Lamis Kattan
King Abdulaziz University, Saudi ArabiaPresentation Title:
Decoding consciousness: EEG signatures of smooth vs. complicated emergence from general anesthesia
Abstract
Introduction: Emergence from general anesthesia remains a critical period with potential complications. Electroencephalogram (EEG) patterns during this phase may offer insights into neurophysiological processes and help predict emergence quality. Our study aims to identify specific EEG patterns associated with smooth versus complicated emergence from general anesthesia.
Methods: We are analyzing EEG recordings from patients undergoing general anesthesia, obtained from multiple databases including OpenNeuro, PhysioNet, and the Temple University Hospital EEG Corpus. Emergence is being classified as smooth or complicated based on clinical records. We are extracting features such as power spectral density, entropy measures, and time frequency representations. Various machine learning algorithms are being applied to classify emergence types based on these EEG features.
Preliminary Results: Initial analyses suggest distinct EEG patterns between smooth and complicated emergences. Smooth emergence appears to be characterized by gradual spectral changes, while complicated emergence shows minimal changes before consciousness returns. Our machine learning models are showing promising results in classifying emergence types, though final accuracy metrics are still being determined.
Discussion: This ongoing research may contribute to the development of real-time monitoring tools for anesthetic management. Understanding EEG signatures of anesthesia emergence could provide insights into consciousness neurobiology and help refine anesthetic protocols. We anticipate that our findings will have significant implications for improving patient outcomes during anesthesia recovery.
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