Adaptive weighted dual MAML: Proposing a novel method for the automated diagnosis of partial sleep deprivation.

Journal: PloS one
Published Date:

Abstract

INTRODUCTION: Sleep disorders significantly disrupt normal sleep patterns and pose serious health risks. Traditional diagnostic methods, such as questionnaires and polysomnography, often require extensive time and are susceptible to errors. This highlights the need for automated detection systems to enhance diagnostic efficiency. This study proposes a novel method for the automated diagnosis of partial sleep deprivation utilizing electroencephalogram (EEG) signals.

Authors

  • Soraya Khanmohmmadi
    Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran.
  • Toktam Khatibi
    Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran 1411713116, Iran. Electronic address: toktam.khatibi@modares.ac.ir.
  • Golnaz Tajeddin
    School of Industrial and Systems Engineering, Tarbiat Modares University (TMU), Tehran, Iran.
  • Elham Akhondzadeh
    Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran.
  • Amir Shojaee
    Faculty of Medical Science, Tarbiat Modares University, Tehran, Iran.