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Mostafaei SH, Tanha J, Sharafkhaneh A. A novel deep learning model based on transformer and cross modality attention for classification of sleep stages. J Biomed Inform 2024; 157:104689. [PMID: 39029770 DOI: 10.1016/j.jbi.2024.104689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 06/13/2024] [Accepted: 07/10/2024] [Indexed: 07/21/2024]
Abstract
The classification of sleep stages is crucial for gaining insights into an individual's sleep patterns and identifying potential health issues. Employing several important physiological channels in different views, each providing a distinct perspective on sleep patterns, can have a great impact on the efficiency of the classification models. In the context of neural networks and deep learning models, transformers are very effective, especially when dealing with time series data, and have shown remarkable compatibility with sequential data analysis as physiological channels. On the other hand, cross-modality attention by integrating information from multiple views of the data enables to capture relationships among different modalities, allowing models to selectively focus on relevant information from each modality. In this paper, we introduce a novel deep-learning model based on transformer encoder-decoder and cross-modal attention for sleep stage classification. The proposed model processes information from various physiological channels with different modalities using the Sleep Heart Health Study Dataset (SHHS) data and leverages transformer encoders for feature extraction and cross-modal attention for effective integration to feed into the transformer decoder. The combination of these elements increased the accuracy of the model up to 91.33% in classifying five classes of sleep stages. Empirical evaluations demonstrated the model's superior performance compared to standalone approaches and other state-of-the-art techniques, showcasing the potential of combining transformer and cross-modal attention for improved sleep stage classification.
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Affiliation(s)
| | - Jafar Tanha
- Faculty of Electrical and Computer Engineering, University of Tabriz, P.O. Box 51666-16471, Tabriz, Iran.
| | - Amir Sharafkhaneh
- Professor of Medicine, Section of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
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2
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Liu Z, Zhang Q, Luo S, Qin M. FPJA-Net: A Lightweight End-to-End Network for Sleep Stage Prediction Based on Feature Pyramid and Joint Attention. Interdiscip Sci 2024:10.1007/s12539-024-00636-9. [PMID: 39155326 DOI: 10.1007/s12539-024-00636-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 05/07/2024] [Accepted: 05/13/2024] [Indexed: 08/20/2024]
Abstract
Sleep staging is the most crucial work before diagnosing and treating sleep disorders. Traditional manual sleep staging is time-consuming and depends on the skill of experts. Nowadays, automatic sleep staging based on deep learning attracts more and more scientific researchers. As we know, the salient waves in sleep signals contain the most important information for automatic sleep staging. However, the key information is not fully utilized in existing deep learning methods since most of them only use CNN or RNN which could not capture multi-scale features in salient waves effectively. To tackle this limitation, we propose a lightweight end-to-end network for sleep stage prediction based on feature pyramid and joint attention. The feature pyramid module is designed to effectively extract multi-scale features in salient waves, and these features are then fed to the joint attention module to closely attend to the channel and location information of the salient waves. The proposed network has much fewer parameters and significant performance improvement, which is better than the state-of-the-art results. The overall accuracy and macro F1 score on the public dataset Sleep-EDF39, Sleep-EDF153 and SHHS are 90.1%, 87.8%, 87.4%, 84.4% and 86.9%, 83.9%, respectively. Ablation experiments confirm the effectiveness of each module.
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Affiliation(s)
- Zhi Liu
- School of Artificial Intelligence, Chongqing University of Technology, Chongqing, 401135, China.
| | - Qinhan Zhang
- School of Artificial Intelligence, Chongqing University of Technology, Chongqing, 401135, China
| | - Sixin Luo
- School of Artificial Intelligence, Chongqing University of Technology, Chongqing, 401135, China
| | - Meiqiao Qin
- School of Artificial Intelligence, Chongqing University of Technology, Chongqing, 401135, China
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3
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Satapathy SK, Brahma B, Panda B, Barsocchi P, Bhoi AK. Machine learning-empowered sleep staging classification using multi-modality signals. BMC Med Inform Decis Mak 2024; 24:119. [PMID: 38711099 DOI: 10.1186/s12911-024-02522-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 04/29/2024] [Indexed: 05/08/2024] Open
Abstract
The goal is to enhance an automated sleep staging system's performance by leveraging the diverse signals captured through multi-modal polysomnography recordings. Three modalities of PSG signals, namely electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG), were considered to obtain the optimal fusions of the PSG signals, where 63 features were extracted. These include frequency-based, time-based, statistical-based, entropy-based, and non-linear-based features. We adopted the ReliefF (ReF) feature selection algorithms to find the suitable parts for each signal and superposition of PSG signals. Twelve top features were selected while correlated with the extracted feature sets' sleep stages. The selected features were fed into the AdaBoost with Random Forest (ADB + RF) classifier to validate the chosen segments and classify the sleep stages. This study's experiments were investigated by obtaining two testing schemes: epoch-wise testing and subject-wise testing. The suggested research was conducted using three publicly available datasets: ISRUC-Sleep subgroup1 (ISRUC-SG1), sleep-EDF(S-EDF), Physio bank CAP sleep database (PB-CAPSDB), and S-EDF-78 respectively. This work demonstrated that the proposed fusion strategy overestimates the common individual usage of PSG signals.
