Development of a Point of Care System for Automated Coma Prognosis

Renseignements sur le financement
Canadian Institutes of Health Research
  • Type de subvention: Programme de projets de recherche concertée sur la santé (en partenariat avec le CRSNG)
  • Années: 2018/19 à 2020/21
  • Financement total: $283,383
Mots clés
Chercheur(e) principal(e)
Collaborateur(s)
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Sommaire du projet

The Problem: Coma is a state of unconsciousness with a variety of causes. Traditional tests for coma outcome prediction are mainly based on a set of clinical observations (e.g., pupillary constriction). Recently however, event-related potentials (ERPs; which are transient electroencephalogram [EEG] responses to auditory, visual or tactile stimuli) have been introduced as useful predictors of a positive coma outcome (i.e., emergence). However, such tests require a skilled neurophysiologist and such people are in short supply. Also, none of the current approaches have sufficient positive and negative predictive accuracies to provide definitive prognoses in the clinical setting. Objective: We will apply innovative machine learning methods to analyze patient EEGs to develop a simple, objective, replicable, and inexpensive point of care system which can significantly improve the accuracy of coma prognosis relative to current methods. The physical requirements of the proposed system consist only of an EEG system (inexpensive in terms of medical equipment) and a conventional laptop computer. Methodology: We intend to extend our newest algorithms and develop machine learning tools for automatic analysis and detection of ERP components. Preliminary results by the team in this respect have been very promising. The most salient features (i.e., biomarkers) extracted from the ERP will be identified and combined in an optimal fashion to give an accurate indicator of prognosis. Features will be extracted from resting state brain networks and from network trajectories associated with the processing of ERP signals. Significance: The proposed work will enable critical care physicians to assess coma prognosis with speed and accuracy. Thus, families and their health care team will be provided the most accurate information possible to guide discussions of goals of care and life sustaining therapies in the context of dealing with the consequences of devastating neurological injury.