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International Journal of Zoology and Applied Biosciences Research Article
Advanced automated EEG analysis system for critical care environments
Arockiasamy Selvanayagam, Ponnammal T, Kiran Kumar S, Sibi S and B Nazreen
Year : 2025 | Pages: 467-471
Received on: 25/09/2025
Revised on: 24/10/2025
Accepted on: 22/11/2025
Published on: 01/12/2025
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Arockiasamy Selvanayagam, Ponnammal T, Kiran Kumar S, Sibi S and B Nazreen( 2025).
Advanced automated EEG analysis system for critical care environments
. International Journal of Zoology and Applied Biosciences, 10( 6), 467-471.
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Abstract
Electroencephalography (EEG) plays a critical role in assessing brain function in intensive care units (ICUs), particularly for detecting seizures, monitoring sedation levels, and identifying early signs of neurological deterioration. Traditional EEG interpretation requires continuous specialist involvement, often leading to delays in diagnosis and therapeutic intervention. This study presents an advanced automated EEG analysis system designed specifically for critical care environments, integrating machine learning, real-time signal processing, and cloud-based clinical decision support. The proposed system automates artifact removal, feature extraction, seizure prediction, and continuous monitoring using deep neural architectures optimized for ICU EEG characteristics. Experimental evaluations using publicly available ICU EEG datasets demonstrate improved accuracy, reduced false alarms, and rapid detection capabilities compared to conventional manual reviews. The results highlight the potential of automated EEG systems to enhance neurological surveillance, improve clinical response time, and support resource-limited critical care settings. This work contributes a scalable framework aimed at strengthening ICU neuro-monitoring workflows and advancing precision critical care.
Keywords
Automated EEG Analysis, Intensive Care Unit (ICU), Neurocritical Care, Machine Learning, Signal.
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© The Author(s) 2025. This article is published by International Journal of Zoology and Applied Biosciences under the terms of the Creative Commons Attribution 4.0 International License (creativecommons.org), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