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Affiliation(s)
- Santosh Kumar Satapathy
- Department of Information and Communication Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, 382007, India.
| | - Biswajit Brahma
- McKesson Corporation, 1 Post St, San Francisco, CA, 94104, USA
| | - Baidyanath Panda
- LTIMindtree, 1 American Row, 3Rd Floor, Hartford, CT, 06103, USA
| | - Paolo Barsocchi
- Institute of Information Science and Technologies, National Research Council, 56124, Pisa, Italy.
| | - Akash Kumar Bhoi
- Directorate of Research, Sikkim Manipal University, Gangtok, 737102, Sikkim, India.
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4
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Chen Z, Pan X, Xu Z, Li K, Lv Y, Zhang Y, Sun H. A Semi-Supervised Multi-Scale Arbitrary Dilated Convolution Neural Network for Pediatric Sleep Staging. IEEE J Biomed Health Inform 2024; 28:1043-1053. [PMID: 37930925 DOI: 10.1109/jbhi.2023.3330345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2023]
Abstract
Sleep staging is essential for assessing sleep quality and diagnosing sleep disorders. However, sleep staging is a labor-intensive process, making it arduous to obtain large quantities of high-quality labeled data for automatic sleep staging. Meanwhile, most of the research on automatic sleep staging pays little attention to pediatric sleep staging. To address these challenges, we propose a semi-supervised multi-scale arbitrary dilated convolution neural network (SMADNet) for pediatric sleep staging using the scalogram with a high height-to-width ratio generated by the continuous wavelet transform (CWT) as input. To extract more extended time dimensional feature representations and adapt to scalograms with a high height-to-width ratio in SMADNet, we introduce a multi-scale arbitrary dilation convolution block (MADBlock) based on our proposed arbitrary dilated convolution (ADConv). Finally, we also utilize semi-supervised learning as the training scheme for our network in order to alleviate the reliance on labeled data. Our proposed model has achieved performance comparable to state-of-the-art supervised learning methods with 30% labels. Our model is tested on a private pediatric dataset and achieved 79% accuracy, 72% kappa, and 75% MF1. Therefore, our model demonstrates a powerful feature extraction capability and has achieved performance comparable to state-of-the-art supervised learning methods with a small number of labels.
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5
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M E, Hans WJ, T M I, Lindsay NM. Multi-scale EMG classification with spatial-temporal attention for prosthetic hands. Comput Methods Biomech Biomed Engin 2023:1-16. [PMID: 38037332 DOI: 10.1080/10255842.2023.2287419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 11/20/2023] [Indexed: 12/02/2023]
Abstract
A classification framework for hand gestures using Electromyography (EMG) signals in prosthetic hands is presented. Leveraging the multi-scale characteristics and temporal nature of EMG signals, a Convolutional Neural Network (CNN) is used to extract multi-scale features and classify them with spatial-temporal attention. A multi-scale coarse-grained layer introduced into the input of one-dimensional CNN (1D-CNN) facilitates multi-scale feature extraction. The multi-scale features are fed into the attention layer and subsequently given to the fully connected layer to perform classification. The proposed model achieves classification accuracies of 93.4%, 92.8%, 91.3%, and 94.1% for Ninapro DB1, DB2, DB5, and DB7 respectively, thereby enhancing the confidence of prosthetic hand users.
