Machine Learning Applied to Coma Neuroscience
We use state-of-the-art machine learning methods leveraging multimodal healthcare data with the goal to improve prediction, diagnosis, and prognostication in acute neurological emergencies. We are developing methods that can integrate time-series data (EEG, EKG, blood pressure, intracranial physiology) with electronic health record data and neuroimaging studies to uncover new insights about the mechanisms of neurological recovery after acute brain injury that can support clinical decision-making.
Quantitative Brain Imaging Models
Coma & Critical Care Neurophysiology (Arrest, TBI, and Stroke)
We employ invasive and non-invasive brain monitoring for critically ill patients with coma, cardiac arrest, traumatic brain injury, stroke, epilepsy, or those at risk for neurological deterioration. We focus on continuous EEG monitoring modeling as well as combining scalp EEG data analysis with intracranial EEG (ECoG), cerebral blood flow, brain oxygenation, and intracranial pressure trends. Our group also has experience delivering auditory task experiments in comatose subjects in the critical care environment to identify if neural responses to these tasks predict the likelihood of consciousness recovery.
Neural State Dynamics Predict Acute Coma Recovery
Spreading Depolarizations in Traumatic Brain Injury
Detecting Responses to Sound and Music In Comatose Patients
Tackling Disparities in Neurological Care