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Li C, Mu Y, Zhu P, Pan Y, Zhang S, Yang L, Xu P, Li F. Sleep stages classification by fusing the time-related synchronization analysis and brain activations. Brain Res Bull 2024; 215:111017. [PMID: 38914295 DOI: 10.1016/j.brainresbull.2024.111017] [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: 04/26/2024] [Revised: 05/28/2024] [Accepted: 06/15/2024] [Indexed: 06/26/2024]
Abstract
Sleep staging plays an important role in the diagnosis and treatment of clinical sleep disorders. The sleep staging standard defines every 30 seconds as a sleep period, which may mean that there exist similar brain activity patterns during the same sleep period. Thus, in this work, we propose a novel time-related synchronization analysis framework named time-related multimodal sleep scoring model (TRMSC) to explore the potential time-related patterns of sleeping. In the proposed TRMSC, the time-related synchronization analysis is first conducted on the single channel electrophysiological signal, i.e., Electroencephalogram (EEG) and Electrooculogram (EOG), to explore the time-related patterns, and the spectral activation features are also extracted by spectrum analysis to obtain the multimodal features. With the extracted multimodal features, the feature fusion and selection strategy is utilized to obtain the optimal feature set and achieve robust sleep staging. To verify the effectiveness of the proposed TRMSC, sleep staging experiments were conducted on the Sleep-EDF dataset, and the experimental results indicate that the proposed TRMSC has achieved better performance than other existing strategies, which proves that the time-related synchronization features can make up for the shortcomings of traditional spectrum-based strategies and achieve a higher classification accuracy. The proposed TRMSC model may be helpful for portable sleep analyzers and provide a new analytical method for clinical sleeping research.
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Affiliation(s)
- Cunbo Li
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yufeng Mu
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Pengcheng Zhu
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yue Pan
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Shuhan Zhang
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Lei Yang
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Peng Xu
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fali Li
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
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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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Moctezuma LA, Suzuki Y, Furuki J, Molinas M, Abe T. GRU-powered sleep stage classification with permutation-based EEG channel selection. Sci Rep 2024; 14:17952. [PMID: 39095608 PMCID: PMC11297028 DOI: 10.1038/s41598-024-68978-4] [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: 09/05/2023] [Accepted: 07/30/2024] [Indexed: 08/04/2024] Open
Abstract
We present a new approach to classifying the sleep stage that incorporates a computationally inexpensive method based on permutations for channel selection and takes advantage of deep learning power, specifically the gated recurrent unit (GRU) model, along with other deep learning methods. By systematically permuting the electroencephalographic (EEG) channels, different combinations of EEG channels are evaluated to identify the most informative subset for the classification of the 5-class sleep stage. For analysis, we used an EEG dataset that was collected at the International Institute for Integrative Sleep Medicine (WPI-IIIS) at the University of Tsukuba in Japan. The results of these explorations provide many new insights such as the (1) drastic decrease in performance when channels are fewer than 3, (2) 3-random channels selected by permutation provide the same or better prediction than the 3 channels recommended by the American Academy of Sleep Medicine (AASM), (3) N1 class suffers the most in prediction accuracy as the channels drop from 128 to 3 random or 3 AASM, and (4) no single channel provides acceptable levels of accuracy in the prediction of 5 classes. The results obtained show the GRU's ability to retain essential temporal information from EEG data, which allows capturing the underlying patterns associated with each sleep stage effectively. Using permutation-based channel selection, we enhance or at least maintain as high model efficiency as when using high-density EEG, incorporating only the most informative EEG channels.
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Affiliation(s)
- Luis Alfredo Moctezuma
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan.
| | - Yoko Suzuki
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Junya Furuki
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Marta Molinas
- Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway
| | - Takashi Abe
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, Japan
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Li Y, Chen J, Ma W, Zhao G, Fan X. MVF-SleepNet: Multi-View Fusion Network for Sleep Stage Classification. IEEE J Biomed Health Inform 2024; 28:2485-2495. [PMID: 36129857 DOI: 10.1109/jbhi.2022.3208314] [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: 11/07/2022]
Abstract
Sleep stage classification is of great importance in human health monitoring and disease diagnosing. Clinically, visual-inspected classifying sleep into different stages is quite time consuming and highly relies on the expertise of sleep specialists. Many automated models for sleep stage classification have been proposed in previous studies but their performances still exist a gap to the real clinical application. In this work, we propose a novel multi-view fusion network named MVF-SleepNet based on multi-modal physiological signals of electroencephalography (EEG), electrocardiography (ECG), electrooculography (EOG), and electromyography (EMG). To capture the relationship representation among multi-modal physiological signals, we construct two views of Time-frequency images (TF images) and Graph-learned graphs (GL graphs). To learn the spectral-temporal representation from sequentially timed TF images, the combination of VGG-16 and GRU networks is utilized. To learn the spatial-temporal representation from sequentially timed GL graphs, the combination of Chebyshev graph convolution and temporal convolution networks is employed. Fusing the spectral-temporal representation and spatial-temporal representation can further boost the performance of sleep stage classification. A large number of experiment results on the publicly available datasets of ISRUC-S1 and ISRUC-S3 show that the MVF-SleepNet achieves overall accuracy of 0.821, F1 score of 0.802 and Kappa of 0.768 on ISRUC-S1 dataset, and accuracy of 0.841, F1 score of 0.828 and Kappa of 0.795 on ISRUC-S3 dataset. The MVF-SleepNet achieves competitive results on both datasets of ISRUC-S1 and ISRUC-S3 for sleep stage classification compared to the state-of-the-art baselines. The source code of MVF-SleepNet is available on Github (https://github.com/YJPai65/MVF-SleepNet).
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Jiang J, Li Z, Li H, Yang J, Ma X, Yan B. Sleep architecture and the incidence of depressive symptoms in middle-aged and older adults: A community-based study. J Affect Disord 2024; 352:222-228. [PMID: 38342319 DOI: 10.1016/j.jad.2024.02.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 01/30/2024] [Accepted: 02/07/2024] [Indexed: 02/13/2024]
Abstract
BACKGROUND Rapid eye movement (REM) sleep and three stages of non-REM (NREM) sleep comprise the full sleep cycle. The changes in sleep have been linked to depression risk. This study aimed to explore the association between sleep architecture and depressive symptoms. METHODS A total of 3247 participants from the Sleep Heart Health Study (SHHS) were included in this cohort study. REM and NREM sleep were monitored by in-home polysomnography at SHHS visit 1. Depressive symptoms was reported as the first occurrence between SHHS visits 1 and 2 (mean follow-up of 5.3 years). Multivariable logistic regression was used to investigate the relationship between sleep stages and depressive symptoms. RESULTS In total, 225 cases of depressive symptoms (6.9 %) were observed between SHHS visits 1 and 2. A significant linear association between NREM Stage 1 and depressive symptoms was found after adjusting for potential covariates. Multivariable logistic regression analysis showed that percentage in NREM Stage 1 was associated with the incidence of depressive symptoms (odds ratio [OR], 1.06; 95 % confidence interval [CI], 1.02-1.10; P = 0.001), as were time in NREM Stage 1 and depressive symptoms (OR, 1.02; 95 % CI, 1.01-1.03; P = 0.001). However, no significant association with depressive symptoms was found for other sleep stage. LIMITATIONS The specific follow-up time for depressive symptoms diagnosis was missing. CONCLUSIONS Increased time or percentage in NREM Stage 1 was associated with a higher risk of developing depressive symptoms. The early change in sleep architecture were important for incidence of depressive symptoms and warrants constant concerns.