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Affiliation(s)
- Emimal M
- Department of Electronics and Communication Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai, India
| | - W Jino Hans
- Department of Electronics and Communication Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai, India
| | - Inbamalar T M
- Department of Electronics and Communication Engineering, RMK College of Engineering and Technology, Puduvoyal, Chennai, India
| | - N Mahiban Lindsay
- Department of Electrical and Electronics Engineering, Hindustan Institute of Technology and Science, Padur, Chennai, India
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6
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Liu Z, Qin M, Lu Y, Luo S, Zhang Q. DenSleepNet: DenseNet based model for sleep staging with two-frequency feature fusion and coordinate attention. Biomed Eng Lett 2023; 13:751-761. [PMID: 37872995 PMCID: PMC10590351 DOI: 10.1007/s13534-023-00301-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 06/15/2023] [Accepted: 06/23/2023] [Indexed: 10/25/2023] Open
Abstract
Sleep staging is often applied to assess the quality of sleep and also be used to prevent and monitor psychiatric disorders caused by sleep. However, it remains a challenge to extract the discriminative features of salient waveforms in sleep EEG and enable the network to effectively classify sleep stages by emphasizing these crucial features, thus achieving higher accuracy. In this study, an end-to-end deep learning model based on DenseNet for automatic sleep staging is designed and constructed. In the framework, two convolutional branches are devised to extract the underlying features (Two-Frequency Feature) at various frequencies, which are then fused and input into the DenseNet module to extract salient waveform features. After that, the Coordinate Attention mechanism is employed to enhance the localization of salient waveform features by emphasizing the position of salient waveforms and the spatial relationship across the entire frequency spectrum. Finally, the obtained features are accessed to the fully connected for sleep staging. The model was validated with a 20-fold cross-validation procedure on two public available datasets, and the overall accuracy, kappa coefficient, and MF1 score reached 92.9%, 78.7, 0.86 and 90.0%, 75.8, 0.80 on Sleep-EDF-20 and Sleep-EDFx, respectively. Experimental results show that the proposed model achieves competitive performance for sleep staging compared with the reported approaches under the identical conditions.
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Affiliation(s)
- Zhi Liu
- School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China
| | - Meiqiao Qin
- School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China
| | - Yunhua Lu
- School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China
| | - Sixin Luo
- School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China
| | - Qinhan Zhang
- School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China
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7
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Jin Z, Jia K. A temporal multi-scale hybrid attention network for sleep stage classification. Med Biol Eng Comput 2023; 61:2291-2303. [PMID: 36997808 DOI: 10.1007/s11517-023-02808-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 02/13/2023] [Indexed: 04/01/2023]
Abstract
Sleep is crucial for human health. Automatic sleep stage classification based on polysomnogram (PSG) is meaningful for the diagnosis of sleep disorders, which has attracted extensive attention in recent years. Most existing methods could not fully consider the different transitions of sleep stages and fit the visual inspection of sleep experts simultaneously. To this end, we propose a temporal multi-scale hybrid attention network, namely TMHAN, to automatically achieve sleep staging. The temporal multi-scale mechanism incorporates short-term abrupt and long-term periodic transitions of the successive PSG epochs. Furthermore, the hybrid attention mechanism includes 1-D local attention, 2-D global attention, and 2-D contextual sparse multi-head self-attention for three kinds of sequence-level representations. The concatenated representation is subsequently fed into a softmax layer to train an end-to-end model. Experimental results on two benchmark sleep datasets show that TMHAN obtains the best performance compared with several baselines, demonstrating the effectiveness of our model. In general, our work not only provides good classification performance, but also fits the actual sleep staging processes, which makes contribution for the combination of deep learning and sleep medicine.
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Affiliation(s)
- Zheng Jin
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
- Beijing Laboratory of Advanced Information Networks, Beijing, 100124, China
| | - Kebin Jia
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.
- Beijing Laboratory of Advanced Information Networks, Beijing, 100124, China.
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8
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Gaiduk M, Serrano Alarcón Á, Seepold R, Martínez Madrid N. Current status and prospects of automatic sleep stages scoring: Review. Biomed Eng Lett 2023; 13:247-272. [PMID: 37519865 PMCID: PMC10382458 DOI: 10.1007/s13534-023-00299-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 06/07/2023] [Accepted: 06/18/2023] [Indexed: 08/01/2023] Open
Abstract
The scoring of sleep stages is one of the essential tasks in sleep analysis. Since a manual procedure requires considerable human and financial resources, and incorporates some subjectivity, an automated approach could result in several advantages. There have been many developments in this area, and in order to provide a comprehensive overview, it is essential to review relevant recent works and summarise the characteristics of the approaches, which is the main aim of this article. To achieve it, we examined articles published between 2018 and 2022 that dealt with the automated scoring of sleep stages. In the final selection for in-depth analysis, 125 articles were included after reviewing a total of 515 publications. The results revealed that automatic scoring demonstrates good quality (with Cohen's kappa up to over 0.80 and accuracy up to over 90%) in analysing EEG/EEG + EOG + EMG signals. At the same time, it should be noted that there has been no breakthrough in the quality of results using these signals in recent years. Systems involving other signals that could potentially be acquired more conveniently for the user (e.g. respiratory, cardiac or movement signals) remain more challenging in the implementation with a high level of reliability but have considerable innovation capability. In general, automatic sleep stage scoring has excellent potential to assist medical professionals while providing an objective assessment.
