Multimodal deep learning approach to predicting neurological recovery from coma after cardiac arrest

arXiv:2403.06027v1 Announce Type: new
Abstract: 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 …

abstract aim arxiv challenge clinical cs.lg data deep learning eeg eess.sp george modelling multimodal multimodal deep learning recovery series team type work