Diagnostic deep learning algorithms that use resting EEG to distinguish major depressive disorder, bipolar disorder, and schizophrenia from each other and from healthy volunteers.

Journal: Journal of affective disorders
PMID: 37963517

Abstract

BACKGROUND: Mood disorders and schizophrenia affect millions worldwide. Currently, diagnosis is primarily determined by reported symptomatology. As symptoms may overlap, misdiagnosis is common, potentially leading to ineffective or destabilizing treatment. Diagnostic biomarkers could significantly improve clinical care by reducing dependence on symptomatic presentation.

Authors

  • Maryam Ravan
    Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada. Electronic address: mravan@ece.mcmaster.ca.
  • Amin Noroozi
    Department of Digital, Technologies, and Arts, Staffordshire University, Staffordshire, England, UK.
  • Mary Margarette Sanchez
    Department of Electrical and Computer Engineering, New York Institute of Technology, New York, NY, USA.
  • Lee Borden
    Department of Electrical and Computer Engineering, New York Institute of Technology, New York, NY, USA.
  • Nafia Alam
    Department of Electrical and Computer Engineering, New York Institute of Technology, New York, NY, USA.
  • Pierre Flor-Henry
    Alberta Hospital, Edmonton, AB, Canada.
  • Sinisa Colic
  • Ahmad Khodayari-Rostamabad
    Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada.
  • Luciano Minuzzi
    3 Department of Psychiatry and Behavioral Neurosciences and St. Joseph's Healthcare Hamilton, Hamilton, Canada .
  • Gary Hasey
    Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada.

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