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Affiliation(s)
- Maksym Gaiduk
- HTWG Konstanz – University of Applied Sciences, Alfred-Wachtel-Str.8, 78462 Konstanz, Germany
| | | | - Ralf Seepold
- HTWG Konstanz – University of Applied Sciences, Alfred-Wachtel-Str.8, 78462 Konstanz, Germany
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9
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Habashi AG, Azab AM, Eldawlatly S, Aly GM. Generative adversarial networks in EEG analysis: an overview. J Neuroeng Rehabil 2023; 20:40. [PMID: 37038142 PMCID: PMC10088201 DOI: 10.1186/s12984-023-01169-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 03/30/2023] [Indexed: 04/12/2023] Open
Abstract
Electroencephalogram (EEG) signals have been utilized in a variety of medical as well as engineering applications. However, one of the challenges associated with recording EEG data is the difficulty of recording large amounts of data. Consequently, data augmentation is a potential solution to overcome this challenge in which the objective is to increase the amount of data. Inspired by the success of Generative Adversarial Networks (GANs) in image processing applications, generating artificial EEG data from the limited recorded data using GANs has seen recent success. This article provides an overview of various techniques and approaches of GANs for augmenting EEG signals. We focus on the utility of GANs in different applications including Brain-Computer Interface (BCI) paradigms such as motor imagery and P300-based systems, in addition to emotion recognition, epileptic seizures detection and prediction, and various other applications. We address in this article how GANs have been used in each study, the impact of using GANs on the model performance, the limitations of each algorithm, and future possibilities for developing new algorithms. We emphasize the utility of GANs in augmenting the limited EEG data typically available in the studied applications.
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Affiliation(s)
- Ahmed G Habashi
- Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, 1 El-Sarayat St., Abbassia, Cairo, Egypt
| | - Ahmed M Azab
- Biomedical Engineering Department, Technical Research Center, Cairo, Egypt
| | - Seif Eldawlatly
- Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, 1 El-Sarayat St., Abbassia, Cairo, Egypt.
- Computer Science and Engineering Department, The American University in Cairo, Cairo, Egypt.
| | - Gamal M Aly
- Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, 1 El-Sarayat St., Abbassia, Cairo, Egypt
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10
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Huang X, Shirahama K, Irshad MT, Nisar MA, Piet A, Grzegorzek M. Sleep Stage Classification in Children Using Self-Attention and Gaussian Noise Data Augmentation. SENSORS (BASEL, SWITZERLAND) 2023; 23:3446. [PMID: 37050506 PMCID: PMC10098613 DOI: 10.3390/s23073446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 03/20/2023] [Accepted: 03/22/2023] [Indexed: 06/19/2023]
Abstract
The analysis of sleep stages for children plays an important role in early diagnosis and treatment. This paper introduces our sleep stage classification method addressing the following two challenges: the first is the data imbalance problem, i.e., the highly skewed class distribution with underrepresented minority classes. For this, a Gaussian Noise Data Augmentation (GNDA) algorithm was applied to polysomnography recordings to seek the balance of data sizes for different sleep stages. The second challenge is the difficulty in identifying a minority class of sleep stages, given their short sleep duration and similarities to other stages in terms of EEG characteristics. To overcome this, we developed a DeConvolution- and Self-Attention-based Model (DCSAM) which can inverse the feature map of a hidden layer to the input space to extract local features and extract the correlations between all possible pairs of features to distinguish sleep stages. The results on our dataset show that DCSAM based on GNDA obtains an accuracy of 90.26% and a macro F1-score of 86.51% which are higher than those of our previous method. We also tested DCSAM on a well-known public dataset-Sleep-EDFX-to prove whether it is applicable to sleep data from adults. It achieves a comparable performance to state-of-the-art methods, especially accuracies of 91.77%, 92.54%, 94.73%, and 95.30% for six-stage, five-stage, four-stage, and three-stage classification, respectively. These results imply that our DCSAM based on GNDA has a great potential to offer performance improvements in various medical domains by considering the data imbalance problems and correlations among features in time series data.
