EEG-Based Sleep Stage Classification via Neural Architecture Search

Kong, Gangwei; Li, Chang; Peng, Hu; Han, Zhihui; Qiao, Heyuan

doi:10.1109/tnsre.2023.3238764

Cited by 20 publications

(4 citation statements)

References 50 publications

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“…When in a home-based setting, it is hard to guarantee the high quality of the collected EEG signals due to its sensitivity to environment. The existing mainstream end-toend sleep staging models require both EEG and EOG signals with high qualities to build sleep staging task to achieve good performance [50]. Fortunately, we can collect high-quality EOG signals at home due to their low environmental requirements.…”

Section: Discussionmentioning

confidence: 99%

Simplifying Multimodal With Single EOG Modality for Automatic Sleep Staging

Zhou,

Zhao,

Wang

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Polysomnography (PSG) recordings have been widely used for sleep staging in clinics, containing multiple modality signals (i.e., EEG and EOG). Recently, many studies have combined EEG and EOG modalities for sleep staging, since they are the most and the second most powerful modality for sleep staging among PSG recordings, respectively. However, EEG is complex to collect and sensitive to environment noise or other body activities, imbedding its use in clinical practice. Comparatively, EOG is much more easily to be obtained. In order to make full use of the powerful ability of EEG and the easy collection of EOG, we propose a novel framework to simplify multimodal sleep staging with a single EOG modality. It still performs well with only EOG modality in the absence of the EEG. Specifically, we first model the correlation between EEG and EOG, and then based on the correlation we generate multimodal features with time and frequency guided generators by adopting the idea of generative adversarial learning. We collected a real-world sleep dataset containing 67 recordings and used other four public datasets for evaluation. Compared with other existing sleep staging methods, our framework performs the best when solely using the EOG modality. Moreover, under our framework, EOG provides a comparable performance to EEG.

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Section: Discussionmentioning

confidence: 99%

Simplifying Multimodal With Single EOG Modality for Automatic Sleep Staging

Zhou,

Zhao,

Wang

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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show abstract

“…However, there is no universal approach to guide the design of deep learning models for arbitrary classification tasks, given the diversity in layer modules and optimization strategies. Furthermore, besides the experiments presented in this study, there are various types of datasets available for further validation [30], [31],…”

Section: Repeat Relu-lcn Leaky-lcnmentioning

confidence: 99%

“…NAS has demonstrated advancements in improving model performance across various applications, such as image processing [24], [25], semantic segmentation [26], [27], and object detection [28], [29]. Recent research also explores the use of NAS for healthcare applications, such as electroencephalography (EEG) data processing [30], muscle fatigue detection [31], cardiac abnormality diagnosis [32], and heartbeat classification [33]. Moreover, an NAS was developed by leveraging k-fold cross-validation, and the deep learning model was evaluated on data from the UCR archive [34].…”

mentioning

confidence: 99%

AutoNet-Generated Deep Layer-Wise Convex Networks for ECG Classification

Shen,

Lu,

Zhu

et al. 2024

IEEE Trans. Pattern Anal. Mach. Intell.

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The design of neural networks typically involves trial-and-error, a time-consuming process for obtaining an optimal architecture, even for experienced researchers. Additionally, it is widely accepted that loss functions of deep neural networks are generally non-convex with respect to the parameters to be optimised. We propose the Layer-wise Convex Theorem to ensure that the loss is convex with respect to the parameters of a given layer, achieved by constraining each layer to be an overdetermined system of non-linear equations. Based on this theorem, we developed an end-to-end algorithm (the AutoNet) to automatically generate layer-wise convex networks (LCNs) for any given training set. We then demonstrate the performance of the AutoNet-generated LCNs (AutoNet-LCNs) compared to state-of-the-art models on three electrocardiogram (ECG) classification benchmark datasets, with further validation on two non-ECG benchmark datasets for more general tasks. The AutoNet-LCN was able to find networks customised for each dataset without manual fine-tuning under 2 GPU-hours, and the resulting networks outperformed the state-of-the-art models with fewer than 5% parameters on all the above five benchmark datasets. The efficiency and robustness of the AutoNet-LCN markedly reduce model discovery costs and enable efficient training of deep learning models in resource-constrained settings.

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“…Phan et al (2021) [17] also compared the performances of CNNs, recurrent neural networks (RNNs), and transformer networks on a large-scale dataset of PSG recordings, and found that the transformer networks outperformed the CNNs and RNNs in accuracy and generalization to unseen data. Kong et al (2023) [18] proposed a novel neural architecture search (NAS) search framework for EEG-based sleep staging. It optimized the model by search-space Jinji and search-space regularization and sharing parameters between units.…”

Section: Automatic Sleep Staging Algorithm Modelmentioning

confidence: 99%

Sleep CLIP: A Multimodal Sleep Staging Model Based on Sleep Signals and Sleep Staging Labels

Yang,

Wang,

et al. 2023

Sensors

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Since the release of the contrastive language-image pre-training (CLIP) model designed by the OpenAI team, it has been applied in several fields owing to its high accuracy. Sleep staging is an important method of diagnosing sleep disorders, and the completion of sleep staging tasks with high accuracy has always remained the main goal of sleep staging algorithm designers. This study is aimed at designing a multimodal model based on the CLIP model that is more suitable for sleep staging tasks using sleep signals and labels. The pre-training efforts of the model involve five different training sets. Finally, the proposed method is tested on two training sets (EDF-39 and EDF-153), with accuracies of 87.3 and 85.4%, respectively.

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EEG-Based Sleep Stage Classification via Neural Architecture Search

Cited by 20 publications

References 50 publications

Simplifying Multimodal With Single EOG Modality for Automatic Sleep Staging

Simplifying Multimodal With Single EOG Modality for Automatic Sleep Staging

AutoNet-Generated Deep Layer-Wise Convex Networks for ECG Classification

Sleep CLIP: A Multimodal Sleep Staging Model Based on Sleep Signals and Sleep Staging Labels

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