{"doi":"10.64898/2025.12.09.25341792","title":"Binary Classification of Consciousness Using Cerebral Blood Flow and EEG Features","abstract":"Background: Assessing consciousness at the bedside in the neurocritical care unit is complicated by sedation and other treatment effects. While EEG is commonly used, it offers a limited view of the neurovascular unit. We evaluated whether combining cerebral blood flow (CBF) features with EEG improves binary classification of consciousness in patients with severe brain injury. Methods: We retrospectively analyzed 35 adults who underwent multimodal neuromonitoring. Signals were segmented into 30-min windows after each probe recalibration. We used parameters including CBF low-frequency bands (Band IV 0.027-0.073 Hz, Band V 0.01-0.027 Hz, and Band All 0-0.5 Hz) and EEG band powers (delta-beta), alpha-delta ratio (ADR), alpha/(delta+theta) (ADTR), total power. A random forest (RF) model trained using K-fold cross-validation achieved optimal classification. Highly correlated features (r > 0.8) were excluded from simultaneous use. Performance was summarized with ROC-AUC and accuracy, with confusion matrices shown for the best combinations. Results: Multimodal feature combinations significantly improved classification compared to EEG features alone. The best-performing combination (EEG ADR, total EEG power, and CBF Band V) achieved a ROC-AUC of 0.86 and an accuracy of 82%, representing up to 69% improvement over EEG-only models. This model also performed well on noninvasive optical blood flow data. Conclusions: Combining EEG and CBF metrics, particularly low-frequency oscillations in perfusion fluctuations, enhances classification of consciousness in critically ill patients and may support future bedside tools for real-time neurovascular monitoring and in decision making about treatment and rehabilitation.","journal":"medRxiv","year":2025,"id":585632,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8829,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1299203,"name":"Irfaan A. Dar","orcid":null,"position":1,"is_corresponding":false},{"id":330918,"name":"Brandon Foreman","orcid":"0000-0002-5418-674X","position":2,"is_corresponding":false},{"id":758781,"name":"Ulaş Sunar","orcid":"0000-0002-0623-7522","position":3,"is_corresponding":false},{"id":1499461,"name":"Farzad Azizi Zade","orcid":"0009-0007-6510-8919","position":0,"is_corresponding":true}],"reference_count":76,"raw_metadata":null,"created_at":"2026-07-19T02:59:24.273134Z","pmid":"41404288","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":[]}