计算机科学
突出
人工智能
深度学习
睡眠(系统调用)
人工神经网络
机器学习
睡眠阶段
特征(语言学)
深层神经网络
多导睡眠图
脑电图
心理学
哲学
精神科
操作系统
语言学
作者
Jaeun Phyo,Wonjun Ko,Eunjin Jeon,Heung-Il Suk
出处
期刊:IEEE transactions on cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2023-07-01
卷期号:53 (7): 4500-4510
被引量:5
标识
DOI:10.1109/tcyb.2022.3198997
摘要
Sleep staging is essential for sleep assessment and plays a vital role as a health indicator. Many recent studies have devised various machine/deep learning methods for sleep staging. However, two key challenges hinder the practical use of those methods: 1) effectively capturing salient waveforms in sleep signals and 2) correctly classifying confusing stages in transitioning epochs. In this study, we propose a novel deep neural-network structure, TransSleep, that captures distinctive local temporal patterns and distinguishes confusing stages using two auxiliary tasks. In particular, TransSleep captures salient waveforms in sleep signals by an attention-based multiscale feature extractor and correctly classifies confusing stages in transitioning epochs, while modeling contextual relationships with two auxiliary tasks. Results show that TransSleep achieves promising performance in automatic sleep staging. The validity of TransSleep is demonstrated by its state-of-the-art performance on two publicly available datasets: 1) Sleep-EDF and 2) MASS. Furthermore, we performed ablations to analyze our results from different perspectives. Based on our overall results, we believe that TransSleep has immense potential to provide new insights into deep-learning-based sleep staging.
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