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Affiliation(s)
- Jialu Jiang
- Department of Psychiatry, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China; Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Zhenyang Li
- Xi'an Jiaotong University Health Science Center, Xi'an, China; Department of Clinical Research Center, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Huimin Li
- Department of Psychiatry, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jian Yang
- Department of Psychiatry, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China; Department of Clinical Research Center, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China; Shaanxi Belt and Road Joint Laboratory of Precision Medicine in Psychiatry, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xiancang Ma
- Department of Psychiatry, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China; Shaanxi Belt and Road Joint Laboratory of Precision Medicine in Psychiatry, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
| | - Bin Yan
- Department of Psychiatry, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China; Department of Clinical Research Center, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China; Shaanxi Belt and Road Joint Laboratory of Precision Medicine in Psychiatry, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
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An P, Zhao J, Du B, Zhao W, Zhang T, Yuan Z. Amplitude-Time Dual-View Fused EEG Temporal Feature Learning for Automatic Sleep Staging. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:6492-6506. [PMID: 36215384 DOI: 10.1109/tnnls.2022.3210384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Electroencephalogram (EEG) plays an important role in studying brain function and human cognitive performance, and the recognition of EEG signals is vital to develop an automatic sleep staging system. However, due to the complex nonstationary characteristics and the individual difference between subjects, how to obtain the effective signal features of the EEG for practical application is still a challenging task. In this article, we investigate the EEG feature learning problem and propose a novel temporal feature learning method based on amplitude-time dual-view fusion for automatic sleep staging. First, we explore the feature extraction ability of convolutional neural networks for the EEG signal from the perspective of interpretability and construct two new representation signals for the raw EEG from the views of amplitude and time. Then, we extract the amplitude-time signal features that reflect the transformation between different sleep stages from the obtained representation signals by using conventional 1-D CNNs. Furthermore, a hybrid dilation convolution module is used to learn the long-term temporal dependency features of EEG signals, which can overcome the shortcoming that the small-scale convolution kernel can only learn the local signal variation information. Finally, we conduct attention-based feature fusion for the learned dual-view signal features to further improve sleep staging performance. To evaluate the performance of the proposed method, we test 30-s-epoch EEG signal samples for healthy subjects and subjects with mild sleep disorders. The experimental results from the most commonly used datasets show that the proposed method has better sleep staging performance and has the potential for the development and application of an EEG-based automatic sleep staging system.
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Jirakittayakorn N, Wongsawat Y, Mitrirattanakul S. ZleepAnlystNet: a novel deep learning model for automatic sleep stage scoring based on single-channel raw EEG data using separating training. Sci Rep 2024; 14:9859. [PMID: 38684765 PMCID: PMC11058251 DOI: 10.1038/s41598-024-60796-y] [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: 10/31/2023] [Accepted: 04/26/2024] [Indexed: 05/02/2024] Open
Abstract
Numerous models for sleep stage scoring utilizing single-channel raw EEG signal have typically employed CNN and BiLSTM architectures. While these models, incorporating temporal information for sequence classification, demonstrate superior overall performance, they often exhibit low per-class performance for N1-stage, necessitating an adjustment of loss function. However, the efficacy of such adjustment is constrained by the training process. In this study, a pioneering training approach called separating training is introduced, alongside a novel model, to enhance performance. The developed model comprises 15 CNN models with varying loss function weights for feature extraction and 1 BiLSTM for sequence classification. Due to its architecture, this model cannot be trained using an end-to-end approach, necessitating separate training for each component using the Sleep-EDF dataset. Achieving an overall accuracy of 87.02%, MF1 of 82.09%, Kappa of 0.8221, and per-class F1-socres (W 90.34%, N1 54.23%, N2 89.53%, N3 88.96%, and REM 87.40%), our model demonstrates promising performance. Comparison with sleep technicians reveals a Kappa of 0.7015, indicating alignment with reference sleep stags. Additionally, cross-dataset validation and adaptation through training with the SHHS dataset yield an overall accuracy of 84.40%, MF1 of 74.96% and Kappa of 0.7785 when tested with the Sleep-EDF-13 dataset. These findings underscore the generalization potential in model architecture design facilitated by our novel training approach.
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Affiliation(s)
- Nantawachara Jirakittayakorn
- Institute for Innovative Learning, Mahidol University, Nakhon Pathom, Thailand
- Faculty of Dentistry, Mahidol University, Bangkok, Thailand
| | - Yodchanan Wongsawat
- Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, Thailand
| | - Somsak Mitrirattanakul
- Department of Masticatory Science, Faculty of Dentistry, Mahidol University, Bangkok, Thailand.
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Attallah O. ADHD-AID: Aiding Tool for Detecting Children's Attention Deficit Hyperactivity Disorder via EEG-Based Multi-Resolution Analysis and Feature Selection. Biomimetics (Basel) 2024; 9:188. [PMID: 38534873 DOI: 10.3390/biomimetics9030188] [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: 01/31/2024] [Revised: 03/12/2024] [Accepted: 03/13/2024] [Indexed: 03/28/2024] Open
Abstract
The severe effects of attention deficit hyperactivity disorder (ADHD) among adolescents can be prevented by timely identification and prompt therapeutic intervention. Traditional diagnostic techniques are complicated and time-consuming because they are subjective-based assessments. Machine learning (ML) techniques can automate this process and prevent the limitations of manual evaluation. However, most of the ML-based models extract few features from a single domain. Furthermore, most ML-based studies have not examined the most effective electrode placement on the skull, which affects the identification process, while others have not employed feature selection approaches to reduce the feature space dimension and consequently the complexity of the training models. This study presents an ML-based tool for automatically identifying ADHD entitled "ADHD-AID". The present study uses several multi-resolution analysis techniques including variational mode decomposition, discrete wavelet transform, and empirical wavelet decomposition. ADHD-AID extracts thirty features from the time and time-frequency domains to identify ADHD, including nonlinear features, band-power features, entropy-based features, and statistical features. The present study also looks at the best EEG electrode placement for detecting ADHD. Additionally, it looks into the location combinations that have the most significant impact on identification accuracy. Additionally, it uses a variety of feature selection methods to choose those features that have the greatest influence on the diagnosis of ADHD, reducing the classification's complexity and training time. The results show that ADHD-AID has provided scores for accuracy, sensitivity, specificity, F1-score, and Mathew correlation coefficients of 0.991, 0.989, 0.992, 0.989, and 0.982, respectively, in identifying ADHD with 10-fold cross-validation. Also, the area under the curve has reached 0.9958. ADHD-AID's results are significantly higher than those of all earlier studies for the detection of ADHD in adolescents. These notable and trustworthy findings support the use of such an automated tool as a means of assistance for doctors in the prompt identification of ADHD in youngsters.
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Affiliation(s)
- Omneya Attallah
- Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria 21937, Egypt
- Wearables, Biosensing and Biosignal Processing Laboratory, Arab Academy for Science, Technology and Maritime Transport, Alexandria 21937, Egypt
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Radhakrishnan BL, Ezra K, Jebadurai IJ, Selvakumar I, Karthikeyan P. An Autonomous Sleep-Stage Detection Technique in Disruptive Technology Environment. SENSORS (BASEL, SWITZERLAND) 2024; 24:1197. [PMID: 38400354 PMCID: PMC10892786 DOI: 10.3390/s24041197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 02/07/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024]
Abstract
Autonomous sleep tracking at home has become inevitable in today's fast-paced world. A crucial aspect of addressing sleep-related issues involves accurately classifying sleep stages. This paper introduces a novel approach PSO-XGBoost, combining particle swarm optimisation (PSO) with extreme gradient boosting (XGBoost) to enhance the XGBoost model's performance. Our model achieves improved overall accuracy and faster convergence by leveraging PSO to fine-tune hyperparameters. Our proposed model utilises features extracted from EEG signals, spanning time, frequency, and time-frequency domains. We employed the Pz-oz signal dataset from the sleep-EDF expanded repository for experimentation. Our model achieves impressive metrics through stratified-K-fold validation on ten selected subjects: 95.4% accuracy, 95.4% F1-score, 95.4% precision, and 94.3% recall. The experiment results demonstrate the effectiveness of our technique, showcasing an average accuracy of 95%, outperforming traditional machine learning classifications. The findings revealed that the feature-shifting approach supplements the classification outcome by 3 to 4 per cent. Moreover, our findings suggest that prefrontal EEG derivations are ideal options and could open up exciting possibilities for using wearable EEG devices in sleep monitoring. The ease of obtaining EEG signals with dry electrodes on the forehead enhances the feasibility of this application. Furthermore, the proposed method demonstrates computational efficiency and holds significant value for real-time sleep classification applications.
