Eye movement detection using electrooculography and machine learning in cardiac arrest patients.

Journal: Resuscitation
PMID: 40133167

Abstract

AIM: To train a machine learning algorithm to identify eye movement from electrooculography (EOG) in cardiac arrest (CA) patients. Neuroprognostication of comatose post-CA patients is challenging, requiring novel biomarkers to guide decision making. Eye movement may be a promising marker of arousal recovery, as pathways for eye movement and arousal share common anatomic structures. Continuous quantification of eye movement is feasible through electroencephalogram (EEG) with EOG, but manual quantification is resource-intensive.

Authors

  • Cameron J Hill
    Boston Medical Center, United States; Boston University Chobanian and Avedisian School of Medicine, United States. Electronic address: chill20@bu.edu.
  • Chelsea A Sykora
    Boston Medical Center, United States; Boston University Chobanian and Avedisian School of Medicine, United States. Electronic address: sykorac@ohsu.edu.
  • Stephen Schmugge
    University of North Carolina, Charlotte, United States. Electronic address: sjschmug@uncc.edu.
  • Samuel Tate
    University of North Carolina, Charlotte, United States. Electronic address: state18@uncc.edu.
  • Michael F M Cronin
    Boston Medical Center, United States; Boston University Chobanian and Avedisian School of Medicine, United States. Electronic address: mfmc@bu.edu.
  • Joseph Sisto
    Boston Medical Center, United States. Electronic address: joseph.sisto@bmc.org.
  • Leigh Ann Mallinger
    Boston Medical Center, United States. Electronic address: leigh.mallinger@bmc.org.
  • Allyson Reinert
    Boston Medical Center, United States. Electronic address: allyson.reinert@bmc.org.
  • Rebecca A Stafford
    Boston Medical Center, United States. Electronic address: rebecca.stafford@bmc.org.
  • Brian S Tao
    Boston Medical Center, United States; Boston University Chobanian and Avedisian School of Medicine, United States. Electronic address: briantao@bu.edu.
  • Naveen Arunachalam Sakthiyendran
    Boston Medical Center, United States; Boston University Chobanian and Avedisian School of Medicine, United States. Electronic address: naveenar@bu.edu.
  • Kerry Nguyen
    Boston University, United States.
  • Ashwin Krishnaswamy
    Boston University, United States.
  • Shruti Patil
    Symbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India.
  • Abrar Al-Faraj
    Boston Medical Center, United States; Boston University Chobanian and Avedisian School of Medicine, United States. Electronic address: abrar.Al-Faraj@bmc.org.
  • Ika Noviawaty
    Boston Medical Center, United States; Boston University Chobanian and Avedisian School of Medicine, United States. Electronic address: ika.noviawaty@bmc.org.
  • Mary Russo
    Boston Medical Center, United States. Electronic address: mary.russo@bmc.org.
  • Brian Pugsley
    Boston Medical Center, United States. Electronic address: brian.pugsley@bmc.org.
  • Jong Woo Lee
    Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA.
  • David Greer
    Boston Medical Center, United States; Boston University Chobanian and Avedisian School of Medicine, United States. Electronic address: david.greer@bmc.org.
  • Min Shin
    University of North Carolina, Charlotte, United States. Electronic address: mcshin@uncc.edu.
  • Charlene J Ong
    Boston Medical Center, United States; Boston University Chobanian and Avedisian School of Medicine, United States. Electronic address: cjong@bu.edu.

Keywords

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