Automated sleep stage scoring of the Sleep Heart Health Study using deep neural networks.

Journal: Sleep
Published Date: Oct 21, 2019

Abstract

STUDY OBJECTIVES: Polysomnography (PSG) scoring is labor intensive and suffers from variability in inter- and intra-rater reliability. Automated PSG scoring has the potential to reduce the human labor costs and the variability inherent to this task. Deep learning is a form of machine learning that uses neural networks to recognize data patterns by inspecting many examples rather than by following explicit programming.

Authors

  • Linda Zhang
    Department of Biomedical Informatics, Vanderbilt University, 2525 West End Ave, Suite 14113, Nashville, TN, 37203, USA. linda.zhang92@vanderbilt.edu.
  • Daniel Fabbri
    Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.
  • Raghu Upender
    Department of Neurology, Sleep Disorders Division, Vanderbilt University School of Medicine, Nashville, TN.
  • David Kent
    Department of Otolaryngology, Vanderbilt University Medical Center, Nashville, TN.

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