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Affiliation(s)
- Baskaran Lizzie Radhakrishnan
- Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore 641114, India; (B.L.R.); (I.J.J.)
| | - Kirubakaran Ezra
- Department of Computer Science and Engineering, Grace College of Engineering, Coimbatore 628005, India;
| | - Immanuel Johnraja Jebadurai
- Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore 641114, India; (B.L.R.); (I.J.J.)
| | - Immanuel Selvakumar
- Department of Electrical and Electronics Engineering, Karunya Institute of Technology and Sciences, Coimbatore 641114, India;
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Jain R, G RA. Modality-Specific Feature Selection, Data Augmentation and Temporal Context for Improved Performance in Sleep Staging. IEEE J Biomed Health Inform 2024; 28:1031-1042. [PMID: 38051608 DOI: 10.1109/jbhi.2023.3339713] [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: 12/07/2023]
Abstract
This work attempts to design an effective sleep staging system, making the best use of the available signals, strategies, and features in the literature. It must not only perform well on different datasets comprising healthy and clinical populations but also achieve good accuracy in cross-dataset experiments. Toward this end, we propose a model comprising multiple binary classifiers in a hierarchical fashion, where, at each level, one or more of EEG, EOG, and EMG are selected to best differentiate between two sleep stages. The best set of 100 features is chosen out of all the features derived from selected signals. The class imbalance in data is addressed by random undersampling and boosting techniques with decision trees as weak learners. Temporal context and data augmentation are used to improve the performance. We also evaluate the performance of our model by training and testing on different datasets. We compare the results of five approaches: using only EEG, EEG+EOG, EEG+EMG+EOG, EEG+EMG, and selective modality with a specific combination of EEG, EMG, and/or EOG at each level. The best results are obtained by considering features from EEG+EMG+EOG at each hierarchical level. The proposed model achieves average accuracies of 83.1%, 90.0%, 84.4%, 82.1%, 81.5%, 79.9%, and 73.7% on Sleep-EDF, Exp Sleep-EDF, ISRUC-S1, S2 and S3, DRMS-SUB, and DRMS-PAT datasets, respectively. For all the datasets except DRMS-SUB, the proposed method outperforms all the state-of-the-art approaches. Cross-dataset performance exceeds 80% for all datasets except DRMS-PAT; independent of whether the test data is from normal subjects or patients.
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Ji X, Li Y, Wen P, Barua P, Acharya UR. MixSleepNet: A Multi-Type Convolution Combined Sleep Stage Classification Model. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107992. [PMID: 38218118 DOI: 10.1016/j.cmpb.2023.107992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 12/09/2023] [Accepted: 12/19/2023] [Indexed: 01/15/2024]
Abstract
BACKGROUND AND OBJECTIVE Sleep staging is an essential step for sleep disorder diagnosis, which is time-intensive and laborious for experts to perform this work manually. Automatic sleep stage classification methods not only alleviate experts from these demanding tasks but also enhance the accuracy and efficiency of the classification process. METHODS A novel multi-channel biosignal-based model constructed by the combination of a 3D convolutional operation and a graph convolutional operation is proposed for the automated sleep stages using various physiological signals. Both the 3D convolution and graph convolution can aggregate information from neighboring brain areas, which helps to learn intrinsic connections from the biosignals. Electroencephalogram (EEG), electromyogram (EMG), electrooculogram (EOG) and electrocardiogram (ECG) signals are employed to extract time domain and frequency domain features. Subsequently, these signals are input to the 3D convolutional and graph convolutional branches, respectively. The 3D convolution branch can explore the correlations between multi-channel signals and multi-band waves in each channel in the time series, while the graph convolution branch can explore the connections between each channel and each frequency band. In this work, we have developed the proposed multi-channel convolution combined sleep stage classification model (MixSleepNet) using ISRUC datasets (Subgroup 3 and 50 random samples from Subgroup 1). RESULTS Based on the first expert's label, our generated MixSleepNet yielded an accuracy, F1-score and Cohen kappa scores of 0.830, 0.821 and 0.782, respectively for ISRUC-S3. It obtained accuracy, F1-score and Cohen kappa scores of 0.812, 0.786, and 0.756, respectively for the ISRUC-S1 dataset. In accordance with the evaluations conducted by the second expert, the comprehensive accuracies, F1-scores, and Cohen kappa coefficients for the ISRUC-S3 and ISRUC-S1 datasets are determined to be 0.837, 0.820, 0.789, and 0.829, 0.791, 0.775, respectively. CONCLUSION The results of the performance metrics by the proposed method are much better than those from all the compared models. Additional experiments were carried out on the ISRUC-S3 sub-dataset to evaluate the contributions of each module towards the classification performance.
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Affiliation(s)
- Xiaopeng Ji
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
| | - Yan Li
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
| | - Peng Wen
- School of Engineering, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
| | - Prabal Barua
- Cogninet Brain Team, Sydney, NSW 2010, Australia.
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
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12
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Wu Y, Jiang X, Guo Y, Zhu H, Dai C, Chen W. Physiological measurements for driving drowsiness: A comparative study of multi-modality feature fusion and selection. Comput Biol Med 2023; 167:107590. [PMID: 37897962 DOI: 10.1016/j.compbiomed.2023.107590] [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: 08/12/2023] [Revised: 09/18/2023] [Accepted: 10/17/2023] [Indexed: 10/30/2023]
Abstract
A large number of traffic accidents were caused by drowsiness while driving. In-vehicle alert system based on physiological signals was one of the most promising solutions to monitor driving fatigue. However, different physiological modalities can be used, and many relative studies compared different modalities without considering the implementation feasibility of portable or wearable devices. Moreover, evaluations of each modality in previous studies were based on inconsistent choices of fatigue label and signal features, making it hard to compare the results of different studies. Therefore, the modality comparison and fusion for continuous drowsiness estimation while driving was still unclear. This work sought to comprehensively compare widely-used physiological modalities, including forehead electroencephalogram (EEG), electrooculogram (EOG), R-R intervals (RRI) and breath, in a hardware setting feasible for portable or wearable devices to monitor driving fatigue. Moreover, a more general conclusion on modality comparison and fusion was reached based on the regression of features or their combinations and the awake-to-drowsy transition. Finally, the feature subset of fused modalities was produced by feature selection method, to select the optimal feature combination and reduce computation consumption. Considering practical feasibility, the most effective combination with the highest correlation coefficient was using forehead EEG or EOG, along with RRI and RRI-derived breath. If more comfort and convenience was required, the combination of RRI and RRI-derived breath was also promising.
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Affiliation(s)
- Yonglin Wu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China; School of Information Science and Technology, Fudan University, Shanghai, China
| | - Xinyu Jiang
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Yao Guo
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Hangyu Zhu
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Chenyun Dai
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Wei Chen
- School of Information Science and Technology, Fudan University, Shanghai, China.
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13
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Stoner L, Higgins S, Paterson C. The 24-h activity cycle and cardiovascular outcomes: establishing biological plausibility using arterial stiffness as an intermediate outcome. Am J Physiol Heart Circ Physiol 2023; 325:H1243-H1263. [PMID: 37737729 DOI: 10.1152/ajpheart.00258.2023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 09/07/2023] [Accepted: 09/15/2023] [Indexed: 09/23/2023]
Abstract
This review proposes a biologically plausible working model for the relationship between the 24-h activity cycle (24-HAC) and cardiovascular disease. The 24-HAC encompasses moderate-to-vigorous physical activity (MVPA), light physical activity, sedentary behavior (SB), and sleep. MVPA confers the greatest relative cardioprotective effect, when considering MVPA represents just 2% of the day if physical activity guidelines (30 min/day) are met. While we have well-established guidelines for MVPA, those for the remaining activity behaviors are vague. The vague guidelines are attributable to our limited mechanistic understanding of the independent and additive effects of these behaviors on the cardiovascular system. Our proposed biological model places arterial stiffness, a measure of vascular aging, as the key intermediate outcome. Starting with prolonged exposure to SB or static standing, we propose that the reported transient increases in arterial stiffness are driven by a cascade of negative hemodynamic effects following venous pooling. The subsequent autonomic, metabolic, and hormonal changes further impair vascular function. Vascular dysfunction can be offset by using mechanistic-informed interruption strategies and by engaging in protective behaviors throughout the day. Physical activity, especially MVPA, can confer protection by chronically improving endothelial function and associated protective mechanisms. Conversely, poor sleep, especially in duration and quality, negatively affects hormonal, metabolic, autonomic, and hemodynamic variables that can confound the physiological responses to next-day activity behaviors. Our hope is that the proposed biologically plausible working model will assist in furthering our understanding of the effects of these complex, interrelated activity behaviors on the cardiovascular system.
