Transferring Structured Knowledge in Unsupervised Domain Adaptation of a Sleep Staging Network

Automatic sleep staging based on deep learning (DL) has been attracting attention for analyzing sleep quality and determining treatment effects. It is challenging to acquire long-term sleep data from numerous subjects and manually labeling them even though most DL-based models are trained using larg...

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Veröffentlicht in:	IEEE journal of biomedical and health informatics 2022-03, Vol.26 (3), p.1273-1284
Hauptverfasser:	Yoo, Chaehwa, Lee, Hyang Woon, Kang, Je-Won
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptation Adaptation models Adversarial machine learning Attention Brain modeling Datasets Deep learning Discriminators Domains Electroencephalography Feature extraction Humans knowledge transfer local alignment Minimax technique Optimization Performance enhancement Performance evaluation Sleep Sleep Stages Sleep staging Training Transfer learning unsupervised domain adaptation
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