<i>CoSleep</i>: A Multi-View Representation Learning Framework for Self-Supervised Learning of Sleep Stage Classification

Ye, Jianan; Xiao, Qinfeng; Wang, Jing; Zhang, Hongjun; Deng, Jiaoxue; Lin, Youfang

doi:10.1109/lsp.2021.3130826

Cited by 29 publications

(24 citation statements)

References 20 publications

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“…In addition, SSLAPP developed a contrastive learning approach with attention-based augmentations in the embedding space to add more positive pairs [26]. Last, CoSleep [14] and SleepECL [27] are yet another two contrastive methods that exploit information, e.g., inter-epoch dependency and frequency domain views, from EEG data to obtain more positive pairs for contrastive learning.…”

Section: Self-supervised Learning For Sleep Stagingmentioning

confidence: 99%

“…We compare the performance of the adopted pretrained SSC models against state-of-the-art self-supervised methods proposed specifically for the sleep stage classification problem. the reported results of SleepDPC [25], CoSleep [14], and SS-LAPP [26] on Sleep-EDF dataset. We compare these methods against existing SSC models with the best-performing SSL method.…”

Section: Comparison With Baselinesmentioning

confidence: 99%

“…Currently, SSL algorithms can produce state-of-the-art performance on standard computer vision benchmarks [9], [11]- [13]. Consequently, the SSL paradigm has gained more interest to be applied for sleep stage classification problem [8], [14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluation

Eldele¹,

Ragab²,

Chen³

et al. 2022

Preprint

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The past few years have witnessed a remarkable advance in deep learning for EEG-based sleep stage classification (SSC). However, the success of these models is attributed to possessing a massive amount of labeled data for training, limiting their applicability in real-world scenarios. In such scenarios, sleep labs can generate a massive amount of data, but labeling these data can be expensive and time-consuming. Recently, the selfsupervised learning (SSL) paradigm has shined as one of the most successful techniques to overcome the scarcity of labeled data. In this paper, we evaluate the efficacy of SSL to boost the performance of existing SSC models in the few-labels regime. We conduct a thorough study on three SSC datasets, and we find that fine-tuning the pretrained SSC models with only 5% of labeled data can achieve competitive performance to the supervised training with full labels. Moreover, self-supervised pretraining helps SSC models to be more robust to data imbalance and domain shift problems.

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Section: Self-supervised Learning For Sleep Stagingmentioning

confidence: 99%

Section: Comparison With Baselinesmentioning

confidence: 99%

See 1 more Smart Citation

Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluation

Eldele¹,

Ragab²,

Chen³

et al. 2022

Preprint

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show abstract

“…Compared with manual staging, it saves time and effort. It can not only improve the efficiency of data judgment and classification, but also reduce the misjudgment caused by doctors' subjective factors in the process of staging ( 11 , 12 ).…”

Section: Related Workmentioning

confidence: 99%

Intelligent automatic sleep staging model based on CNN and LSTM

Liu

Dai

Zhou

et al. 2022

Front. Public Health

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Since electroencephalogram (EEG) is a significant basis to treat and diagnose somnipathy, sleep electroencephalogram automatic staging methods play important role in the treatment and diagnosis of sleep disorders. Due to the characteristics of weak signals, EEG needs accurate and efficient algorithms to extract feature information before applying it in the sleep stages. Conventional feature extraction methods have low efficiency and are difficult to meet the time validity of fast staging. In addition, it can easily lead to the omission of key features owing to insufficient a priori knowledge. Deep learning networks, such as convolutional neural networks (CNNs), have powerful processing capabilities in data analysis and data mining. In this study, a deep learning network is introduced into the study of the sleep stage. In this study, the feature fusion method is presented, and long-term and short-term memory (LSTM) is selected as the classification network to improve the accuracy of sleep stage recognition. First, based on EEG and deep learning network, an automatic sleep phase method based on a multi-channel EGG is proposed. Second, CNN-LSTM is used to monitor EEG and EOG samples during sleep. In addition, without any signal preprocessing or feature extraction, data expansion (DA) can be realized for unbalanced data, and special data and non-general data can be deleted. Finally, the MIT-BIH dataset is used to train and evaluate the proposed model. The experimental results show that the EEG-based sleep phase method proposed in this paper provides an effective method for the diagnosis and treatment of sleep disorders, and hence has a practical application value.

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“…Currently, SSL algorithms can produce state-of-the-art performance on standard computer vision benchmarks [6]- [9]. Consequently, the SSL paradigm has gained more interest to be applied for sleep stage classification problem [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Self-Supervised Learning for Label- Efficient Sleep Stage Classification: A Comprehensive Evaluation

Eldele

Ragab

Chen

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

The past few years have witnessed a remarkable advance in deep learning for EEG-based sleep stage classification (SSC). However, the success of these models is attributed to possessing a massive amount of labeled data for training, limiting their applicability in real-world scenarios. In such scenarios, sleep labs can generate a massive amount of data, but labeling can be expensive and time-consuming. Recently, the self-supervised learning (SSL) paradigm has emerged as one of the most successful techniques to overcome labels' scarcity. In this paper, we evaluate the efficacy of SSL to boost the performance of existing SSC models in the few-labels regime. We conduct a thorough study on three SSC datasets, and we find that finetuning the pretrained SSC models with only 5% of labeled data can achieve competitive performance to the supervised training with full labels. Moreover, self-supervised pretraining helps SSC models to be more robust to data imbalance and domain shift problems.

show abstract

CoSleep: A Multi-View Representation Learning Framework for Self-Supervised Learning of Sleep Stage Classification

Cited by 29 publications

References 20 publications

Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluation

Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluation

Intelligent automatic sleep staging model based on CNN and LSTM

Self-Supervised Learning for Label- Efficient Sleep Stage Classification: A Comprehensive Evaluation

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