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Deep transfer learning for improving single-EEG arousal detection

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  • Alexander Neergaard Olesen
  • Poul Jennum
  • Emmanuel Mignot
  • Helge B D Sorensen
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Datasets in sleep science present challenges for machine learning algorithms due to differences in recording setups across clinics. We investigate two deep transfer learning strategies for overcoming the channel mismatch problem for cases where two datasets do not contain exactly the same setup leading to degraded performance in single-EEG models. Specifically, we train a baseline model on multivariate polysomnography data and subsequently replace the first two layers to prepare the architecture for single-channel electroencephalography data. Using a fine-tuning strategy, our model yields similar performance to the baseline model (F1=0.682 and F1=0.694, respectively), and was significantly better than a comparable single-channel model. Our results are promising for researchers working with small databases who wish to use deep learning models pre-trained on larger databases.

Original languageEnglish
JournalProceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society
Volume2020
Pages (from-to)99-103
Number of pages5
ISSN2375-7477
DOIs
Publication statusPublished - Jul 2020

    Research areas

  • Arousal, Deep Learning, Electroencephalography, Machine Learning, Neural Networks, Computer

ID: 61826705