Multiclass classification of obstructive sleep apnea/hypopnea based on a convolutional neural network from a single-lead electrocardiogram.

Journal: Physiological measurement
Published Date:

Abstract

OBJECTIVE: In this paper, we propose a convolutional neural network (CNN)-based deep learning architecture for multiclass classification of obstructive sleep apnea and hypopnea (OSAH) using single-lead electrocardiogram (ECG) recordings. OSAH is the most common sleep-related breathing disorder. Many subjects who suffer from OSAH remain undiagnosed; thus, early detection of OSAH is important.

Authors

  • Erdenebayar Urtnasan
  • Jong-Uk Park
    Department of Biomedical Engineering, Yonsei University, Wonju, Gangwondo, Korea.
  • Kyoung-Joung Lee