1. MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-Signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning
- Author
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Nat Dilokthanakul, Payongkit Lakhan, Pitshaporn Leelaarporn, Nannapas Banluesombatkul, Theerawit Wilaiprasitporn, Pichayoot Ouppaphan, Nattapong Jaimchariyatam, Huy Phan, Ekapol Chuangsuwanich, Busarakum Chaitusaney, and Wei Chen
- Subjects
Signal Processing (eess.SP) ,FOS: Computer and information sciences ,Computer Science - Machine Learning ,Source code ,Computer science ,Polysomnography ,media_common.quotation_subject ,0206 medical engineering ,Feature extraction ,Pilot Projects ,02 engineering and technology ,Machine learning ,computer.software_genre ,Machine Learning (cs.LG) ,Data modeling ,03 medical and health sciences ,0302 clinical medicine ,Health Information Management ,FOS: Electrical engineering, electronic engineering, information engineering ,Humans ,Electrical Engineering and Systems Science - Signal Processing ,Electrical and Electronic Engineering ,media_common ,Sleep Stages ,business.industry ,Deep learning ,Electroencephalography ,Workload ,020601 biomedical engineering ,Computer Science Applications ,Quantitative Biology - Neurons and Cognition ,FOS: Biological sciences ,Task analysis ,Neurons and Cognition (q-bio.NC) ,Artificial intelligence ,Sleep ,business ,Transfer of learning ,computer ,030217 neurology & neurosurgery ,Biotechnology - Abstract
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. 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., Comment: IEEE Journal of Biomedical and Health Informatics (Accepted) (source code is available at https://github.com/IoBT-VISTEC/MetaSleepLearner)
- Published
- 2021
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