Peer-Reviewed Publication
Neurocrit Care2021;34(3):908-917.June 1, 2021Journal Article
Monitoring the Burden of Seizures and Highly Epileptiform Patterns in Critical Care with a Novel Machine Learning Method.
Baharan Kamousi1, Suganya Karunakaran1, Kapil Gururangan2, Matthew Markert3, Barbara Decker4, Pouya Khankhanian4, Laura Mainardi4, James Quinn5, Raymond Woo1, Josef Parvizi6
1Ceribell Inc., 2483 Old Middlefield Way, Suite 120, Mountain View, CA, USA.
2Department of Neurology, The Mount Sinai Hospital, New York, NY, USA.
3Department of Neurology and Neurological Sciences, Stanford University Medical Center, 300 Pasteur Drive, Stanford, CA, 94305, USA.
4Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
5Department of Emergency Medicine, Stanford University Medical Center, Stanford, CA, USA.
6Department of Neurology and Neurological Sciences, Stanford University Medical Center, 300 Pasteur Drive, Stanford, CA, 94305, USA. jparvizi@stanford.edu.
Abstract
INTRODUCTION: Current electroencephalography (EEG) practice relies on interpretation by expert neurologists, which introduces diagnostic and therapeutic delays that can impact patients' clinical outcomes. As EEG practice expands, these experts are becoming increasingly limited resources. A highly sensitive and specific automated seizure detection system would streamline practice and expedite appro…
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