Work

Deep transfer learning for improving single-EEG arousal detection

Year: 2020

Type: conference-paper

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.

Source: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society

DOI: 10.1109/embc44109.2020.9176723

Authors: Alexander Neergaard Olesen, Poul Jennum, Emmanuel Mignot, Helge B. D. Sorensen

Institutions: Technical University of Denmark, Copenhagen University Hospital, Stanford University

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

Funders: Augustinus Fonden, Danmarks Tekniske Universitet, Knud Højgaards Fond, Stanford Research Computing Center, Stanford University