  This work showcases our team's (The BEEGees) contributions to the 2023 George
B. Moody PhysioNet Challenge. The aim was to predict neurological recovery from
coma following cardiac arrest using clinical data and time-series such as
multi-channel EEG and ECG signals. Our modelling approach is multimodal, based
on two-dimensional spectrogram representations derived from numerous EEG
channels, alongside the integration of clinical data and features extracted
directly from EEG recordings. Our submitted model achieved a Challenge score of
$0.53$ on the hidden test set for predictions made $72$ hours after return of
spontaneous circulation. Our study shows the efficacy and limitations of
employing transfer learning in medical classification. With regard to
prospective implementation, our analysis reveals that the performance of the
model is strongly linked to the selection of a decision threshold and exhibits
strong variability across data splits.