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Affiliation(s)
- Lee Stoner
- Department of Exercise and Sport Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
- Department of Epidemiology, The Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
- Center for Health Promotion and Disease Prevention, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
| | - Simon Higgins
- Department of Exercise and Sport Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
| | - Craig Paterson
- Department of Exercise and Sport Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
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14
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Lyu J, Shi W, Zhang C, Yeh CH. A Novel Sleep Staging Method Based on EEG and ECG Multimodal Features Combination. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4073-4084. [PMID: 37819827 DOI: 10.1109/tnsre.2023.3323892] [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: 10/13/2023]
Abstract
Accurate sleep staging evaluates the quality of sleep, supporting the clinical diagnosis and intervention of sleep disorders and related diseases. Although previous attempts to classify sleep stages have achieved high classification performance, little attention has been paid to integrating the rich information in brain and heart dynamics during sleep for sleep staging. In this study, we propose a generalized EEG and ECG multimodal feature combination to classify sleep stages with high efficiency and accuracy. Briefly, a hybrid features combination in terms of multiscale entropy and intrinsic mode function are used to reflect nonlinear dynamics in multichannel EEGs, along with heart rate variability measures over time/frequency domains, and sample entropy across scales are applied for ECGs. For both the max-relevance and min-redundancy method and principal component analysis were used for dimensionality reduction. The selected features were classified by four traditional machine learning classifiers. Macro-F1 score, macro-geometric mean, and Cohen kappa value are adopted to evaluate the classification performance of each class in an imbalanced dataset. Experimental results show that EEG features contribute more to wake stage classification while ECG features contribute more to deep sleep stages. The proposed combination achieves the highest accuracy of 84.3% and the highest kappa value of 0.794 on the support vector machine in the ISRUC-S3 dataset, suggesting the proposed multimodal features combination is promising in accuracy and efficiency compared to other state-of-the-art methods.
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15
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Zan H, Yildiz A. Multi-task learning for arousal and sleep stage detection using fully convolutional networks. J Neural Eng 2023; 20:056034. [PMID: 37769664 DOI: 10.1088/1741-2552/acfe3a] [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: 04/29/2023] [Accepted: 09/28/2023] [Indexed: 10/03/2023]
Abstract
Objective.Sleep is a critical physiological process that plays a vital role in maintaining physical and mental health. Accurate detection of arousals and sleep stages is essential for the diagnosis of sleep disorders, as frequent and excessive occurrences of arousals disrupt sleep stage patterns and lead to poor sleep quality, negatively impacting physical and mental health. Polysomnography is a traditional method for arousal and sleep stage detection that is time-consuming and prone to high variability among experts.Approach. In this paper, we propose a novel multi-task learning approach for arousal and sleep stage detection using fully convolutional neural networks. Our model, FullSleepNet, accepts a full-night single-channel EEG signal as input and produces segmentation masks for arousal and sleep stage labels. FullSleepNet comprises four modules: a convolutional module to extract local features, a recurrent module to capture long-range dependencies, an attention mechanism to focus on relevant parts of the input, and a segmentation module to output final predictions.Main results.By unifying the two interrelated tasks as segmentation problems and employing a multi-task learning approach, FullSleepNet achieves state-of-the-art performance for arousal detection with an area under the precision-recall curve of 0.70 on Sleep Heart Health Study and Multi-Ethnic Study of Atherosclerosis datasets. For sleep stage classification, FullSleepNet obtains comparable performance on both datasets, achieving an accuracy of 0.88 and an F1-score of 0.80 on the former and an accuracy of 0.83 and an F1-score of 0.76 on the latter.Significance. Our results demonstrate that FullSleepNet offers improved practicality, efficiency, and accuracy for the detection of arousal and classification of sleep stages using raw EEG signals as input.
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Affiliation(s)
- Hasan Zan
- Vocational School, Mardin Artuklu University, Mardin, Turkey
| | - Abdulnasır Yildiz
- Department of Electrical and Electronics Engineering, Dicle University, Diyarbakir, Turkey
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16
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Ji X, Li Y, Wen P. 3DSleepNet: A Multi-Channel Bio-Signal Based Sleep Stages Classification Method Using Deep Learning. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3513-3523. [PMID: 37639413 DOI: 10.1109/tnsre.2023.3309542] [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: 08/31/2023]
Abstract
A novel multi-channel-based 3D convolutional neural network (3D-CNN) is proposed in this paper to classify sleep stages. Time domain features, frequency domain features, and time-frequency domain features are extracted from electroencephalography (EEG), electromyogram (EMG), and electrooculogram (EOG) channels and fed into the 3D-CNN model to classify sleep stages. Intrinsic connections among different bio-signals and different frequency bands in time series and time-frequency are learned by 3D convolutional layers, while the frequency relations are learned by 2D convolutional layers. Partial dot-product attention layers help this model find the most important channels and frequency bands in different sleep stages. A long short-term memory unit is added to learn the transition rules among neighboring epochs. Classification experiments were conducted using both ISRUC-S3 datasets and ISRUC-S1, sleep-disorder datasets. The experimental results showed that the overall accuracy achieved 0.832 and the F1-score and Cohen's kappa reached 0.814 and 0.783, respectively, on ISRUC-S3, which are a competitive classification performance with the state-of-the-art baselines. The overall accuracy, F1-score, and Cohen's kappa on ISRUC-S1 achieved 0.820, 0.797, and 0.768, respectively, which also demonstrate its generality on unhealthy subjects. Further experiments were conducted on ISRUC-S3 subset to evaluate its training time. The training time on 10 subjects from ISRUC-S3 with 8549 epochs is 4493s, which indicates its highest calculation speed compared with the existing high-performance graph convolutional networks and [Formula: see text]Net architecture algorithms.
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17
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Dai Y, Li X, Liang S, Wang L, Duan Q, Yang H, Zhang C, Chen X, Li L, Li X, Liao X. MultiChannelSleepNet: A Transformer-Based Model for Automatic Sleep Stage Classification With PSG. IEEE J Biomed Health Inform 2023; 27:4204-4215. [PMID: 37289607 DOI: 10.1109/jbhi.2023.3284160] [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: 06/10/2023]
Abstract
Automatic sleep stage classification plays an essential role in sleep quality measurement and sleep disorder diagnosis. Although many approaches have been developed, most use only single-channel electroencephalogram signals for classification. Polysomnography (PSG) provides multiple channels of signal recording, enabling the use of the appropriate method to extract and integrate the information from different channels to achieve higher sleep staging performance. We present a transformer encoder-based model, MultiChannelSleepNet, for automatic sleep stage classification with multichannel PSG data, whose architecture is implemented based on the transformer encoder for single-channel feature extraction and multichannel feature fusion. In a single-channel feature extraction block, transformer encoders extract features from time-frequency images of each channel independently. Based on our integration strategy, the feature maps extracted from each channel are fused in the multichannel feature fusion block. Another set of transformer encoders further capture joint features, and a residual connection preserves the original information from each channel in this block. Experimental results on three publicly available datasets demonstrate that our method achieves higher classification performance than state-of-the-art techniques. MultiChannelSleepNet is an efficient method to extract and integrate the information from multichannel PSG data, which facilitates precision sleep staging in clinical applications.
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18
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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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19
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Yao H, Liu T, Zou R, Ding S, Xu Y. A Spatial-Temporal Transformer Architecture Using Multi-Channel Signals for Sleep Stage Classification. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3353-3362. [PMID: 37578925 DOI: 10.1109/tnsre.2023.3305201] [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: 08/16/2023]
Abstract
Sleep stage classification is a fundamental task in diagnosing and monitoring sleep diseases. There are 2 challenges that remain open: (1) Since most methods only rely on input from a single channel, the spatial-temporal relationship of sleep signals has not been fully explored. (2) Lack of sleep data makes models hard to train from scratch. Here, we propose a vision Transformer-based architecture to process multi-channel polysomnogram signals. The method is an end-to-end framework that consists of a spatial encoder, a temporal encoder, and an MLP head classifier. The spatial encoder using a pre-trained Vision Transformer captures spatial information from multiple PSG channels. The temporal encoder utilizing the self-attention mechanism understands transitions between nearby epochs. In addition, we introduce a tailored image generation method to extract features within multi-channel and reshape them for transfer learning. We validate our method on 3 datasets and outperform the state-of-the-art algorithms. Our method fully explores the spatial-temporal relationship among different brain regions and addresses the problem of data insufficiency in clinical environments. Benefiting from reformulating the problem as image classification, the method could be applied to other 1D-signal problems in the future.
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20
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C M J, Vinod AP. Signal Selection Technique based on Statistical Approach for Enhanced Detection of Single-trial Auditory Evoked Potentials. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083184 DOI: 10.1109/embc40787.2023.10340031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Volume conduction from insignificant neuronal sources in the brain poses a challenge to the detection and classification of single-trial low amplitude evoked potentials in electroencephalograph (EEG). This work presents a statistical signal selection method for enhanced detection of single-trial EEG auditory evoked potential (AEP) elicited in the brain in response to subjects' own name audio stimulus. The proposed method comprises of a signal selection stage based on a statistical analysis followed by a support vector machine (SVM)-based classifier. The EEG signals recorded from the Fp1 electrode of 24 subjects are used to generate a classifier-dependent feature vector. With the selected one-quarter of AEP signals, a single-trial classification accuracy of 70.59% is obtained. To the best of our knowledge, this is the first study that reports the classification of single-trial AEPs evoked by subjects' own-name audio stimulus versus familiar-name audio stimulus.
