A Multi-channel EEG Data Analysis for Poor Neuro-prognostication in
  Comatose Patients with Self and Cross-channel Attention Mechanism
Hemin Ali Qadir Naimahmed Nesaragi Per Steiner Halvorsen Ilangko Balasingham
This work investigates the predictive potential of bipolar
electroencephalogram (EEG) recordings towards efficient prediction of poor
neurological outcomes. A retrospective design using a hybrid deep learning
approach is utilized to optimize an objective function aiming for high
specificity, i.e., true positive rate (TPR) with reduced false positives (<
0.05). A multi-channel EEG array of 18 bipolar channel pairs from a randomly
selected 5-minute segment in an hour is kept. In order to determine the outcome
prediction, a combination of a feature encoder with 1-D convolutional layers,
learnable position encoding, a context network with attention mechanisms, and
finally, a regressor and classifier blocks are used. The feature encoder
extricates local temporal and spatial features, while the following position
encoding and attention mechanisms attempt to capture global temporal
dependencies. Results: The proposed framework by our team, OUS IVS, when
validated on the challenge hidden validation data, exhibited a score of 0.57.