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SUMMARY:Poster Flash Talks Group B: Time-Frequency Analysis of EEG for Pre
 dicting Optimal Extubation Timing during Anaesthesia - JinWen Loh (ENS - P
 aris)
DTSTART:20251203T144000Z
DTEND:20251203T144500Z
UID:TALK241102@talks.cam.ac.uk
DESCRIPTION:The final phase of general anaesthesia\, emergence\, requires 
 precise timing for extubation. In paediatric patients\, suboptimal timing 
 carries a heightened risk of severe complications. Currently\, there is no
  formal characterisation of the time-frequency signatures in paediatric EE
 G that correlate with safe extubation readiness. This gap represents an op
 en signal processing problem: the identification of discriminative\, tempo
 rally-localised features within a non-stationary time series to classify n
 eurological state\, and eventually predicting the optimal time for extubat
 ion.\nWe propose a novel time-frequency analysis framework to identify dis
 criminative precursors in EEG signals. We analyzed a dataset of 64 patient
 s (ages 2-18) by computing the relative spectral power in key frequency ba
 nds over time. This revealed a consistent\, temporally-ordered sequence of
  spectral peaks preceding successful extubation.\nThe intervals between th
 ese peak events were highly consistent across the cohort. Leveraging this 
 discovered structure\, we trained a model to predict the optimal extubatio
 n time based on expert-annotated protocols. Using a leave-one-out cross-va
 lidation scheme\, our model achieved a mean absolute error of 81 seconds (
 std: 79 seconds) from the expert-defined optimal time.\nThis work establis
 hes a robust\, data-driven framework for interpreting EEG during emergence
 . We identify a consistent sequence of time-frequency events that serve as
  precursors to extubation readiness. Our predictive model demonstrates the
  viability of translating this knowledge into a clinical tool\, with the p
 otential to standardise and improve patient safety in paediatric anaesthes
 ia.
LOCATION:Seminar Room 1\, Newton Institute
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