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21
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Siddiquee AT, Lee SK, Kim S, Lee MH, Kim HJ, Shin C. All-cause and major-cause mortality associated with sleep latency in the Korean Genome and Epidemiology Study (KoGES): a population-based prospective cohort study. THE LANCET. HEALTHY LONGEVITY 2023; 4:e316-e325. [PMID: 37421960 DOI: 10.1016/s2666-7568(23)00080-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 04/21/2023] [Accepted: 04/24/2023] [Indexed: 07/10/2023] Open
Abstract
BACKGROUND The all-cause and cause-specific mortality risk associated with sleep latencies in the general adult population is unknown. We aimed to investigate the association of habitual prolonged sleep latency with long-term all-cause and cause-specific mortality in adults. METHODS The Korean Genome and Epidemiology Study (KoGES) is a population-based prospective cohort study comprising community-dwelling men and women aged 40-69 years from Ansan, South Korea. The cohort was studied bi-annually from April 17, 2003, to Dec 15, 2020, and the current analysis included all individuals who completed the Pittsburgh Sleep Quality Index (PSQI) questionnaire between April 17, 2003, and Feb 23, 2005. The final study population comprised 3757 participants. Data were analysed from Aug 1, 2021, to May 31, 2022. The main exposure was sleep latency groups based on the PSQI questionnaire: fell asleep in 15 min or less, fell asleep in 16-30 min, occasional prolonged sleep latency (fell asleep in >30 min once or twice a week in the past month) and habitual prolonged sleep latency (fell asleep in >60 min more than once a week or fell asleep in >30 min ≥3 times a week, or both) in the past month at baseline. Outcomes were all-cause and cause-specific (cancer, cardiovascular disease, and other causes) mortality reported during the 18-year study period. Cox proportional hazards regression models were used to examine the prospective relationship between sleep latency and all-cause mortality, and competing risk analyses were done to investigate the association of sleep latency with cause-specific mortality. FINDINGS During a median follow-up of 16·7 years (IQR 16·3-17·4), 226 deaths were reported. After adjusting for demographic characteristics, physical characteristics, lifestyle factors, chronic conditions, and sleep variables, self-reported habitual prolonged sleep latency was associated with an increased risk of all-cause mortality (hazard ratio [HR] 2·22, 95% CI 1·38-3·57) compared to the reference group (those who fell asleep in 16-30 min). In the fully adjusted model, habitual prolonged sleep latency was associated with a more than doubled risk of dying from cancer compared to the reference group (HR 2·74, 95% CI 1·29-5·82). No significant association was observed between habitual prolonged sleep latency and deaths from cardiovascular disease and other causes. INTERPRETATION In this population-based prospective cohort study, habitual prolonged sleep latency was independently associated with an increased risk of all-cause and cancer-specific mortality in adults (independently of demographic characteristics, lifestyle factors, chronic morbidities, and other sleep variables). Although further studies are warranted to investigate the causality of the relationship, strategies or interventions to prevent habitual prolonged sleep latencies might enhance longevity in the general adult population. FUNDING Korea Centers for Disease Control and Prevention.
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Affiliation(s)
- Ali Tanweer Siddiquee
- Institute of Human Genomic Study, College of Medicine, Korea University, Seoul, South Korea
| | - Seung Ku Lee
- Institute of Human Genomic Study, College of Medicine, Korea University, Seoul, South Korea
| | - Soriul Kim
- Institute of Human Genomic Study, College of Medicine, Korea University, Seoul, South Korea
| | - Min-Hee Lee
- Institute of Human Genomic Study, College of Medicine, Korea University, Seoul, South Korea
| | - Hyeon Jin Kim
- Department of Neurology, Korea University Ansan Hospital, Ansan, South Korea
| | - Chol Shin
- Institute of Human Genomic Study, College of Medicine, Korea University, Seoul, South Korea; Biomedical Research Center, Korea University Ansan Hospital, Ansan, South Korea.
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22
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Nan L, Tang M, Liang B, Mo S, Kang N, Song S, Zhang X, Zeng X. Automated Sagittal Skeletal Classification of Children Based on Deep Learning. Diagnostics (Basel) 2023; 13:diagnostics13101719. [PMID: 37238203 DOI: 10.3390/diagnostics13101719] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 05/02/2023] [Accepted: 05/05/2023] [Indexed: 05/28/2023] Open
Abstract
Malocclusions are a type of cranio-maxillofacial growth and developmental deformity that occur with high incidence in children. Therefore, a simple and rapid diagnosis of malocclusions would be of great benefit to our future generation. However, the application of deep learning algorithms to the automatic detection of malocclusions in children has not been reported. Therefore, the aim of this study was to develop a deep learning-based method for automatic classification of the sagittal skeletal pattern in children and to validate its performance. This would be the first step in establishing a decision support system for early orthodontic treatment. In this study, four different state-of-the-art (SOTA) models were trained and compared by using 1613 lateral cephalograms, and the best performance model, Densenet-121, was selected was further subsequent validation. Lateral cephalograms and profile photographs were used as the input for the Densenet-121 model, respectively. The models were optimized using transfer learning and data augmentation techniques, and label distribution learning was introduced during model training to address the inevitable label ambiguity between adjacent classes. Five-fold cross-validation was conducted for a comprehensive evaluation of our method. The sensitivity, specificity, and accuracy of the CNN model based on lateral cephalometric radiographs were 83.99, 92.44, and 90.33%, respectively. The accuracy of the model with profile photographs was 83.39%. The accuracy of both CNN models was improved to 91.28 and 83.98%, respectively, while the overfitting decreased after addition of label distribution learning. Previous studies have been based on adult lateral cephalograms. Therefore, our study is novel in using deep learning network architecture with lateral cephalograms and profile photographs obtained from children in order to obtain a high-precision automatic classification of the sagittal skeletal pattern in children.
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Affiliation(s)
- Lan Nan
- College of Stomatology, Guangxi Medical University, Nanning 530021, China
| | - Min Tang
- College of Stomatology, Guangxi Medical University, Nanning 530021, China
| | - Bohui Liang
- School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China
| | - Shuixue Mo
- College of Stomatology, Guangxi Medical University, Nanning 530021, China
| | - Na Kang
- College of Stomatology, Guangxi Medical University, Nanning 530021, China
| | - Shaohua Song
- College of Stomatology, Guangxi Medical University, Nanning 530021, China
| | - Xuejun Zhang
- School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China
| | - Xiaojuan Zeng
- College of Stomatology, Guangxi Medical University, Nanning 530021, China
- Guangxi Health Commission Key Laboratory of Prevention and Treatment for Oral Infectious Diseases, Nanning 530021, China
- Guangxi Key Laboratory of Oral and Maxillofacial Rehabilitation and Reconstruction, Nanning 530021, China
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23
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Nasiri S, Ganglberger W, Sun H, Thomas RJ, Westover MB. Exploiting labels from multiple experts in automated sleep scoring. Sleep 2023; 46:zsad034. [PMID: 36795078 PMCID: PMC10171620 DOI: 10.1093/sleep/zsad034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Indexed: 02/17/2023] Open
Affiliation(s)
- Samaneh Nasiri
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Clinical Data Animation Center (CDAC), Boston, MA, USA
- McCance Center for Brain Health, MGH, Boston, MA, USA
| | - Wolfgang Ganglberger
- Harvard Medical School, Boston, MA, USA
- Clinical Data Animation Center (CDAC), Boston, MA, USA
- McCance Center for Brain Health, MGH, Boston, MA, USA
| | - Haoqi Sun
- Harvard Medical School, Boston, MA, USA
- Clinical Data Animation Center (CDAC), Boston, MA, USA
- McCance Center for Brain Health, MGH, Boston, MA, USA
- Department of Medicine, Division of Pulmonary, Critical Care & Sleep, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Robert J Thomas
- Harvard Medical School, Boston, MA, USA
- McCance Center for Brain Health, MGH, Boston, MA, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Clinical Data Animation Center (CDAC), Boston, MA, USA
- McCance Center for Brain Health, MGH, Boston, MA, USA
- Department of Medicine, Division of Pulmonary, Critical Care & Sleep, Beth Israel Deaconess Medical Center, Boston, MA, USA
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24
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Zhu H, Fu C, Shu F, Yu H, Chen C, Chen W. The Effect of Coupled Electroencephalography Signals in Electrooculography Signals on Sleep Staging Based on Deep Learning Methods. Bioengineering (Basel) 2023; 10:573. [PMID: 37237643 PMCID: PMC10215192 DOI: 10.3390/bioengineering10050573] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 04/20/2023] [Accepted: 04/26/2023] [Indexed: 05/28/2023] Open
Abstract
The influence of the coupled electroencephalography (EEG) signal in electrooculography (EOG) on EOG-based automatic sleep staging has been ignored. Since the EOG and prefrontal EEG are collected at close range, it is not clear whether EEG couples in EOG or not, and whether or not the EOG signal can achieve good sleep staging results due to its intrinsic characteristics. In this paper, the effect of a coupled EEG signal in an EOG signal on automatic sleep staging is explored. The blind source separation algorithm was used to extract a clean prefrontal EEG signal. Then the raw EOG signal and clean prefrontal EEG signal were processed to obtain EOG signals coupled with different EEG signal contents. Afterwards, the coupled EOG signals were fed into a hierarchical neural network, including a convolutional neural network and recurrent neural network for automatic sleep staging. Finally, an exploration was performed using two public datasets and one clinical dataset. The results showed that using a coupled EOG signal could achieve an accuracy of 80.4%, 81.1%, and 78.9% for the three datasets, slightly better than the accuracy of sleep staging using the EOG signal without coupled EEG. Thus, an appropriate content of coupled EEG signal in an EOG signal improved the sleep staging results. This paper provides an experimental basis for sleep staging with EOG signals.
