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

arXiv: https://arxiv.org/abs/2403.06027 · HTML: https://mlsanity.com/paper/2403.06027/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 9 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels](https://mlsanity.com/paper/1910.13051/) (2019)
- [Dual Bayesian ResNet: A Deep Learning Approach to Heart Murmur Detection](https://mlsanity.com/paper/2305.16691/) (2023)

## Built on by (most cited first, 1 of 1)

- [NeuroECG: ECGFounder-Based Deep ECG Representation for EEG-Free Neurological Prognostication After Cardiac Arrest](https://mlsanity.com/paper/2609.18891/) (2026)

## Abstract (arXiv)

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.