2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDBIO-11.5
Paper Title SELF-SUPERVISED LEARNING FOR SLEEP STAGE CLASSIFICATION WITH PREDICTIVE AND DISCRIMINATIVE CONTRASTIVE CODING
Authors Qinfeng Xiao, Jing Wang, Jianan Ye, Hongjun Zhang, Yuyan Bu, Yiqiong Zhang, Hao Wu, Beijing Jiaotong University, China
SessionBIO-11: Deep Learning for Physiological Signals
LocationGather.Town
Session Time:Thursday, 10 June, 13:00 - 13:45
Presentation Time:Thursday, 10 June, 13:00 - 13:45
Presentation Poster
Topic Biomedical Imaging and Signal Processing: [BIO] Biomedical signal processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract The purpose of this paper is to learn efficient representations from raw electroencephalogram (EEG) signals for sleep stage classification via self-supervised learning (SSL). Although supervised methods have gained favorable performance, they heavily rely on manually labeled datasets. Recently, SSL arrives comparable performance with fully supervised methods despite limited labeled data by extracting high-level semantic representations. To alleviate the severe reliance of labels, we propose SleepDPC, a novel sleep stage classification algorithm based on SSL. By incorporating two dedicated predictive and discriminative learning principles, SleepDPC discovers underlying semantics from raw EEG signals in a more efficient manner. We thoroughly evaluate the performance of our proposed method on two publicly available datasets. The experimental results show that our method not only learns meaningful representations but also produces superior performance versus various competing methods despite limited access of labeled data.