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

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...

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Veröffentlicht in:	IEEE journal of biomedical and health informatics 2021-06, Vol.25 (6), p.1949-1963
Hauptverfasser:	Banluesombatkul, Nannapas, Ouppaphan, Pichayoot, Leelaarporn, Pitshaporn, Lakhan, Payongkit, Chaitusaney, Busarakum, Jaimchariyatam, Nattapong, Chuangsuwanich, Ekapol, Chen, Wei, Phan, Huy, Dilokthanakul, Nat, Wilaiprasitporn, Theerawit
Format:	Artikel
Sprache:	eng
Schlagworte:	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
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