2024/03/09 by Felix H. Krones, Benjamin H. Walker, Krones, Felix H. +7
Medicine · #Cardiac Arrest and Resuscitation #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Signal Processing (eess.SP) #Traumatic Brain Injury Research #Traumatic Brain Injury and Neurovascular Disturbances #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2403.06027
openalex publication_date 2024/03/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.