ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Deep-Learning-Assisted Prediction of Neurological Recovery from Coma After Cardiac Arrest

Babu, VK and Roshan, N and Pandit, R (2023) Deep-Learning-Assisted Prediction of Neurological Recovery from Coma After Cardiac Arrest. In: UNSPECIFIED.


Download (4MB) | Preview


We develop a deep-learning-based algorithm to predict the probability of recovery of a comatose patient who has suffered a heart attack by analyzing electroencephalogram (EEG) and electrocardiogram (ECG) data. These have been provided to participants in the George Moody Physionet Challenge (2023); our team name is RPGIISC. Given EEGs and ECGs, we extract, from hour-long traces for each patient, the burst-suppression (BS) rate, interchannel EEG correlations, time intervals between successive peaks of ECG, and associated heart variability rate (HVR) metrics. We also use other information provided, e.g., patient age, sex, return of spontaneous circulation (ROSC), in-hospital or out-of-hospital cardiac arrest, presence of a shockable rhythm, and targeted temperature management. With these features, we then use combinations of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to make predictions of (a) the probability of recovery mathcalP and (b) the cerebral performance category (CPC), at hourly scales; we then combine these hourly results to predict final values for mathcalP and CPC. In the official phase, when evaluated at 72 hours after ROSC, the score obtained by our algorithm on the hidden-validation data and hidden-test data is 0.63, and 0.43(ranked 24th), respectively. © 2023 CinC.

Item Type: Conference Paper
Publication: Computing in Cardiology
Publisher: IEEE Computer Society
Additional Information: The copyright for this article belongs to IEEE Computer Society.
Keywords: Computer system recovery; Electroencephalography; Forecasting; Heart; Hospitals; Long short-term memory; Patient rehabilitation; Recovery, Burst suppression; Cardiac arrest; Correlation time; Heart attack; Learning-based algorithms; Neurological recoveries; Performance; PhysioNet; Temperature management; Time interval, Electrocardiograms
Department/Centre: Division of Physical & Mathematical Sciences > Centre for High Energy Physics
Date Deposited: 05 Mar 2024 04:53
Last Modified: 05 Mar 2024 04:53
URI: https://eprints.iisc.ac.in/id/eprint/84060

Actions (login required)

View Item View Item