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

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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Bibliographic Details
Main Authors: Banluesombatkul, N. (Author), Chaitusaney, B. (Author), Chen, W. (Author), Chuangsuwanich, E. (Author), Dilokthanakul, N. (Author), Jaimchariyatam, N. (Author), Lakhan, P. (Author), Leelaarporn, P. (Author), Ouppaphan, P. (Author), Phan, H. (Author), Wilaiprasitporn, T. (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:
adult
Agnostic
article
Automatic systems
Biomedical signal processing
classifier
controlled study
Conventional approach
convolutional neural network
Deep learning
electroencephalogram
electroencephalography
Electroencephalography
feasibility study
feature extraction
female
Healthy subjects
human
Human-machine collaboration
Humans
Large dataset
Learning approach
Learning frameworks
Learning systems
male
meta-learning
Model interpretations
Pilot Projects
pilot study
polysomnography
Polysomnography
pre-trained EEG
sleep
Sleep
Sleep research
sleep stage
Sleep stage classification
Sleep Stages
Statistical differences
transfer learning
Transfer learning
transfer of learning
workload
Online Access:View Fulltext in Publisher

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