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Affiliation(s)
- Hangyu Zhu
- School of Information Science and Technology, Fudan University, Shanghai 200433, China
| | - Cong Fu
- Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China
| | - Feng Shu
- Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
| | - Huan Yu
- Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China
| | - Chen Chen
- Human Phenome Institute, Fudan University, Shanghai 201203, China
| | - Wei Chen
- School of Information Science and Technology, Fudan University, Shanghai 200433, China
- Human Phenome Institute, Fudan University, Shanghai 201203, China
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25
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Liu X, Ding X, Liu J, Nie W, Yuan Q. Automatic focal EEG identification based on deep reinforcement learning. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
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26
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Majeed RR, Alkhafaji SKD. ECG classification system based on multi-domain features approach coupled with least square support vector machine (LS-SVM). Comput Methods Biomech Biomed Engin 2023; 26:540-547. [PMID: 35549774 DOI: 10.1080/10255842.2022.2072684] [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] [Indexed: 11/03/2022]
Abstract
Developing a robust authentication and identification method becomes an urgent demand to protect the integrity of devices data. Although the use of passwords provides an acceptable control and authentication, it has shown much weakness in terms of speed and integrity, which make biometrics the ideal authentication solution. As a result, electrocardiogram (ECG) signals have received a great attention in most authentication systems due to the individualized nature of the ECG signals that make them difficult to counterfeit and ubiquitous. In this paper, we propose a new model for ECG verification using multi-domain features coupled with a least square support vector machine (LS-SVM). Two types of features are investigated to find the best set of features to individual from ECG signals. Time domain and frequency domain features based on optimized Triple Band filter bank are extracted from ECG signals. The extracted features are investigated to figure out the best relevant features and remove the redundant ones. The selected features are fed to three classifiers, including Least Square Support Vector Machine (LS-SVM), K-means, and K-nearest. The obtained results have shown that our ECG biometric authentication system outperforms existing methods. The proposed model obtained an average of accuracy of 88%, 95% with time and frequency features, respectively, while it recorded 99% when a combination of time and frequency features are used to classify ECG signals. A public dataset is used to assess the proposed model.
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Affiliation(s)
- Russel R Majeed
- College of Education for Pure Sciences, University of Thi-Qar, Nasiriyah, Iraq
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Motin MA, Karmakar C, Palaniswami M, Penzel T, Kumar D. Multi-stage sleep classification using photoplethysmographic sensor. ROYAL SOCIETY OPEN SCIENCE 2023; 10:221517. [PMID: 37063995 PMCID: PMC10090868 DOI: 10.1098/rsos.221517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 03/24/2023] [Indexed: 06/19/2023]
Abstract
The conventional approach to monitoring sleep stages requires placing multiple sensors on patients, which is inconvenient for long-term monitoring and requires expert support. We propose a single-sensor photoplethysmographic (PPG)-based automated multi-stage sleep classification. This experimental study recorded the PPG during the entire night's sleep of 10 patients. Data analysis was performed to obtain 79 features from the recordings, which were then classified according to sleep stages. The classification results using support vector machine (SVM) with the polynomial kernel yielded an overall accuracy of 84.66%, 79.62% and 72.23% for two-, three- and four-stage sleep classification. These results show that it is possible to conduct sleep stage monitoring using only PPG. These findings open the opportunities for PPG-based wearable solutions for home-based automated sleep monitoring.
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Affiliation(s)
- Mohammod Abdul Motin
- Department of Electrical and Electronic Engineering, Rajshahi University of Engineering and Technology, Kazla, Rajshahi 6204, Bangladesh
| | - Chandan Karmakar
- School of IT, Deakin University, Burwood, Melbourne, VIC 3125, Australia
| | - Marimuthu Palaniswami
- Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Thomas Penzel
- Interdisciplinary Sleep Medicine Center, Charite Universitatsmedizin, 10117 Berlin, Germany
| | - Dinesh Kumar
- School of Electrical and Biomedical Engineering, RMIT University, Melbourne, VIC 3001, Australia
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Yu Y, Qiu Y, Li G, Zhang K, Bo B, Pei M, Ye J, Thompson GJ, Cang J, Fang F, Feng Y, Duan X, Tong C, Liang Z. Sleep fMRI with simultaneous electrophysiology at 9.4 T in male mice. Nat Commun 2023; 14:1651. [PMID: 36964161 PMCID: PMC10039056 DOI: 10.1038/s41467-023-37352-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 03/14/2023] [Indexed: 03/26/2023] Open
Abstract
Sleep is ubiquitous and essential, but its mechanisms remain unclear. Studies in animals and humans have provided insights of sleep at vastly different spatiotemporal scales. However, challenges remain to integrate local and global information of sleep. Therefore, we developed sleep fMRI based on simultaneous electrophysiology at 9.4 T in male mice. Optimized un-anesthetized mouse fMRI setup allowed manifestation of NREM and REM sleep, and a large sleep fMRI dataset was collected and openly accessible. State dependent global patterns were revealed, and state transitions were found to be global, asymmetrical and sequential, which can be predicted up to 17.8 s using LSTM models. Importantly, sleep fMRI with hippocampal recording revealed potentiated sharp-wave ripple triggered global patterns during NREM than awake state, potentially attributable to co-occurrence of spindle events. To conclude, we established mouse sleep fMRI with simultaneous electrophysiology, and demonstrated its capability by revealing global dynamics of state transitions and neural events.
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Affiliation(s)
- Yalin Yu
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yue Qiu
- Department of Anesthesia, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Gen Li
- Department of Biomedical Engineering, College of Future Technology, Academy for Advanced Interdisciplinary Studies, National Biomedical Imaging Centre, Peking University, Beijing, China
| | - Kaiwei Zhang
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Binshi Bo
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Mengchao Pei
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Jingjing Ye
- iHuman Institute, ShanghaiTech University, Shanghai, China
| | | | - Jing Cang
- Department of Anesthesia, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Fang Fang
- Department of Anesthesia, Zhongshan Hospital, Fudan University, Shanghai, China
- The Central Hospital of Xuhui District, Shanghai, China
| | - Yanqiu Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Xiaojie Duan
- Department of Biomedical Engineering, College of Future Technology, Academy for Advanced Interdisciplinary Studies, National Biomedical Imaging Centre, Peking University, Beijing, China.
| | - Chuanjun Tong
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
| | - Zhifeng Liang
- Institute of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
- Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, China.
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Peketi S, Dhok SB. Machine Learning Enabled P300 Classifier for Autism Spectrum Disorder Using Adaptive Signal Decomposition. Brain Sci 2023; 13:brainsci13020315. [PMID: 36831857 PMCID: PMC9954262 DOI: 10.3390/brainsci13020315] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 02/06/2023] [Accepted: 02/08/2023] [Indexed: 02/16/2023] Open
Abstract
Joint attention skills deficiency in Autism spectrum disorder (ASD) hinders individuals from communicating effectively. The P300 Electroencephalogram (EEG) signal-based brain-computer interface (BCI) helps these individuals in neurorehabilitation training to overcome this deficiency. The detection of the P300 signal is more challenging in ASD as it is noisy, has less amplitude, and has a higher latency than in other individuals. This paper presents a novel application of the variational mode decomposition (VMD) technique in a BCI system involving ASD subjects for P300 signal identification. The EEG signal is decomposed into five modes using VMD. Thirty linear and non-linear time and frequency domain features are extracted for each mode. Synthetic minority oversampling technique data augmentation is performed to overcome the class imbalance problem in the chosen dataset. Then, a comparative analysis of three popular machine learning classifiers is performed for this application. VMD's fifth mode with a support vector machine (fine Gaussian kernel) classifier gave the best performance parameters, namely accuracy, F1-score, and the area under the curve, as 91.12%, 91.18%, and 96.6%, respectively. These results are better when compared to other state-of-the-art methods.
