Sleep Staging Based on Multi Scale Dual Attention Network

Wang, Huafeng; Lu, Chonggang; Zhang, Qi; Hu, Zhimin; Yuan, Xiaodong; Zhang, Pingshu; Liu, Wanquan

doi:10.48550/arxiv.2107.08442

Cited by 2 publications

(3 citation statements)

References 29 publications

(49 reference statements)

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“…To account for the imbalanced nature of the datasets, the macro average F1-score (MF1) was also used, which is defined in Equation (8), where 𝐹1 𝑖 is per-class F1-Score for class 𝑖.…”

Section: Overall Evaluationmentioning

confidence: 99%

“…Furthermore, PSG, which involves recording multiple electrophysiological signals, is expensive, complicated, and impractical for longitudinal sleep monitoring in home settings. PSG can also cause physical discomfort and mental stress, leading to poor sleep quality and disruption of regular sleep patterns [8]. Consequently, there is a growing interest in developing portable, wireless, and wearable devices equipped with low-cost sensors attached to a single location, such as the Fpz channel in the frontal region of the brain, to acquire single-channel EEG signals, facilitating comfortable and convenient sleep monitoring [9].…”

Section: Introductionmentioning

confidence: 99%

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Automatic sleep staging by a hybrid model based on deep 1D-ResNet-SE and LSTM with single-channel raw EEG signals

Gao

2023

Preprint

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Sleep staging is crucial in assessing sleep quality and diagnosing sleep disorders. Recent advances in deep learning methods with electroencephalogram (EEG) signals have shown remarkable success in automatic sleep staging. However, the use of deeper neural networks may lead to the issues of gradient disappearance and explosion, while the non-stationary nature and low signal-to-noise ratio of EEG signals can negatively impact feature representation. To overcome these challenges, we proposed a novel lightweight sequence-to-sequence deep learning model, 1D-ResNet-SE-LSTM, to classify sleep stages into five classes using single-channel raw EEG signals. Our proposed model consists of two main components: a one-dimensional residual convolutional neural network with a squeeze-and-excitation module to extract and reweight features from EEG signals, and a long short-term memory network to capture the transition rules among sleep stages. In addition, we applied the weighted cross-entropy loss function to alleviate the class imbalance problem. We evaluated the performance of our model on two publicly available datasets, Sleep-EDF Expanded and ISRUC-Sleep, and obtained an overall accuracy rate of 86.39% and 81.97%, respectively, along with corresponding macro average F1-scores of 81.95% and 79.94%. Our model outperforms ex-isting sleep staging models, particularly for the N1 stage, where it achieves F1-scores of 59.00% and 55.53%. The kappa coefficient is 0.812 and 0.766 for the Sleep-EDF Expanded and ISRUC-Sleep datasets, respectively, indicating strong agreement with certified sleep experts. We also investigated the effect of different weight coefficient combinations and sequence lengths of EEG epochs used as input to the model on its performance. Furthermore, the ablation study was conducted to evaluate the contribution of each component to the model's performance.

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Section: Overall Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic sleep staging by a hybrid model based on deep 1D-ResNet-SE and LSTM with single-channel raw EEG signals

Gao

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Furthermore, PSG, which involves recording multiple electrophysiological signals, is expensive, complicated, and impractical for longitudinal sleep monitoring in home settings. PSG can also cause physical discomfort and mental stress, leading to poor sleep quality and disruption of regular sleep patterns ( Wang et al, 2021 ). Consequently, there is a growing interest in developing portable, wireless, and wearable devices equipped with low-cost sensors attached to a single location, such as the Fpz channel in the frontal region of the brain, to acquire single-channel EEG signals, facilitating comfortable and convenient sleep monitoring ( Gao et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Automatic sleep staging by a hybrid model based on deep 1D-ResNet-SE and LSTM with single-channel raw EEG signals

Li,

Gao

2023

PeerJ Computer Science

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Sleep staging is crucial for assessing sleep quality and diagnosing sleep disorders. Recent advances in deep learning methods with electroencephalogram (EEG) signals have shown remarkable success in automatic sleep staging. However, the use of deeper neural networks may lead to the issues of gradient disappearance and explosion, while the non-stationary nature and low signal-to-noise ratio of EEG signals can negatively impact feature representation. To overcome these challenges, we proposed a novel lightweight sequence-to-sequence deep learning model, 1D-ResNet-SE-LSTM, to classify sleep stages into five classes using single-channel raw EEG signals. Our proposed model consists of two main components: a one-dimensional residual convolutional neural network with a squeeze-and-excitation module to extract and reweight features from EEG signals, and a long short-term memory network to capture the transition rules among sleep stages. In addition, we applied the weighted cross-entropy loss function to alleviate the class imbalance problem. We evaluated the performance of our model on two publicly available datasets; Sleep-EDF Expanded consists of 153 overnight PSG recordings collected from 78 healthy subjects and ISRUC-Sleep includes 100 PSG recordings collected from 100 subjects diagnosed with various sleep disorders, and obtained an overall accuracy rate of 86.39% and 81.97%, respectively, along with corresponding macro average F1-scores of 81.95% and 79.94%. Our model outperforms existing sleep staging models in terms of overall performance metrics and per-class F1-scores for several sleep stages, particularly for the N1 stage, where it achieves F1-scores of 59.00% and 55.53%. The kappa coefficient is 0.812 and 0.766 for the Sleep-EDF Expanded and ISRUC-Sleep datasets, respectively, indicating strong agreement with certified sleep experts. We also investigated the effect of different weight coefficient combinations and sequence lengths of EEG epochs used as input to the model on its performance. Furthermore, the ablation study was conducted to evaluate the contribution of each component to the model’s performance. The results demonstrate the effectiveness and robustness of the proposed model in classifying sleep stages, and highlights its potential to reduce human clinicians’ workload, making sleep assessment and diagnosis more effective. However, the proposed model is subject to several limitations. Firstly, the model is a sequence-to-sequence network, which requires input sequences of EEG epochs. Secondly, the weight coefficients in the loss function could be further optimized to balance the classification performance of each sleep stage. Finally, apart from the channel attention mechanism, incorporating more advanced attention mechanisms could enhance the model’s effectiveness.

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Sleep Staging Based on Multi Scale Dual Attention Network

Cited by 2 publications

References 29 publications

Automatic sleep staging by a hybrid model based on deep 1D-ResNet-SE and LSTM with single-channel raw EEG signals

Automatic sleep staging by a hybrid model based on deep 1D-ResNet-SE and LSTM with single-channel raw EEG signals

Automatic sleep staging by a hybrid model based on deep 1D-ResNet-SE and LSTM with single-channel raw EEG signals

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