Deep transfer learning for improving single-EEG arousal detection

Alexander Neergaard Olesen, Poul Jennum, Emmanuel Mignot, Helge Bjarup Dissing Sørensen

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

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
Title of host publicationProceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society
PublisherIEEE
Publication date2020
Article number9176723
ISBN (Print)978-1-7281-1990-8
DOIs
Publication statusPublished - 2020
Event42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society - EMBS Virtual Academy, Montreal, Canada
Duration: 20 Jul 202024 Jul 2020

Conference

Conference42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society
LocationEMBS Virtual Academy
CountryCanada
CityMontreal
Period20/07/202024/07/2020

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