Standardized image-based polysomnography database and deep learning algorithm for sleep-stage classification.

Journal: Sleep
Published Date: Dec 11, 2023

Abstract

STUDY OBJECTIVES: Polysomnography (PSG) scoring is labor-intensive, subjective, and often ambiguous. Recently several deep learning (DL) models for automated sleep scoring have been developed, they are tied to a fixed amount of input channels and resolution. In this study, we constructed a standardized image-based PSG dataset in order to overcome the heterogeneity of raw signal data obtained from various PSG devices and various sleep laboratory environments.

Authors

  • Jaemin Jeong
    Department of Computer Engineering, School of Software, Hallym University, Chuncheon, Republic of Korea.
  • Wonhyuck Yoon
    OUaR LaB, Inc, Seoul, Republic of Korea.
  • Jeong-Gun Lee
    Department of Computer Engineering, School of Software, Hallym University, Chuncheon, Republic of Korea.
  • Dongyoung Kim
    Center for Soft and Living Matter, Institute for Basic Science (IBS), Ulsan, 44919, Republic of Korea.
  • Yunhee Woo
    Institute of New Frontier Research, Division of Big Data and Artificial Intelligence, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon, Republic of Korea.
  • Dong-Kyu Kim
    Department of Otorhinolaryngology-Head and Neck Surgery.
  • Hyun-Woo Shin
    OUaR LaB, Inc, Seoul, Republic of Korea.

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