11 [0.09-0.14].
conclusionsThese results demonstrate that incorporating EEG evolution over time improves the accuracy of neurologic outcome prediction for patients with coma after cardiac arrest.
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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.