A lightweight automatic sleep staging method for children using single-channel EEG based on edge artificial intelligence

Zhu, Liqiang; Wang, Changming; Zhou, He; Zhang, Yuan

doi:10.1007/s11280-021-00983-3

Cited by 7 publications

(8 citation statements)

References 33 publications

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“…A stacked 1D CNN- and LSTM-based method has been introduced in [ 13 ] and applied to a small private dataset of 26 children ranging from 2–12 years old. The method employed data from a single EEG channel and trained using the edge AI paradigm.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, there is an increased interest in automatic sleep stage scoring techniques to support experts in their diagnosis. Several approaches have been proposed to automatically classify each epoch to one of the five sleep stages, including shallow classifiers, such as support vector machine [ 7 ], random forest [ 8 ], decision tree [ 9 ], as well as deep learning techniques [ 10 ], such as convolutional neural network (CNN) [ 11 ] and long short-term memory (LSTM) [ 12 , 13 ]. Most of the recent automatic sleep stage scoring methods rely on a signal from a single EEG channel [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…Several approaches have been proposed to automatically classify each epoch to one of the five sleep stages, including shallow classifiers, such as support vector machine [ 7 ], random forest [ 8 ], decision tree [ 9 ], as well as deep learning techniques [ 10 ], such as convolutional neural network (CNN) [ 11 ] and long short-term memory (LSTM) [ 12 , 13 ]. Most of the recent automatic sleep stage scoring methods rely on a signal from a single EEG channel [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. A typical EEG-based sleep stage scoring method consists of pre-processing and feature extraction modules followed by the classifier module.…”

Section: Introductionmentioning

confidence: 99%

“…Children’s sleep is known to have significant variations across different age groups and within the same age group [ 21 ]. However, due to the unavailability of adequate child sleep data, very little work has been carried out on automatically analyzing child sleep [ 13 , 16 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

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Influence of Channel Selection and Subject’s Age on the Performance of the Single Channel EEG-Based Automatic Sleep Staging Algorithms

Nazih

Shahin

Eldesouki

et al. 2023

Sensors

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The electroencephalogram (EEG) signal is a key parameter used to identify the different sleep stages present in an overnight sleep recording. Sleep staging is crucial in the diagnosis of several sleep disorders; however, the manual annotation of the EEG signal is a costly and time-consuming process. Automatic sleep staging algorithms offer a practical and cost-effective alternative to manual sleep staging. However, due to the limited availability of EEG sleep datasets, the reliability of existing sleep staging algorithms is questionable. Furthermore, most reported experimental results have been obtained using adult EEG signals; the effectiveness of these algorithms using pediatric EEGs is unknown. In this paper, we conduct an intensive study of two state-of-the-art single-channel EEG-based sleep staging algorithms, namely DeepSleepNet and AttnSleep, using a recently released large-scale sleep dataset collected from 3984 patients, most of whom are children. The paper studies how the performance of these sleep staging algorithms varies when applied on different EEG channels and across different age groups. Furthermore, all results were analyzed within individual sleep stages to understand how each stage is affected by the choice of EEG channel and the participants’ age. The study concluded that the selection of the channel is crucial for the accuracy of the single-channel EEG-based automatic sleep staging methods. For instance, channels O1-M2 and O2-M1 performed consistently worse than other channels for both algorithms and through all age groups. The study also revealed the challenges in the automatic sleep staging of newborns and infants (1–52 weeks).

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Influence of Channel Selection and Subject’s Age on the Performance of the Single Channel EEG-Based Automatic Sleep Staging Algorithms

Nazih

Shahin

Eldesouki

et al. 2023

Sensors

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“…Photoplethysmography (PPG) signal and heart rate variable (HRV) were used in the study. Zhu et al ( 19 ) adapted edge AI to develop a lightweight automatic sleep staging method for children using single-channel EEG. The trained sleep staging model will be deployed to edge smart devices so that the sleep staging can be implemented on edge devices which will greatly save network resources and improve the performance and privacy of the sleep staging application.…”

Section: Related Workmentioning

confidence: 99%

Intelligent automatic sleep staging model based on CNN and LSTM

Liu

Dai

Zhou

et al. 2022

Front. Public Health

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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.

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