CCRRSleepNet: A Hybrid Relational Inductive Biases Network for Automatic Sleep Stage Classification on Raw Single-Channel EEG

Neng, Wenpeng; Lu, Jun; Xu, Lan

doi:10.3390/brainsci11040456

Cited by 34 publications

(7 citation statements)

References 20 publications

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“…The spectrum of the CNN architectures varies from a very basic one [18,60] to specialized ones, such as ResNet [21], U-Net [61,62], and U 2 -Net [63]. Epoch-wise features can also be learned by capturing sequential information within 30-second signals using RNNs solely (e.g., LSTM [52] and GRU [53]) [19,22,64] or hybrid networks (e.g., CRNN [60,65]). Emerging network architectures like graph convolutional network (GCN) [66] and Transformer [24] have also been shown to be useful for epoch encoding.…”

Section: The State-of-the-artmentioning

confidence: 99%

Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions

Phan,

Mikkelsen

2021

Preprint

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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: The State-of-the-artmentioning

confidence: 99%

Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions

Phan,

Mikkelsen

2021

Preprint

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“…H. Dong et al applied multi-layer perception (MLP) and LSTM to address the temporal pattern recognition challenge [14]. A few approaches proposed to combine CNN and RNN in order to extract both temporal and spatial information in the biomedical data [29]- [31]. A. Supratak et al proposed an architecture named DeepSleepNet which was the combination of the CNN and RNN and the five-class sleep staging results can reach 86.2% [10].…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Segmented Attention Network for Sleep Staging by Fusing Local Characteristics and Adjacent Information

Zhou

Zhu

Shen

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Sleep staging is the essential step in sleep quality assessment and sleep disorders diagnosis. However, most current automatic sleep staging approaches use recurrent neural networks (RNN), resulting in a relatively large training burden. Moreover, these methods only extract information of the whole epoch or adjacent epochs, ignoring the local signal variations within epoch. To address these issues, a novel deep learning architecture named segmented attention network (SAN) is proposed in this paper. The architecture can be divided into feature extraction (FE) and time sequence encoder (TSE). The FE module consists of multiple multiscale CNN (MMCNN) and residual squeeze and excitation block (SE block). The former extracts features from multiple equallength EEG segments and the latter reinforced the features. The TSE module based on a multi-head attention mechanism could capture the temporal information in the features extracted by FE module. Noteworthy, in SAN, we replaced the RNN module with a TSE module for temporal learning and made the network faster. The evaluation of the model was performed on two widely used public datasets, Montreal Archive of Sleep Studies (MASS) and Sleep-EDFX, and one clinical dataset from Huashan Hospital of Fudan University, Shanghai, China (HSFU). The proposed model achieved the accuracy of 85.5%, 86.4%, 82.5% on Sleep-EDFX, MASS and HSFU, respectively. The experimental results exhibited favorable performance and consistent improvements of SAN on different datasets in comparison with the state-of-the-art studies. It also proved the necessity of sleep staging by integrating the local characteristics within epochs and adjacent informative features among epochs.

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“…Efficient and feasible sleep assessment is made mandatory for analyzing nap related issues and making timely interference. Assessment of sleep commonly depends on the manual phasing of overnight polysomnography (PSG) signal, including electrocardiogram (ECG), electroencephalogram (EEG), blood oxygen saturation electrooculogram (EOG), electromyogram (EMG), and respiration [3], by well trained and authorized technicians. The more time-taking characteristics of manual doze phasing hamper the application zones on very huge datasets and restrict related research in this domain [4].…”

Section: Introductionmentioning

confidence: 99%

Competitive Multi-Verse Optimization with Deep Learning Based Sleep Stage Classification

Hilal¹,

Al-Rasheed²,

Alzahrani³

et al. 2023

Computer Systems Science and Engineering

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Sleep plays a vital role in optimum working of the brain and the body. Numerous people suffer from sleep-oriented illnesses like apnea, insomnia, etc. Sleep stage classification is a primary process in the quantitative examination of polysomnographic recording. Sleep stage scoring is mainly based on experts' knowledge which is laborious and time consuming. Hence, it can be essential to design automated sleep stage classification model using machine learning (ML) and deep learning (DL) approaches. In this view, this study focuses on the design of Competitive Multi-verse Optimization with Deep Learning Based Sleep Stage Classification (CMVODL-SSC) model using Electroencephalogram (EEG) signals. The proposed CMVODL-SSC model intends to effectively categorize different sleep stages on EEG signals. Primarily, data pre-processing is performed to convert the actual data into useful format. Besides, a cascaded long short term memory (CLSTM) model is employed to perform classification process. At last, the CMVO algorithm is utilized for optimally tuning the hyperparameters involved in the CLSTM model. In order to report the enhancements of the CMVODL-SSC model, a wide range of simulations was carried out and the results ensured the better performance of the CMVODL-SSC model with average accuracy of 96.90%.

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CCRRSleepNet: A Hybrid Relational Inductive Biases Network for Automatic Sleep Stage Classification on Raw Single-Channel EEG

Cited by 34 publications

References 20 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

A Lightweight Segmented Attention Network for Sleep Staging by Fusing Local Characteristics and Adjacent Information

Competitive Multi-Verse Optimization with Deep Learning Based Sleep Stage Classification

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