medRxiv : the preprint server for health sciences · 2025
Binary Classification of Consciousness Using Cerebral Blood Flow and EEG Features.
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Azizi Zade Farzad, Dar Irfaan, Foreman Brandon, Sunar Ulas
Аннотация
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.
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.