SHNN: A single-channel EEG sleep staging model based on semi-supervised learning

Zhang, Yongqing; Cao, Wenpeng; Feng, Li-Xiao; Wang, Manqing; Geng, Tianyu; Zhou, Jiliu; Gao, Dongrui

doi:10.1016/j.eswa.2022.119288

Cited by 29 publications

(8 citation statements)

References 30 publications

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“…In [68], the authors showed the power of multi-scale modeling to capture fast and slow varying features. To improve upon this approach, many papers used different variations of the multi-scale approach to better capture the features for downstream tasks [14,18,[124][125][126][126][127][128][129][130][131][132][133][134][135]. Some papers followed the direction in [84] and added attention modules to their models to improve performance [18,30,80,84,126,126,[136][137][138][139][140][141][142][143][144][145][146][147][148].…”

Section: Convolutional and Recurrent Modelsmentioning

confidence: 99%

Advances in Modeling and Interpretability of Deep Neural Sleep Staging: A Systematic Review

Soleimani,

Barahona,

Chen

et al. 2023

Physiologia

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Sleep staging has a very important role in diagnosing patients with sleep disorders. In general, this task is very time-consuming for physicians to perform. Deep learning shows great potential to automate this process and remove physician bias from decision making. In this study, we aim to identify recent trends on performance improvement and the causes for these trends. Recent papers on sleep stage classification and interpretability are investigated to explore different modeling and data manipulation techniques, their efficiency, and recent advances. We identify an improvement in performance up to 12% on standard datasets over the last 5 years. The improvements in performance do not appear to be necessarily correlated to the size of the models, but instead seem to be caused by incorporating new architectural components, such as the use of transformers and contrastive learning.

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Section: Convolutional and Recurrent Modelsmentioning

confidence: 99%

Advances in Modeling and Interpretability of Deep Neural Sleep Staging: A Systematic Review

Soleimani,

Barahona,

Chen

et al. 2023

Physiologia

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“…To date, several widely used indicators have been proposed for detecting driving fatigue, such as monitoring fatigue-related facial expressions, blinking, yawning (Wang Z. et al, 2020 ), muscle (Zhang et al, 2021a ), and measuring fatigue-related physiological variables such as electrooculography (Zheng and Lu, 2017 ), heart rate variability (Du et al, 2020a ), and electroencephalography (Zhang et al, 2020 ). Of the many fatigue detection indicators, EEG signals are good mental indicators (Zhang Y. et al, 2022 ). Because the EEG signal is closely related to brain activity (Zhang X. et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

EEG driving fatigue detection based on log-Mel spectrogram and convolutional recurrent neural networks

Gao

Xue²,

Wan³

et al. 2023

Front. Neurosci.

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Driver fatigue detection is one of the essential tools to reduce accidents and improve traffic safety. Its main challenge lies in the problem of how to identify the driver's fatigue state accurately. Existing detection methods include yawning and blinking based on facial expressions and physiological signals. Still, lighting and the environment affect the detection results based on facial expressions. In contrast, the electroencephalographic (EEG) signal is a physiological signal that directly responds to the human mental state, thus reducing the impact on the detection results. This paper proposes a log-Mel spectrogram and Convolution Recurrent Neural Network (CRNN) model based on EEG to implement driver fatigue detection. This structure allows the advantages of the different networks to be exploited to overcome the disadvantages of using them individually. The process is as follows: first, the original EEG signal is subjected to a one-dimensional convolution method to achieve a Short Time Fourier Transform (STFT) and passed through a Mel filter bank to obtain a logarithmic Mel spectrogram, and then the resulting logarithmic Mel spectrogram is fed into a fatigue detection model to complete the fatigue detection task for the EEG signals. The fatigue detection model consists of a 6-layer convolutional neural network (CNN), bi-directional recurrent neural networks (Bi-RNNs), and a classifier. In the modeling phase, spectrogram features are transported to the 6-layer CNN to automatically learn high-level features, thereby extracting temporal features in the bi-directional RNN to obtain spectrogram-temporal information. Finally, the alert or fatigue state is obtained by a classifier consisting of a fully connected layer, a ReLU activation function, and a softmax function. Experiments were conducted on publicly available datasets in this study. The results show that the method can accurately distinguish between alert and fatigue states with high stability. In addition, the performance of four existing methods was compared with the results of the proposed method, all of which showed that the proposed method could achieve the best results so far.

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“…Du et al (2021) proposed a deep learning framework for Takagi-Sugeno-Kang (TSK) type convolutional recursive fuzzy network, which can extract spatiotemporal features from EEG signals. Zhang et al, (2023) proposed a hybrid neural network for extracting features from single-channel EEG. In 2023, Peng et al (2023) (Schirrmeister et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

3D-STCNN: Spatiotemporal Convolutional Neural Network Based on EEG 3D Features for Detecting Driving Fatigue

Peng

Gao

Wang

et al. 2023

JDSIS

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Fatigue driving has become one of the main causes of traffic accidents, and driving fatigue detection based on electroencephalogram (EEG) can effectively evaluate the driver’s mental state and avoid the occurrence of traffic accidents. This article evaluates a feature extraction method for extracting multiple features of EEG signals and establishes a Spatiotemporal Convolutional Neural Network (STCNN) to detect driver fatigue. Firstly, we constructed a three-dimensional feature of the EEG signal, which includes the frequency domain, time domain, and spatial features of the EEG signal. Then, we use STCNN for fatigue state classification. STCNN is composed of an Attention Time Network (ATNet) based on attention mechanism and an Attention Convolutional Neural Network (ACNN) based on attention mechanism. In addition, we conducted fatigue driving experiments and collected EEG signals from 14 subjects in both awake and fatigued states, ultimately collecting EEG data under three different driving task loads. We conducted extensive experiments on this basis and compared the effectiveness of STCNN and six competitive methods. The results show that the classification accuracy of STCNN is 87.55%, which can effectively detect the fatigue status of drivers.

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SHNN: A single-channel EEG sleep staging model based on semi-supervised learning

Cited by 29 publications

References 30 publications

Advances in Modeling and Interpretability of Deep Neural Sleep Staging: A Systematic Review

Advances in Modeling and Interpretability of Deep Neural Sleep Staging: A Systematic Review

EEG driving fatigue detection based on log-Mel spectrogram and convolutional recurrent neural networks

3D-STCNN: Spatiotemporal Convolutional Neural Network Based on EEG 3D Features for Detecting Driving Fatigue

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