Point-of-Care EEG Artificial Intelligence Measure of Seizure Burden Associates With Clinical Outcome at Discharge.

Josef Parvizi¹, Michelle Armenta Salas², Mariel Kalkach Aparicio³, Sahar F Zafar⁴, Masoom Desai⁵, Suganya Karunakaran², Archit Gupta², Baharan Kamousi², Lawrence J Hirsch⁶, Aaron F Struck⁷

¹Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Palo Alto, CA.

²Ceribell, Inc., Sunnyvale, CA.

³Department of Neurology, University of South Dakota Sanford School of Medicine, Sioux Falls, SD.

⁴Department of Neurology, Harvard Medical School, Boston, MA.

⁵Department of Neurology, University of New Mexico School of Medicine, Albuquerque, NM.

⁶Department of Neurology, Yale School of Medicine, New Haven, CT.

⁷Department of Neurology, University of Wisconsin-Madison, Madison, WI.

PubMed PMC DOI PDF

Abstract

OBJECTIVES: Point-of-care (POC) electroencephalography (EEG) enabled with artificial intelligence (AI) algorithms hold the potential to address gaps in EEG access and interpretation. We assessed the clinical association of the patterns recognized by one of such systems. DESIGN: Secondary cohort analysis of the retrospective multicenter Seizure Assessment and Forecasting with Efficient Rapid-EEG (…

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