A novel sleep staging network based on multi-scale dual attention

Dual-modal and multi-scale deep neural networks for sleep staging using EEG and ECG signals

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2021.102455 ◽

2021 ◽

Vol 66 ◽

pp. 102455

Author(s):

Ranqi Zhao ◽

Yi Xia ◽

Qiuyang Wang

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Sleep Staging ◽

Ecg Signals ◽

Multi Scale

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MRNet: a Multi-scale Residual Network for EEG-based Sleep Staging

10.1109/ijcnn52387.2021.9534133 ◽

2021 ◽

Author(s):

Xue Jiang ◽

Jianhui Zhao ◽

Du Bo ◽

An Panfeng ◽

Haowen Guo ◽

...

Keyword(s):

Sleep Staging ◽

Residual Network ◽

Multi Scale

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SalientSleepNet: Multimodal Salient Wave Detection Network for Sleep Staging

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/360 ◽

2021 ◽

Author(s):

Ziyu Jia ◽

Youfang Lin ◽

Jing Wang ◽

Xuehui Wang ◽

Peiyi Xie ◽

...

Keyword(s):

Sleep Stage ◽

Disease Diagnosis ◽

Sleep Stages ◽

Sleep Staging ◽

Neural Network Models ◽

Multimodal Data ◽

Multi Scale ◽

Wave Detection ◽

Scale Transition ◽

Transition Rules

Sleep staging is fundamental for sleep assessment and disease diagnosis. Although previous attempts to classify sleep stages have achieved high classification performance, several challenges remain open: 1) How to effectively extract salient waves in multimodal sleep data; 2) How to capture the multi-scale transition rules among sleep stages; 3) How to adaptively seize the key role of specific modality for sleep staging. To address these challenges, we propose SalientSleepNet, a multimodal salient wave detection network for sleep staging. Specifically, SalientSleepNet is a temporal fully convolutional network based on the $U^2$-Net architecture that is originally proposed for salient object detection in computer vision. It is mainly composed of two independent $U^2$-like streams to extract the salient features from multimodal data, respectively. Meanwhile, the multi-scale extraction module is designed to capture multi-scale transition rules among sleep stages. Besides, the multimodal attention module is proposed to adaptively capture valuable information from multimodal data for the specific sleep stage. Experiments on the two datasets demonstrate that SalientSleepNet outperforms the state-of-the-art baselines. It is worth noting that this model has the least amount of parameters compared with the existing deep neural network models.

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A Multi-scale Residual Convolutional Neural Network for Sleep Staging Based on Single Channel Electroencephalography Signal

10.21203/rs.3.rs-554671/v1 ◽

2021 ◽

Author(s):

QINGHUA ZHONG ◽

Haibo Lei ◽

Qianru Chen ◽

Guofu Zhou

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Sleep Disorder ◽

Eye Movement ◽

Cross Validation ◽

Single Channel ◽

Rapid Eye Movement Sleep ◽

Sleep Staging ◽

Eeg Signals ◽

Multi Scale

Abstract Sleep disorder is a serious public health problem. Non hospital sleep monitoring system for monitoring sleep quality can effectively support the screening of sleep disorder related diseases. A new algorithm of multi-scale residual convolutional neural network (MS-RESCNN) was proposed to discover the feature of electroencephalography (EEG) signals detected with wearable system and staging the sleep stage. EEG signals were analyzed by this algorithm every 30 seconds, and then sleep staging results of wake-up (W), rapid eye movement sleep (REM) and non-rapid eye movement sleep (NREM) were outputed. NREM can also be subdivided into N1, N2 and N3 stages. 5-fold cross validation and independent subject cross validation were performed on the dataset with Kappa cofficients 0.7360 and 0.7001, respectively. The accuracy rates of those methods were 92.06% and 91.13%, respectively. Compared with the other methods, our proposed method can obtain the information of sleep stages from single channel EEG signals without special feature extraction. It has a good performance and can provide support for clinical application based on automatic sleep staging.

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