Unsupervised Domain Adaptation by Statistics Alignment for Deep Sleep Staging Networks

Fan, Jiahao; Zhu, Hangyu; Jiang, Xinyu; Meng, Long; Chen, Chen; Fu, Cong; Yu, Huan; Dai, Chenyun; Chen, Wei

doi:10.1109/tnsre.2022.3144169

Cited by 27 publications

(8 citation statements)

References 27 publications

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“…For example, encouraging results have been reported using adversarial domain adaption methods to match the feature distributions of the source and target domains via gradient reversal from a domain classifier that is tasked to discriminate between the two domains (Nasiri and Clifford 2020, Yoo et al 2021. A pretrained network can be also be adapted to a target domain by modulating the domain-specific statistics of deep features stored in the network's normalization layers like batch normalization (Fan et al 2022). While the reliance on target data labels are costly as human scoring is required, in general, the performance gains are proportional to the amount of labelled data.…”

Section: Interpretabilitymentioning

confidence: 99%

Automatic sleep staging of EEG signals: recent development, challenges, and future directions

Phan

Mikkelsen

2022

Physiol. Meas.

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Modern deep learning holds a great potential to transform clinical practice on human sleep. Teaching a machine to carry out routine tasks would be a tremendous reduction in workload for clinicians. Sleep staging, a fundamental step in sleep practice, is a suitable task for this and will be the focus in this article. Recently, automatic sleep staging systems have been trained to mimic manual scoring, leading to similar performance to human sleep experts, at least on scoring of healthy subjects. Despite tremendous progress, we have not seen automatic sleep scoring adopted widely in clinical environments. This review aims to give a shared view of the authors on the most recent state-of-the-art development in automatic sleep staging, the challenges that still need to be addressed, and the future directions for automatic sleep scoring to achieve clinical value.

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Section: Interpretabilitymentioning

confidence: 99%

Automatic sleep staging of EEG signals: recent development, challenges, and future directions

Phan

Mikkelsen

2022

Physiol. Meas.

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“…The second baseline was the result obtained with a simple adaptation of the normalization statistics in the batch norm layers of the sleep staging network. This commonly known domain adaptation technique [28] was recently demonstrated in personalized sleep staging [40]. The method adjusts normalization statistics to a target domain without training any network weights.…”

Section: Personalizationmentioning

confidence: 99%

From unsupervised to semi-supervised adversarial domain adaptation in electroencephalography-based sleep staging

Heremans¹,

Phan²,

Borzée³

et al. 2022

J. Neural Eng.

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Objective. The recent breakthrough of wearable sleep monitoring devices results in large amounts of sleep data. However, as limited labels are available, interpreting these data requires automated sleep stage classification methods with a small need for labeled training data. Transfer learning and domain adaptation offer possible solutions by enabling models to learn on a source dataset and adapt to a target dataset. Approach. In this paper, we investigate adversarial domain adaptation applied to real use cases with wearable sleep datasets acquired from diseased patient populations. Different practical aspects of the adversarial domain adaptation framework \hl{are examined}, including the added value of (pseudo-)labels from the target dataset and the influence of domain mismatch between the source and target data. The method is also implemented for personalization to specific patients. Main results. The results show that adversarial domain adaptation is effective in the application of sleep staging on wearable data. When compared to a model applied on a target dataset without any adaptation, the domain adaptation method in its simplest form achieves relative gains of 7%-27% in accuracy. The performance on the target domain is further boosted by adding pseudo-labels and real target domain labels when available, and by choosing an appropriate source dataset. Furthermore, unsupervised adversarial domain adaptation can also personalize a model, improving the performance by 1%-2% compared to a non-personal model. Significance. In conclusion, adversarial domain adaptation provides a flexible framework for semi-supervised and unsupervised transfer learning. This is particularly useful in sleep staging and other wearable EEG applications.

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“…In [21] an adversarial domain adaptation network was created for Electroencephalogram (EEG) classification, which both aligns the marginal distributions of different domains and aims for decreasing the sub-domain shift. Unsupervised domain alignment was also used in [22] for deep sleep staging, while an adversarial domain-adaptive technique was developed in [23] to detect fall events of elderly patients using sensors during different device placement and configuration scenarios. Finally, in [24] an unsupervised domain adaptation method combined with a self-guided adaptive sampling scheme was used to account for instantaneous domain shift during classifier updates.…”

Section: B Domain Adaptationmentioning

confidence: 99%

Multi-Action Knee Contact Force Prediction by Domain Adaptation

Loi,

Zacharaki,

Moustakas

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Most recent musculoskeletal dynamics estimation methods are designed for predefined actions, such as gait, and don't generalize to various tasks. In this work, we address the problem of estimating internal biomechanical forces during more than one actions by introducing unsupervised domain adaptation into a deep learning model. More specifically, we developed a Bidirectional Long Short-Term Memory network for knee contact force prediction, enhanced with correlation alignment layers, in order to minimize the domain shift between kinematic data from different actions. Furthermore, we used the novel Neural State Machine (NSM) as a simulation platform to test and visualize our model predictions in a wide range of trajectories adapted to different 3D scene geometries in real-time. We conducted multiple experiments, including comparison with previous models, model alignment across action classes and real-to-synthetic data alignment. The results showed that the proposed deep learning architecture with domain adaptation performs better than the benchmark in terms of NRMSE and t-test. Overall, our method is capable of predicting knee contact forces for more than one action classes using a single architecture and thereby opens the path for estimating internal forces for intermediate actions, while the knowledge of the hidden state of motion may be used to support personalized rehabilitation. Moreover, our model can be easily integrated into any human motion simulation environment, which shows its potential in enabling biomechanical analysis in an automated and computationally efficient way.

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Unsupervised Domain Adaptation by Statistics Alignment for Deep Sleep Staging Networks

Cited by 27 publications

References 27 publications

Automatic sleep staging of EEG signals: recent development, challenges, and future directions

Automatic sleep staging of EEG signals: recent development, challenges, and future directions

From unsupervised to semi-supervised adversarial domain adaptation in electroencephalography-based sleep staging

Multi-Action Knee Contact Force Prediction by Domain Adaptation

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