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

• Published in: IEEE journal of biomedical and health informatics Vol. 26; no. 3; pp. 1273 - 1284
• Main Authors: Yoo, Chaehwa, Lee, Hyang Woon, Kang, Je-Won
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2021.3103614

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 large-scale sleep data to provide state-of-the-art performance. One way to overcome this data shortage is to create a pre-trained network with an existing large-scale dataset (source domain) that is applicable to small cohorts of datasets (target domain); however, discrepancies in data distribution between the domains prevent successful refinement of this approach. In this paper, we propose an unsupervised domain adaptation method for sleep staging networks to reduce discrepancies by re-aligning the domains in the same space and producing domain-invariant features. Specifically, in addition to a classical domain discriminator, we introduce local discriminators - subject and stage - to maintain the intrinsic structure of sleep data to decrease local misalignments while using adversarial learning to play a minimax game between the feature extractor and discriminators. Moreover, we present several optimization schemes during training because the conventional adversarial learning is not effective to our training scheme. We evaluate the performance of the proposed method by examining the staging performances of a baseline network compared with direct transfer (DT) learning in various conditions. The experimental results demonstrate that the proposed domain adaptation significantly improves the performance though it needs no labeled sleep data in target domain.