• English
  • Deutsch
  • Log In
    Password Login
    Research Outputs
    Fundings & Projects
    Researchers
    Institutes
    Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Scopus
  4. Transfer Learning for Automatic Sleep Staging Using a Pre-Gelled Electrode Grid
 
  • Details
  • Full
Options
2024
Journal Article
Title

Transfer Learning for Automatic Sleep Staging Using a Pre-Gelled Electrode Grid

Abstract
Novel sensor solutions for sleep monitoring at home could alleviate bottlenecks in sleep medical care as well as enable selective or continuous observation over long periods of time and contribute to new insights in sleep medicine and beyond. Since especially in the latter case the sensor data differ strongly in signal, number and extent of sensors from the classical polysomnography (PSG) sensor technology, an automatic evaluation is essential for the application. However, the training of an automatic algorithm is complicated by the fact that the development phase of the new sensor technology, extensive comparative measurements with standardized reference systems, is often not possible and therefore only small datasets are available. In order to circumvent high system-specific training data requirements, we employ pre-training on large datasets with finetuning on small datasets of new sensor technology to enable automatic sleep phase detection for small test series. By pre-training on publicly available PSG datasets and finetuning on 12 nights recorded with new sensor technology based on a pre-gelled electrode grid to capture electroencephalography (EEG), electrooculography (EOG) and electromyography (EMG), an (Formula presented.) score across all sleep phases of 0.81 is achieved (wake 0.84, N1 0.62, N2 0.81, N3 0.87, REM 0.88), using only EEG and EOG. The analysis additionally considers the spatial distribution of the channels and an approach to approximate classical electrode positions based on specific linear combinations of the new sensor grid channels.
Author(s)
Radke, Fabian Alexander
Fraunhofer-Institut für Digitale Medientechnologie IDMT  
Silva Souto, Carlos F. da
Fraunhofer-Institut für Digitale Medientechnologie IDMT  
Pätzold, Wiebke  
Fraunhofer-Institut für Digitale Medientechnologie IDMT  
Wolf, Karen Insa  
Fraunhofer-Institut für Digitale Medientechnologie IDMT  
Journal
Diagnostics  
Open Access
DOI
10.3390/diagnostics14090909
Additional full text version
Landing Page
Language
English
Fraunhofer-Institut für Digitale Medientechnologie IDMT  
Keyword(s)
  • EEG

  • electrode grid

  • home monitoring

  • machine learning

  • sleep staging

  • transfer learning

  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Contact
© 2024