Recovery from Coma after Cardiac Arrest: Which Time-Window Counts the Most for Deep Learning Predictions?
Contributo in Atti di convegno
Data di Pubblicazione:
2023
Citazione:
Recovery from Coma after Cardiac Arrest: Which Time-Window Counts the Most for Deep Learning Predictions? / F. Uslenghi, R. Sassi, M.W. Rivolta - In: Computing in Cardiology[s.l] : IEEE Computer Society, 2023. - ISBN 979-8-3503-8252-5. - pp. 1-4 (( Intervento presentato al 50. convegno Computing in Cardiology tenutosi a Atlanta nel 2023 [10.22489/CinC.2023.238].
Abstract:
The George B. Moody PhysioNet Challenge 2023 was dedicated to the development of automated methods for predicting neurological recovery from coma after cardiac arrest. Models were requested to predict a good vs poor neurological outcome using electroencephalograms (EEGs), electrocardiograms (ECGs) and clinical information. Here, we proposed a deep learning model based on a residual network architecture. The model was designed to process only one 5-minute window for each hour up to the 72nd from spontaneous resuscitation, and aggregated the output probabilities of poor outcome using different weighted averages. A 5-fold cross validation technique was used to set the hyperparameters of the model and evaluate the performance on the public dataset. The model's input involved EEG data, heart rate variability (HRV) features extracted from the available ECGs and clinical information. The weighted averages showed improvement over uniform weighting on the hidden validation set (score increased up to 20%), but no improvement in cross-validation. Also, the addition of HRV features and clinical information did not show significant improvement over using only EEG data. The Challenge scores on the public training set, hidden validation set and hidden test set were 0.887, 0.627, and 0.708, respectively (team name: unimi_bisp_squad. ranking: 5).
Tipologia IRIS:
03 - Contributo in volume
Elenco autori:
F. Uslenghi, R. Sassi, M.W. Rivolta
Link alla scheda completa:
Link al Full Text:
Titolo del libro:
Computing in Cardiology