MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-Signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning

• Published in: IEEE journal of biomedical and health informatics Vol. 25; no. 6; pp. 1949 - 1963
• Main Authors: Banluesombatkul, Nannapas, Ouppaphan, Pichayoot, Leelaarporn, Pitshaporn, Lakhan, Payongkit, Chaitusaney, Busarakum, Jaimchariyatam, Nattapong, Chuangsuwanich, Ekapol, Chen, Wei, Phan, Huy, Dilokthanakul, Nat, Wilaiprasitporn, Theerawit
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation; Brain modeling; Classification; convolutional neural network; Data models; Datasets; Deep learning; Electroencephalography; Feature extraction; Knowledge acquisition; Labelling; Machine learning; meta-learning; pre-trained EEG; Signal classification; Sleep; Sleep stage classification; Source code; Task analysis; Training; Transfer learning
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2020.3037693

Identifying bio-signals based-sleep stages requires time-consuming and tedious labor of skilled clinicians. Deep learning approaches have been introduced in order to challenge the automatic sleep stage classification conundrum. However, the difficulties can be posed in replacing the clinicians with...