{"doi":"10.1109/tbme.2021.3139007","title":"Predicting Neurological Outcome From Electroencephalogram Dynamics in Comatose Patients After Cardiac Arrest With Deep Learning","abstract":"OBJECTIVE: Most cardiac arrest patients who are successfully resuscitated are initially comatose due to hypoxic-ischemic brain injury. Quantitative electroencephalography (EEG) provides valuable prognostic information. However, prior approaches largely rely on snapshots of the EEG, without taking advantage of temporal information. METHODS: We present a recurrent deep neural network with the goal of capturing temporal dynamics from longitudinal EEG data to predict long-term neurological outcomes. We utilized a large international dataset of continuous EEG recordings from 1,038 cardiac arrest patients from seven hospitals in Europe and the US. Poor outcome was defined as a Cerebral Performance Category (CPC) score of 3-5, and good outcome as CPC score 0-2 at 3 to 6-months after cardiac arrest. Model performance is evaluated using 5-fold cross validation. RESULTS: The proposed approach provides predictions which improve over time, beginning from an area under the receiver operating characteristic curve (AUC-ROC) of 0.78 (95% CI: 0.72-0.81) at 12 hours, and reaching 0.88 (95% CI: 0.85-0.91) by 66 h after cardiac arrest. At 66 h, (sensitivity, specificity) points of interest on the ROC curve for predicting poor outcomes were (32,99)%, (55,95)%, and (62,90)%, (99,23)%, (95,47)%, and (90,62)%; whereas for predicting good outcome, the corresponding operating points were (17,99)%, (47,95)%, (62,90)%, (99,19)%, (95,48)%, (70,90)%. Moreover, the model provides predicted probabilities that closely match the observed frequencies of good and poor outcomes (calibration error 0.04). CONCLUSIONS AND SIGNIFICANCE: These findings suggest that accounting for EEG trend information can substantially improve prediction of neurologic outcomes for patients with coma following cardiac arrest.","journal":"IEEE Transactions on Biomedical Engineering","year":2021,"id":159752,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":40,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.958,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":436218,"name":"Edilberto Amorim","orcid":"0000-0001-6972-5622","position":1,"is_corresponding":false},{"id":536464,"name":"Jin Jing","orcid":"0000-0002-2415-5854","position":2,"is_corresponding":false},{"id":249915,"name":"Ona Wu","orcid":"0000-0002-5509-9461","position":3,"is_corresponding":false},{"id":96358,"name":"Mohammad M. Ghassemi","orcid":null,"position":4,"is_corresponding":false},{"id":661352,"name":"Jong Woo Lee","orcid":"0000-0001-5283-7476","position":5,"is_corresponding":false},{"id":671164,"name":"Adithya Sivaraju","orcid":"0000-0002-6027-9205","position":6,"is_corresponding":false},{"id":671165,"name":"Trudy Pang","orcid":"0000-0001-5041-1383","position":7,"is_corresponding":false},{"id":334322,"name":"Susan T. Herman","orcid":"0000-0002-6556-083X","position":8,"is_corresponding":false},{"id":614732,"name":"Nicolas Gaspard","orcid":"0000-0003-1148-6723","position":9,"is_corresponding":false},{"id":671166,"name":"Barry J. Ruijter","orcid":"0000-0002-4390-8938","position":10,"is_corresponding":false},{"id":671167,"name":"Marleen C. Tjepkema‐Cloostermans","orcid":"0000-0002-0686-7284","position":11,"is_corresponding":false},{"id":85624,"name":"Jeannette Hofmeijer","orcid":"0000-0002-7593-5674","position":12,"is_corresponding":false},{"id":614729,"name":"Michel J. A. M. van Putten","orcid":"0000-0001-8319-3626","position":13,"is_corresponding":false},{"id":280809,"name":"M. Brandon Westover","orcid":"0000-0003-4803-312X","position":14,"is_corresponding":false},{"id":671163,"name":"Wei‐Long Zheng","orcid":"0000-0002-9474-6369","position":0,"is_corresponding":true}],"reference_count":56,"raw_metadata":null,"created_at":"2026-07-18T23:44:39.550981Z","pmid":"34962860","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}