AI-Integrated EEG Decision Support for Neurocritical Care: A Conceptual and Feasibility Framework

Clinical Snapshot

Key Findings

• P Value: Not applicable - no statistical testing performed

• Effect Size: Not quantified - qualitative feasibility assessment only

• Primary Outcome: Demonstration of reliable system operation and generation of decision-support indicators

• Nnt Or Sensitivity: Diagnostic accuracy metrics not reported

• Confidence Interval: Not reported

Clinical Application

Early-stage prototype shows technical feasibility but requires extensive clinical validation before implementation Would require TGA approval as medical device software; alignment with ANZICS guidelines needed; integration with existing EMR systems in Australian ICUs would be essential Neurocritical care patients requiring EEG monitoring, though validation needed for broader application

Abstract

The clinical use of electroencephalography (EEG) for neuro-prognostication in neurocritical care remains limited, despite its ability to provide non-invasive insight into brain activity. This study presents a conceptual framework and early-stage prototype of an AI-assisted EEG decision-support system. Using a literature-informed design and system architecture approach, a web-based prototype was developed to support automated EEG analysis and interpretable brain response reporting. Feasibility testing with four users demonstrated reliable system operation and generation of within-patient decision-support indicators. We highlight the system design and feasibility of operationalizing EEG-based analytics into a clinically interpretable decision-support framework for neurocritical care.

References

1. 1.Dewi, S. R. K., Yang, H. C., Huang, C. W., Li, Y. C. J., & Lin, M. C. (2026). AI-Integrated EEG Decision Support for Neurocritical Care: A Conceptual and Feasibility Framework. Studies in Health Technology and Informatics. https://doi.org/10.3233/SHTI260222