MetaSleepLearner: Fast Adaptation of Bio-signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning

04/08/2020
by   Nannapas Banluesombatkul, et al.
0

Objective: 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, disadvantages can be posed in replacing the clinicians with the automatic system. Thus, we aim to develop a framework, capable of assisting the clinicians and lessening the workload. Methods: We proposed the transfer learning framework entitled MetaSleepLearner, using a Model Agnostic Meta-Learning (MAML), in order to transfer the acquired sleep staging knowledge from a large dataset to new individual subject. The capability of MAML was elicited for this task by allowing clinicians to label for a few samples and let the rest be handled by the system. Layer-wise Relevance Propagation (LRP) was also applied to understand the learning course of our approach. Results: In all acquired datasets, in comparison to the conventional approach, MetaSleepLearner achieved a range of 6.15 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. Conclusion: MetaSleepLearner outperformed the conventional approach as a result from the fine-tuning using the recordings of both healthy subjects and patients. Significance: This is the first paper that investigated a non-conventional pre-training method, MAML, in this task, resulting in a framework for human-machine collaboration in sleep stage classification, easing the burden of the clinicians in labelling the sleep stages through only several epochs rather than an entire recording.

READ FULL TEXT

page 1

page 10

research
07/27/2022

Towards Sleep Scoring Generalization Through Self-Supervised Meta-Learning

In this work we introduce a novel meta-learning method for sleep scoring...
research
10/02/2017

Deep Convolutional Neural Networks for Interpretable Analysis of EEG Sleep Stage Scoring

Sleep studies are important for diagnosing sleep disorders such as insom...
research
02/25/2022

MetaVA: Curriculum Meta-learning and Pre-fine-tuning of Deep Neural Networks for Detecting Ventricular Arrhythmias based on ECGs

Ventricular arrhythmias (VA) are the main causes of sudden cardiac death...
research
09/12/2023

Sleep Stage Classification Using a Pre-trained Deep Learning Model

One of the common human diseases is sleep disorders. The classification ...
research
02/07/2023

Automatic Sleep Stage Classification with Cross-modal Self-supervised Features from Deep Brain Signals

The detection of human sleep stages is widely used in the diagnosis and ...
research
01/07/2021

RobustSleepNet: Transfer learning for automated sleep staging at scale

Sleep disorder diagnosis relies on the analysis of polysomnography (PSG)...
research
05/05/2023

Contrastive Learning for Sleep Staging based on Inter Subject Correlation

In recent years, multitudes of researches have applied deep learning to ...

Please sign up or login with your details

Forgot password? Click here to reset