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30
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Lin XX, Lin P, Yeh EH, Liu GR, Lien WC, Fang Y. RAPIDEST: A Framework for Obstructive Sleep Apnea Detection. IEEE Trans Neural Syst Rehabil Eng 2023; 31:387-397. [PMID: 36423308 DOI: 10.1109/tnsre.2022.3224474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The traditional polysomnography (PSG) examination for Obstructive Sleep Apnea (OSA) diagnosis needs to measure several signals, such as EEG, ECG, EMG, EOG and the oxygen level in blood, of a patient who may have to wear many sensors during sleep. After the PSG examination, the Apnea-Hypopnea Index (AHI) is calculated based on the measured data to evaluate the severity of apnea and hypopnea for the patient. This process is obviously complicated and inconvenient. In this paper, we propose an AI-based framework, called RAre Pattern Identification and DEtection for Sleep-stage Transitions (RAPIDEST), to detect OSA based on the sequence of sleep stages from which a novel rarity score is defined to capture the unusualness of the sequence of sleep stages. More importantly, under this framework, we only need EEG signals, thus significantly simplifying the signal collection process and reducing the complexity of the severity determination of apnea and hypopnea. We have conducted extensive experiments to verify the relationship between the rarity score and AHI and demonstrate the effectiveness of our proposed approach.
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31
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Radhakrishnan B. L., Ezra K, Jebadurai IJ. Feature Extraction From Single-Channel EEG Using Tsfresh and Stacked Ensemble Approach for Sleep Stage Classification. INTERNATIONAL JOURNAL OF E-COLLABORATION 2023. [DOI: 10.4018/ijec.316774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
The smart world under Industry 4.0 is witnessing a notable spurt in sleep disorders and sleep-related issues in patients. Artificial intelligence and IoT are taking a giant leap in connecting sleep patients remotely with healthcare providers. The contemporary single-channel-based monitoring devices play a tremendous role in predicting sleep quality and related issues. Handcrafted feature extraction is a time-consuming job in machine learning-based automatic sleep classification. The proposed single-channel work uses Tsfresh to extract features from both the EEG channels (Pz-oz and Fpz-Cz) of the SEDFEx database individually to realise a single-channel EEG. The adopted mRMR feature selection approach selected 55 features from the extracted 787 features. A stacking ensemble classifier achieved 95%, 94%, 91%, and 88% accuracy using stratified 5-fold validation in 2, 3, 4, and 5 class classification employing healthy subjects data. The outcome of the experiments indicates that Tsfresh is an excellent tool to extract standard features from EEG signals.
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Affiliation(s)
- Radhakrishnan B. L.
- Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
| | - Kirubakaran Ezra
- Department of Computer Science and Engineering, GRACE College of Engineering, Thoothukudi, India
| | - Immanuel Johnraja Jebadurai
- Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
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32
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Fang Y, Xia Y, Chen P, Zhang J, Zhang Y. A dual-stream deep neural network integrated with adaptive boosting for sleep staging. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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33
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Zhao Y, Lin X, Zhang Z, Wang X, He X, Yang L. STDP-based adaptive graph convolutional networks for automatic sleep staging. Front Neurosci 2023; 17:1158246. [PMID: 37152593 PMCID: PMC10157055 DOI: 10.3389/fnins.2023.1158246] [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: 02/03/2023] [Accepted: 04/03/2023] [Indexed: 05/09/2023] Open
Abstract
Automatic sleep staging is important for improving diagnosis and treatment, and machine learning with neuroscience explainability of sleep staging is shown to be a suitable method to solve this problem. In this paper, an explainable model for automatic sleep staging is proposed. Inspired by the Spike-Timing-Dependent Plasticity (STDP), an adaptive Graph Convolutional Network (GCN) is established to extract features from the Polysomnography (PSG) signal, named STDP-GCN. In detail, the channel of the PSG signal can be regarded as a neuron, the synapse strength between neurons can be constructed by the STDP mechanism, and the connection between different channels of the PSG signal constitutes a graph structure. After utilizing GCN to extract spatial features, temporal convolution is used to extract transition rules between sleep stages, and a fully connected neural network is used for classification. To enhance the strength of the model and minimize the effect of individual physiological signal discrepancies on classification accuracy, STDP-GCN utilizes domain adversarial training. Experiments demonstrate that the performance of STDP-GCN is comparable to the current state-of-the-art models.
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34
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Li D, Ruan Y, Zheng F, Su Y, Lin Q. Fast Sleep Stage Classification Using Cascaded Support Vector Machines with Single-Channel EEG Signals. SENSORS (BASEL, SWITZERLAND) 2022; 22:9914. [PMID: 36560286 PMCID: PMC9784858 DOI: 10.3390/s22249914] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 12/10/2022] [Accepted: 12/13/2022] [Indexed: 06/01/2023]
Abstract
Long-term sleep stage monitoring is very important for the diagnosis and treatment of insomnia. With the development of wearable electroencephalogram (EEG) devices, we developed a fast and accurate sleep stage classification method in this study with single-channel EEG signals for practical applications. The original sleep recordings were collected from the Sleep-EDF database. The wavelet threshold denoising (WTD) method and wavelet packet transformation (WPT) method were applied as signal preprocessing to extract six kinds of characteristic waves. With a comprehensive feature system including time, frequency, and nonlinear dynamics, we obtained the sleep stage classification results with different Support Vector Machine (SVM) models. We proposed a novel classification method based on cascaded SVM models with various features extracted from denoised EEG signals. To enhance the accuracy and generalization performance of this method, nonlinear dynamics features were taken into consideration. With nonlinear dynamics features included, the average classification accuracy was up to 88.11% using this method. In addition, with cascaded SVM models, the classification accuracy of the non-rapid eye movement sleep stage 1 (N1) was enhanced from 41.5% to 55.65% compared with the single SVM model, and the overall classification time for each epoch was less than 1.7 s. Moreover, we demonstrated that it was possible to apply this method for long-term sleep stage monitor applications.
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Affiliation(s)
- Dezhao Li
- Zhejiang Provincial Key Laboratory of Quantum Precision Measurement, Collaborative Innovation Center for Information Technology in Biological and Medical Physics, College of Science, Zhejiang University of Technology, Hangzhou 310023, China
| | - Yangtao Ruan
- Zhejiang Provincial Key Laboratory of Quantum Precision Measurement, Collaborative Innovation Center for Information Technology in Biological and Medical Physics, College of Science, Zhejiang University of Technology, Hangzhou 310023, China
| | - Fufu Zheng
- Zhejiang Provincial Key Laboratory of Quantum Precision Measurement, Collaborative Innovation Center for Information Technology in Biological and Medical Physics, College of Science, Zhejiang University of Technology, Hangzhou 310023, China
| | - Yan Su
- School of Art, Zhejiang International Studies University, Hangzhou 310023, China
| | - Qiang Lin
- Zhejiang Provincial Key Laboratory of Quantum Precision Measurement, Collaborative Innovation Center for Information Technology in Biological and Medical Physics, College of Science, Zhejiang University of Technology, Hangzhou 310023, China
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35
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Wang Q, Guo Y, Shen Y, Tong S, Guo H. Multi-Layer Graph Attention Network for Sleep Stage Classification Based on EEG. SENSORS (BASEL, SWITZERLAND) 2022; 22:9272. [PMID: 36501974 PMCID: PMC9735886 DOI: 10.3390/s22239272] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 11/24/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
Graph neural networks have been successfully applied to sleep stage classification, but there are still challenges: (1) How to effectively utilize epoch information of EEG-adjacent channels owing to their different interaction effects. (2) How to extract the most representative features according to confused transitional information in confused stages. (3) How to improve classification accuracy of sleep stages compared with existing models. To address these shortcomings, we propose a multi-layer graph attention network (MGANet). Node-level attention prompts the graph attention convolution and GRU to focus on and differentiate the interaction between channels in the time-frequency domain and the spatial domain, respectively. The multi-head spatial-temporal mechanism balances the channel weights and dynamically adjusts channel features, and a multi-layer graph attention network accurately expresses the spatial sleep information. Moreover, stage-level attention is applied to easily confused sleep stages, which effectively improves the limitations of a graph convolutional network in large-scale graph sleep stages. The experimental results demonstrated classification accuracy; MF1 and Kappa reached 0.825, 0.814, and 0.775 and 0.873, 0.801, and 0.827 for the ISRUC and SHHS datasets, respectively, which showed that MGANet outperformed the state-of-the-art baselines.
