DSSNet: A Deep Sequential Sleep Network for Self-Supervised Representation Learning Based on Single-Channel EEG

Chang, Shuohua; Yang, Zhihong; You, Yuyang; Guo, Xiaoyu

doi:10.1109/lsp.2022.3215086

Cited by 7 publications

(1 citation statement)

References 21 publications

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“…Cosleep is a representational learning framework that is based on a multi-view co-training mechanism that was proposed by Ye et al (2022) , along with a memory module that was added to the framework. Chang et al (2022) proposed DSSNet, which combined the classical framework of DeepSleepNet ( Supratak et al, 2017 ) and the classical self-supervised learning loss function InfoNCE. The TS-TCC was proposed by Eldele et al (2021) , used two different augmentations to get two views and adopted a contextual contrasting module to learn discriminative representations.…”

Section: Introductionmentioning

confidence: 99%

PSNSleep: a self-supervised learning method for sleep staging based on Siamese networks with only positive sample pairs

et al. 2023

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Traditional supervised learning methods require large quantities of labeled data. However, labeling sleep data according to polysomnography by well-trained sleep experts is a very tedious job. In the present day, the development of self-supervised learning methods is making significant progress in many fields. It is also possible to apply some of these methods to sleep staging. This is to remove the dependency on labeled data at the stage of representation extraction. Nevertheless, they often rely too much on negative samples for sample selection and construction. Therefore, we propose PSNSleep, a novel self-supervised learning method for sleep staging based on Siamese networks. The crucial step to the success of our method is to select appropriate data augmentations (the time shift block) to construct the positive sample pair. PSNSleep achieves satisfactory results without relying on any negative samples. We evaluate PSNSleep on Sleep-EDF and ISRUC-Sleep and achieve accuracy of 80.0% and 74.4%. The source code is publicly available at https://github.com/arthurxl/PSNSleep.

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Section: Introductionmentioning

confidence: 99%

PSNSleep: a self-supervised learning method for sleep staging based on Siamese networks with only positive sample pairs

et al. 2023

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A CNN-Transformer Deep Learning Model for Real-time Sleep Stage Classification in an Energy-Constrained Wireless Device

Yao

Liu

2022

Preprint

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This paper proposes a deep learning (DL) model for automatic sleep stage classification based on single-channel EEG data. The DL model features a convolutional neural network (CNN) and transformers. The model was designed to run on energy and memory-constrained devices for real-time operation with local processing. The Fpz-Cz EEG signals from a publicly available Sleep-EDF dataset are used to train and test the model. Four convolutional filter layers were used to extract features and reduce the data dimension. Then, transformers were utilized to learn the time-variant features of the data. To improve performance, we also implemented a subject specific training before the inference (i.e., prediction) stage. With the subject specific training, the F1 score was 0.91, 0.37, 0.84, 0.877, and 0.73 for wake, N1-N3, and rapid eye movement (REM) stages, respectively. The performance of the model was comparable to the state-of-the-art works with significantly greater computational costs. We tested a reduced-sized version of the proposed model on a low-cost Arduino Nano 33 BLE board and it was fully functional and accurate. In the future, a fully integrated wireless EEG sensor with edge DL will be developed for sleep research in pre-clinical and clinical experiments, such as real-time sleep modulation.

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Automatic Sleep Stage Classification by CNN-Transformer-LSTM using single-channel EEG signal

Pham,

Mouček

2023

2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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DSSNet: A Deep Sequential Sleep Network for Self-Supervised Representation Learning Based on Single-Channel EEG

Cited by 7 publications

References 21 publications

PSNSleep: a self-supervised learning method for sleep staging based on Siamese networks with only positive sample pairs

PSNSleep: a self-supervised learning method for sleep staging based on Siamese networks with only positive sample pairs

A CNN-Transformer Deep Learning Model for Real-time Sleep Stage Classification in an Energy-Constrained Wireless Device

Automatic Sleep Stage Classification by CNN-Transformer-LSTM using single-channel EEG signal

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