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.

预测心脏骤停后昏迷状态中的神经恢复，使用临床数据和时间序列信号（如多通道脑电图和心电图），本研究展示了多模态的建模方法，基于二维光谱图表示的脑电图信号，结合临床数据和直接从脑电图记录中提取的特征。通过使用转移学习在医学分类中的有效性和局限性，研究表明模型的性能与决策阈值的选择密切相关，并在数据划分中表现出较大的可变性。