Multimodal deep learning approach to predicting neurological recovery from coma after cardiac arrest
This work addresses a critical medical problem for patients and clinicians, but it is incremental as it builds on existing multimodal deep learning approaches with specific performance insights.
The paper tackled predicting neurological recovery from coma after cardiac arrest using multimodal data, achieving a Challenge score of 0.53 on a hidden test set for predictions made 72 hours after return of spontaneous circulation.
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.