ADAST: Attentive Cross-Domain EEG-Based Sleep Staging Framework With Iterative Self-Training

Eldele, Emadeldeen; Ragab, Mohamed; Chen, Zhenghua; Wu, Min; Kwoh, Chee Keong; Li, Xiaoli; Guan, Cuntai

doi:10.1109/tetci.2022.3189695

Cited by 19 publications

(10 citation statements)

References 31 publications

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“…This distribution shift can be caused by a different data collection methodology or differences in subjects' health status. To deal with this challenging scenario, some recent works proposed transfer learning and unsupervised domain adaptation algorithms to mitigate the domain shift [38]- [41].…”

Section: E Robustness To Domain-shiftmentioning

confidence: 99%

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: E Robustness To Domain-shiftmentioning

confidence: 99%

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

Eldele¹,

Ragab²,

Chen³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Disease influences brain activity and, consequently, PSG recordings, creating uncertainty in the data. Moreover, models trained on data acquired with one measurement setup or device may not generalize well to data obtained using a different measurement protocol, resulting in the well-known distribution shift problem [22,23,24,25,26,27]. Hence, measurement factors can also contribute to uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Feature matching as improved transfer learning technique for wearable EEG

Heremans

Phan

Ansari

et al. 2022

Biomedical Signal Processing and Control

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“…The local discriminators will preserve the intrinsic structure of sleep data and reduce local misalignment. Eldele et al ( 2022 ) used a dual classifier for the adversarial domain adaptation framework to improve the accuracy of the decision boundary.…”

Section: Methodsmentioning

confidence: 99%

Automatic sleep staging of single-channel EEG based on domain adversarial neural networks and domain self-attention

Gao

Wang

et al. 2023

Front. Neurosci.

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The diagnosis and management of sleep problems depend heavily on sleep staging. For autonomous sleep staging, many data-driven deep learning models have been presented by trying to construct a large-labeled auxiliary sleep dataset and test it by electroencephalograms on different subjects. These approaches suffer a significant setback cause it assumes the training and test data come from the same or similar distribution. However, this is almost impossible in scenario cross-dataset due to inherent domain shift between domains. Unsupervised domain adaption was recently created to address the domain shift issue. However, only a few customized UDA solutions for sleep staging due to two limitations in previous UDA methods. First, the domain classifier does not consider boundaries between classes. Second, they depend on a shared model to align the domain that could miss the information of domains when extracting features. Given those restrictions, we present a novel UDA approach that combines category decision boundaries and domain discriminator to align the distributions of source and target domains. Also, to keep the domain-specific features, we create an unshared attention method. In addition, we investigated effective data augmentation in cross-dataset sleep scenarios. The experimental results on three datasets validate the efficacy of our approach and show that the proposed method is superior to state-of-the-art UDA methods on accuracy and MF1-Score.

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ADAST: Attentive Cross-Domain EEG-Based Sleep Staging Framework With Iterative Self-Training

Cited by 19 publications

References 31 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

Feature matching as improved transfer learning technique for wearable EEG

Automatic sleep staging of single-channel EEG based on domain adversarial neural networks and domain self-attention

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