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Affiliation(s)
- Xinyu Huang
- Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Kimiaki Shirahama
- Department of Informatics, Kindai University, 3-4-1 Kowakae, Higashiosaka City 577-8502, Osaka, Japan
| | - Muhammad Tausif Irshad
- Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
- Department of IT, University of the Punjab, Lahore 54000, Pakistan
| | | | - Artur Piet
- Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Marcin Grzegorzek
- Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
- Department of Knowledge Engineering, University of Economics, Bogucicka 3, 40287 Katowice, Poland
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Efe E, Ozsen S. CoSleepNet: Automated sleep staging using a hybrid CNN-LSTM network on imbalanced EEG-EOG datasets. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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12
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Phyo J, Ko W, Jeon E, Suk HI. TransSleep: Transitioning-Aware Attention-Based Deep Neural Network for Sleep Staging. IEEE TRANSACTIONS ON CYBERNETICS 2022; PP:4500-4510. [PMID: 36063512 DOI: 10.1109/tcyb.2022.3198997] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Sleep staging is essential for sleep assessment and plays a vital role as a health indicator. Many recent studies have devised various machine/deep learning methods for sleep staging. However, two key challenges hinder the practical use of those methods: 1) effectively capturing salient waveforms in sleep signals and 2) correctly classifying confusing stages in transitioning epochs. In this study, we propose a novel deep neural-network structure, TransSleep, that captures distinctive local temporal patterns and distinguishes confusing stages using two auxiliary tasks. In particular, TransSleep captures salient waveforms in sleep signals by an attention-based multiscale feature extractor and correctly classifies confusing stages in transitioning epochs, while modeling contextual relationships with two auxiliary tasks. Results show that TransSleep achieves promising performance in automatic sleep staging. The validity of TransSleep is demonstrated by its state-of-the-art performance on two publicly available datasets: 1) Sleep-EDF and 2) MASS. Furthermore, we performed ablations to analyze our results from different perspectives. Based on our overall results, we believe that TransSleep has immense potential to provide new insights into deep-learning-based sleep staging.
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Li C, Qi Y, Ding X, Zhao J, Sang T, Lee M. A Deep Learning Method Approach for Sleep Stage Classification with EEG Spectrogram. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:6322. [PMID: 35627856 PMCID: PMC9141573 DOI: 10.3390/ijerph19106322] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 05/11/2022] [Accepted: 05/18/2022] [Indexed: 11/17/2022]
Abstract
The classification of sleep stages is an important process. However, this process is time-consuming, subjective, and error-prone. Many automated classification methods use electroencephalogram (EEG) signals for classification. These methods do not classify well enough and perform poorly in the N1 due to unbalanced data. In this paper, we propose a sleep stage classification method using EEG spectrogram. We have designed a deep learning model called EEGSNet based on multi-layer convolutional neural networks (CNNs) to extract time and frequency features from the EEG spectrogram, and two-layer bi-directional long short-term memory networks (Bi-LSTMs) to learn the transition rules between features from adjacent epochs and to perform the classification of sleep stages. In addition, to improve the generalization ability of the model, we have used Gaussian error linear units (GELUs) as the activation function of CNN. The proposed method was evaluated by four public databases, the Sleep-EDFX-8, Sleep-EDFX-20, Sleep-EDFX-78, and SHHS. The accuracy of the method is 94.17%, 86.82%, 83.02% and 85.12%, respectively, for the four datasets, the MF1 is 87.78%, 81.57%, 77.26% and 78.54%, respectively, and the Kappa is 0.91, 0.82, 0.77 and 0.79, respectively. In addition, our proposed method achieved better classification results on N1, with an F1-score of 70.16%, 52.41%, 50.03% and 47.26% for the four datasets.
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Affiliation(s)
- Chengfan Li
- School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China; (C.L.); (Y.Q.); (J.Z.); (T.S.)
| | - Yueyu Qi
- School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China; (C.L.); (Y.Q.); (J.Z.); (T.S.)
| | - Xuehai Ding
- School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China; (C.L.); (Y.Q.); (J.Z.); (T.S.)
| | - Junjuan Zhao
- School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China; (C.L.); (Y.Q.); (J.Z.); (T.S.)
| | - Tian Sang
- School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China; (C.L.); (Y.Q.); (J.Z.); (T.S.)
| | - Matthew Lee
- 12th Grade, The Bishop’s School, La Jolla, CA 92037, USA;
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