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 the automatic system due to the differences in many aspects found in individual bio-signals, causing the inconsistency in the performance of the model on every incoming individual. Thus, we aim to explore the feasibility of using a novel approach, capable of assisting the clinicians and lessening the workload. We propose the transfer learning framework, entitled MetaSleepLearner, based on Model Agnostic Meta-Learning (MAML), in order to transfer the acquired sleep staging knowledge from a large dataset to new individual subjects (source code is available at https://github.com/IoBT-VISTEC/MetaSleepLearner ). The framework was demonstrated to require the labelling of only a few sleep epochs by the clinicians and allow the remainder to be handled by the system. Layer-wise Relevance Propagation (LRP) was also applied to understand the learning course of our approach. In all acquired datasets, in comparison to the conventional approach, MetaSleepLearner achieved a range of 5.4% to 17.7% improvement with statistical difference in the mean of both approaches. The illustration of the model interpretation after the adaptation to each subject also confirmed that the performance was directed towards reasonable learning. MetaSleepLearner outperformed the conventional approaches as a result from the fine-tuning using the recordings of both healthy subjects and patients. This is the first work that investigated a non-conventional pre-training method, MAML, resulting in a possibility for human-machine collaboration in sleep stage classification and easing the burden of the clinicians in labelling the sleep stages through only several epochs rather than an entire recording.