LWSleepNet: A lightweight attention-based deep learning model for sleep staging with singlechannel EEG
Introduction Sleep is vital to human health, and sleep staging is an essential process in sleep assessment. However, manual classification is an inefficient task. Along with the increased demand for portable sleep quality detection devices, lightweight automatic sleep staging needs to be developed....
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2023-07-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076231188206 |
| _version_ | 1797770838496247808 |
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| author | Chenguang Yang Baozhu Li Yamei Li Yixuan He Yuan Zhang |
| author_facet | Chenguang Yang Baozhu Li Yamei Li Yixuan He Yuan Zhang |
| author_sort | Chenguang Yang |
| collection | DOAJ |
| description | Introduction Sleep is vital to human health, and sleep staging is an essential process in sleep assessment. However, manual classification is an inefficient task. Along with the increased demand for portable sleep quality detection devices, lightweight automatic sleep staging needs to be developed. Methods This study proposes a novel attention-based lightweight deep learning model called LWSleepNet. A depthwise separable multi-resolution convolutional neural network is introduced to analyze the input feature map and captures features at multiple frequencies using two different sized convolutional kernels. The temporal feature extraction module divides the input into patches and feeds them into a multi-head attention block to extract time-dependent information from sleep recordings. The model's convolution operations are replaced with depthwise separable convolutions to minimize its number of parameters and computational cost. The model's performance on two public datasets (Sleep-EDF-20 and Sleep-EDF-78) was evaluated and compared with those of previous studies. Then, an ablation study and sensitivity analysis were performed to evaluate further each module. Results LWSleepNet achieves an accuracy of 86.6% and Macro-F1 score of 79.2% for the Sleep-EDF-20 dataset and an accuracy of 81.5% and Macro-F1 score of 74.3% for the Sleep-EDF-78 dataset with only 55.3 million floating-point operations per second and 180 K parameters. Conclusion On two public datasets, LWSleepNet maintains excellent prediction performance while substantially reducing the number of parameters, demonstrating that our proposed Light multiresolution convolutional neural network and temporal feature extraction modules can provide excellent portability and accuracy and can be easily integrated into portable sleep monitoring devices. |
| first_indexed | 2024-03-12T21:27:52Z |
| format | Article |
| id | doaj.art-a9e1c031d24b4c2b857e93f7c2191c56 |
| institution | Directory Open Access Journal |
| issn | 2055-2076 |
| language | English |
| last_indexed | 2024-03-12T21:27:52Z |
| publishDate | 2023-07-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj.art-a9e1c031d24b4c2b857e93f7c2191c562023-07-28T03:33:40ZengSAGE PublishingDigital Health2055-20762023-07-01910.1177/20552076231188206LWSleepNet: A lightweight attention-based deep learning model for sleep staging with singlechannel EEGChenguang Yang0Baozhu Li1Yamei Li2Yixuan He3Yuan Zhang4 WESTA College, , Chongqing, China Internet of Things and Smart City Innovation Platform, Zhuhai Fudan Innovation Institute, Zhuhai, China College of Electronic and Information Engineering, , Chongqing, China WESTA College, , Chongqing, China College of Electronic and Information Engineering, , Chongqing, ChinaIntroduction Sleep is vital to human health, and sleep staging is an essential process in sleep assessment. However, manual classification is an inefficient task. Along with the increased demand for portable sleep quality detection devices, lightweight automatic sleep staging needs to be developed. Methods This study proposes a novel attention-based lightweight deep learning model called LWSleepNet. A depthwise separable multi-resolution convolutional neural network is introduced to analyze the input feature map and captures features at multiple frequencies using two different sized convolutional kernels. The temporal feature extraction module divides the input into patches and feeds them into a multi-head attention block to extract time-dependent information from sleep recordings. The model's convolution operations are replaced with depthwise separable convolutions to minimize its number of parameters and computational cost. The model's performance on two public datasets (Sleep-EDF-20 and Sleep-EDF-78) was evaluated and compared with those of previous studies. Then, an ablation study and sensitivity analysis were performed to evaluate further each module. Results LWSleepNet achieves an accuracy of 86.6% and Macro-F1 score of 79.2% for the Sleep-EDF-20 dataset and an accuracy of 81.5% and Macro-F1 score of 74.3% for the Sleep-EDF-78 dataset with only 55.3 million floating-point operations per second and 180 K parameters. Conclusion On two public datasets, LWSleepNet maintains excellent prediction performance while substantially reducing the number of parameters, demonstrating that our proposed Light multiresolution convolutional neural network and temporal feature extraction modules can provide excellent portability and accuracy and can be easily integrated into portable sleep monitoring devices.https://doi.org/10.1177/20552076231188206 |
| spellingShingle | Chenguang Yang Baozhu Li Yamei Li Yixuan He Yuan Zhang LWSleepNet: A lightweight attention-based deep learning model for sleep staging with singlechannel EEG Digital Health |
| title | LWSleepNet: A lightweight attention-based deep learning model for sleep staging with singlechannel EEG |
| title_full | LWSleepNet: A lightweight attention-based deep learning model for sleep staging with singlechannel EEG |
| title_fullStr | LWSleepNet: A lightweight attention-based deep learning model for sleep staging with singlechannel EEG |
| title_full_unstemmed | LWSleepNet: A lightweight attention-based deep learning model for sleep staging with singlechannel EEG |
| title_short | LWSleepNet: A lightweight attention-based deep learning model for sleep staging with singlechannel EEG |
| title_sort | lwsleepnet a lightweight attention based deep learning model for sleep staging with singlechannel eeg |
| url | https://doi.org/10.1177/20552076231188206 |
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