What is it about?
An accurate Depth of Anaesthesia (DoA) assessment is crucial for enhancing a patient's surgical experience, as it helps avoid intraoperative (anaesthesia) awareness and reduces postoperative recovery time and cognitive dysfunction. This paper presents two novel research outcomes. The first is the development of a new DoA index using processed electroencephalogram (EEG) signals and machine learning techniques. This research uniquely combines powerful features extracted by two distinct time series decomposition methods: the Fast Fourier Transform (FFT) and Variation Mode Decomposition (VMD).
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Why is it important?
Reliably predicting the LoC within a fixed period would greatly assist medical practitioners by ensuring an appropriate level of anaesthetic is administered to achieve the LoC, thus reducing the risk of anaesthesia awareness and preventing anaesthetic overdose.
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This page is a summary of: Loss of Consciousness Early Warning Modelling with EEG Signals, ACM Transactions on Computing for Healthcare, December 2025, ACM (Association for Computing Machinery),
DOI: 10.1145/3779215.
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