Intelligent automatic sleep staging model based on CNN and LSTM
Since electroencephalogram (EEG) is a significant basis to treat and diagnose somnipathy, sleep electroencephalogram automatic staging methods play important role in the treatment and diagnosis of sleep disorders. Due to the characteristics of weak signals, EEG needs accurate and efficient algorithm...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2022.946833/full |
| _version_ | 1818493014957359104 |
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| author | Lan Zhuang Minhui Dai Minhui Dai Yi Zhou Lingyu Sun Lingyu Sun |
| author_facet | Lan Zhuang Minhui Dai Minhui Dai Yi Zhou Lingyu Sun Lingyu Sun |
| author_sort | Lan Zhuang |
| collection | DOAJ |
| description | Since electroencephalogram (EEG) is a significant basis to treat and diagnose somnipathy, sleep electroencephalogram automatic staging methods play important role in the treatment and diagnosis of sleep disorders. Due to the characteristics of weak signals, EEG needs accurate and efficient algorithms to extract feature information before applying it in the sleep stages. Conventional feature extraction methods have low efficiency and are difficult to meet the time validity of fast staging. In addition, it can easily lead to the omission of key features owing to insufficient a priori knowledge. Deep learning networks, such as convolutional neural networks (CNNs), have powerful processing capabilities in data analysis and data mining. In this study, a deep learning network is introduced into the study of the sleep stage. In this study, the feature fusion method is presented, and long-term and short-term memory (LSTM) is selected as the classification network to improve the accuracy of sleep stage recognition. First, based on EEG and deep learning network, an automatic sleep phase method based on a multi-channel EGG is proposed. Second, CNN-LSTM is used to monitor EEG and EOG samples during sleep. In addition, without any signal preprocessing or feature extraction, data expansion (DA) can be realized for unbalanced data, and special data and non-general data can be deleted. Finally, the MIT-BIH dataset is used to train and evaluate the proposed model. The experimental results show that the EEG-based sleep phase method proposed in this paper provides an effective method for the diagnosis and treatment of sleep disorders, and hence has a practical application value. |
| first_indexed | 2024-12-10T17:49:43Z |
| format | Article |
| id | doaj.art-974d671d8e1a443ab874cb5e79098247 |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-12-10T17:49:43Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-974d671d8e1a443ab874cb5e790982472022-12-22T01:39:07ZengFrontiers Media S.A.Frontiers in Public Health2296-25652022-07-011010.3389/fpubh.2022.946833946833Intelligent automatic sleep staging model based on CNN and LSTMLan Zhuang0Minhui Dai1Minhui Dai2Yi Zhou3Lingyu Sun4Lingyu Sun5Staff Hospital, Central South University, Changsha, ChinaTeaching and Research Section of Clinical Nursing, Xiangya Hospital of Central South University, Changsha, ChinaDepartment of Ophthalmology, Xiangya Hospital, Central South University, Changsha, ChinaDepartment of Ophthalmology, Xiangya Hospital, Central South University, Changsha, ChinaTeaching and Research Section of Clinical Nursing, Xiangya Hospital of Central South University, Changsha, ChinaDepartment of Ophthalmology, Xiangya Hospital, Central South University, Changsha, ChinaSince electroencephalogram (EEG) is a significant basis to treat and diagnose somnipathy, sleep electroencephalogram automatic staging methods play important role in the treatment and diagnosis of sleep disorders. Due to the characteristics of weak signals, EEG needs accurate and efficient algorithms to extract feature information before applying it in the sleep stages. Conventional feature extraction methods have low efficiency and are difficult to meet the time validity of fast staging. In addition, it can easily lead to the omission of key features owing to insufficient a priori knowledge. Deep learning networks, such as convolutional neural networks (CNNs), have powerful processing capabilities in data analysis and data mining. In this study, a deep learning network is introduced into the study of the sleep stage. In this study, the feature fusion method is presented, and long-term and short-term memory (LSTM) is selected as the classification network to improve the accuracy of sleep stage recognition. First, based on EEG and deep learning network, an automatic sleep phase method based on a multi-channel EGG is proposed. Second, CNN-LSTM is used to monitor EEG and EOG samples during sleep. In addition, without any signal preprocessing or feature extraction, data expansion (DA) can be realized for unbalanced data, and special data and non-general data can be deleted. Finally, the MIT-BIH dataset is used to train and evaluate the proposed model. The experimental results show that the EEG-based sleep phase method proposed in this paper provides an effective method for the diagnosis and treatment of sleep disorders, and hence has a practical application value.https://www.frontiersin.org/articles/10.3389/fpubh.2022.946833/fullEEG signalconvolutional neural networklong-term and short-term memorysleep stagemultichannelfeature fusion |
| spellingShingle | Lan Zhuang Minhui Dai Minhui Dai Yi Zhou Lingyu Sun Lingyu Sun Intelligent automatic sleep staging model based on CNN and LSTM Frontiers in Public Health EEG signal convolutional neural network long-term and short-term memory sleep stage multichannel feature fusion |
| title | Intelligent automatic sleep staging model based on CNN and LSTM |
| title_full | Intelligent automatic sleep staging model based on CNN and LSTM |
| title_fullStr | Intelligent automatic sleep staging model based on CNN and LSTM |
| title_full_unstemmed | Intelligent automatic sleep staging model based on CNN and LSTM |
| title_short | Intelligent automatic sleep staging model based on CNN and LSTM |
| title_sort | intelligent automatic sleep staging model based on cnn and lstm |
| topic | EEG signal convolutional neural network long-term and short-term memory sleep stage multichannel feature fusion |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2022.946833/full |
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