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Affiliation(s)
| | - Yecai Guo
- Correspondence: ; Tel.: +86-151-8980-2968
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36
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Sohaib M, Ghaffar A, Shin J, Hasan MJ, Suleman MT. Automated Analysis of Sleep Study Parameters Using Signal Processing and Artificial Intelligence. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:13256. [PMID: 36293844 PMCID: PMC9603486 DOI: 10.3390/ijerph192013256] [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: 08/20/2022] [Revised: 09/27/2022] [Accepted: 10/12/2022] [Indexed: 06/16/2023]
Abstract
An automated sleep stage categorization can readily face noise-contaminated EEG recordings, just as other signal processing applications. Therefore, the denoising of the contaminated signals is inevitable to ensure a reliable analysis of the EEG signals. In this research work, an empirical mode decomposition is used in combination with stacked autoencoders to conduct automatic sleep stage classification with reliable analytical performance. Due to the decomposition of the composite signal into several intrinsic mode functions, empirical mode decomposition offers an effective solution for denoising non-stationary signals such as EEG. Preliminary results showed that through these intrinsic modes, a signal with a high signal-to-noise ratio can be obtained, which can be used for further analysis with confidence. Therefore, later, when statistical features were extracted from the denoised signals and were classified using stacked autoencoders, improved results were obtained for Stage 1, Stage 2, Stage 3, Stage 4, and REM stage EEG signals using this combination.
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Affiliation(s)
- Muhammad Sohaib
- Department of Software Engineering, Lahore Garrison University, Lahore 54000, Pakistan
| | - Ayesha Ghaffar
- Department of Software Engineering, Lahore Garrison University, Lahore 54000, Pakistan
| | - Jungpil Shin
- School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Fukushima, Japan
| | - Md Junayed Hasan
- National Subsea Centre, Robert Gordon University, Scotland AB10 7AQ, UK
| | - Muhammad Taseer Suleman
- Digital Forensics Research and Service Centre, Lahore Garrison University, Lahore 54000, Pakistan or
- Department of Computer Science, School of Systems and Technology, University of Management and Technology Lahore, Lahore 54770, Pakistan
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37
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A comprehensive evaluation of contemporary methods used for automatic sleep staging. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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38
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Automatic sleep stage classification: From classical machine learning methods to deep learning. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103751] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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39
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Ji X, Li Y, Wen P. Jumping Knowledge Based Spatial-temporal Graph Convolutional Networks for Automatic Sleep Stage Classification. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1464-1472. [PMID: 35584068 DOI: 10.1109/tnsre.2022.3176004] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
A novel jumping knowledge spatial-temporal graph convolutional network (JK-STGCN) is proposed in this paper to classify sleep stages. Based on this method, different types of multi-channel bio-signals, including electroencephalography (EEG), electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG) are utilized to classify sleep stages, after extracting features by a standard convolutional neural network (CNN) named FeatureNet. Intrinsic connections among different bio-signal channels from the identical epoch and neighboring epochs can be obtained through two adaptive adjacency matrices learning methods. A jumping knowledge spatial-temporal graph convolution module helps the JK-STGCN model to extract spatial features from the graph convolutions efficiently and temporal features are extracted from its common standard convolutions to learn the transition rules among sleep stages. Experimental results on the ISRUC-S3 dataset showed that the overall accuracy achieved 0.831 and the F1-score and Cohen kappa reached 0.814 and 0.782, respectively, which are the competitive classification performance with the state-of-the-art baselines. Further experiments on the ISRUC-S3 dataset are also conducted to evaluate the execution efficiency of the JK-STGCN model. The training time on 10 subjects is 2621s and the testing time on 50 subjects is 6.8s, which indicates its highest calculation speed compared with the existing high-performance graph convolutional networks and U-Net architecture algorithms. Experimental results on the ISRUC-S1 dataset also demonstrate its generality, whose accuracy, F1-score, and Cohen kappa achieve 0.820, 0.798, and 0.767 respectively.
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40
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Zhu H, Wu Y, Shen N, Fan J, Tao L, Fu C, Yu H, Wan F, Pun SH, Chen C, Chen W. The Masking Impact of Intra-artifacts in EEG on Deep Learning-based Sleep Staging Systems: A Comparative Study. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1452-1463. [PMID: 35536800 DOI: 10.1109/tnsre.2022.3173994] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Elimination of intra-artifacts in EEG has been overlooked in most of the existing sleep staging systems, especially in deep learning-based approaches. Whether intra-artifacts, originated from the eye movement, chin muscle firing, or heart beating, etc., in EEG signals would lead to a positive or a negative masking effect on deep learning-based sleep staging systems was investigated in this paper. We systematically analyzed several traditional pre-processing methods involving fast Independent Component Analysis (FastICA), Information Maximization (Infomax), and Second-order Blind Source Separation (SOBI). On top of these methods, a SOBI-WT method based on the joint use of the SOBI and Wavelet Transform (WT) is proposed. It offered an effective solution for suppressing artifact components while retaining residual informative data. To provide a comprehensive comparative analysis, these pre-processing methods were applied to eliminate the intra-artifacts and the processed signals were fed to two ready-to-use deep learning models, namely two-step hierarchical neural network (THNN) and SimpleSleepNet for automatic sleep staging. The evaluation was performed on two widely used public datasets, Montreal Archive of Sleep Studies (MASS) and Sleep-EDF Expanded, and a clinical dataset that was collected in Huashan Hospital of Fudan University, Shanghai, China (HSFU). The proposed SOBI-WT method increased the accuracy from 79.0% to 81.3% on MASS, 83.3% to 85.7% on Sleep-EDF Expanded, and 75.5% to 77.1% on HSFU compared with the raw EEG signal, respectively. Experimental results demonstrate that the intra-artifacts bring out a masking negative impact on the deep learning-based sleep staging systems and the proposed SOBI-WT method has the best performance in diminishing this negative impact compared with other artifact elimination methods.
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41
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Staud R. Advances in the management of fibromyalgia: what is the state of the art? Expert Opin Pharmacother 2022; 23:979-989. [PMID: 35509228 DOI: 10.1080/14656566.2022.2071606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Fibromyalgia (FM) is a chronic pain syndrome associated with fatigue, insomnia, dyscognition, and emotional distress. Critical illness mechanisms include central sensitization to nociceptive and non-nociceptive stimuli often resulting in hypersensitivity to all sensory input. AREAS COVERED The clinical presentation of FM can vary widely and therefore requires therapies tailored to each patient's set of symptoms. This manuscript examines currently prescribed therapeutic approaches supported by empirical evidence as well as promising novel treatments. Although pharmacological therapy until now has been only moderately effective for FM symptoms, it represents a critical component of every treatment plan. EXPERT OPINION Currently approved pharmacological therapies for FM symptoms have limited but proven effectiveness. Novel therapies with cannabinoids and naltrexone appear promising. Recent functional imaging studies of FM have discovered multiple brain network abnormalities that may provide novel targets for mechanism-based therapies. Future treatment approaches, however, need to improve more than clinical pain but also other FM domains like fatigue, insomnia, and distress.
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Affiliation(s)
- Roland Staud
- Division of Rheumatology and Clinical Immunology, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
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42
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Zhou D, Wang J, Hu G, Zhang J, Li F, Yan R, Kettunen L, Chang Z, Xu Q, Cong F. SingleChannelNet: A model for automatic sleep stage classification with raw single-channel EEG. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103592] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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43
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Li T, Zhang B, Lv H, Hu S, Xu Z, Tuergong Y. CAttSleepNet: Automatic End-to-End Sleep Staging Using Attention-Based Deep Neural Networks on Single-Channel EEG. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:5199. [PMID: 35564593 PMCID: PMC9104971 DOI: 10.3390/ijerph19095199] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 04/20/2022] [Accepted: 04/22/2022] [Indexed: 12/04/2022]
Abstract
Accurate sleep staging results can be used to measure sleep quality, providing a reliable basis for the prevention and diagnosis of sleep-related diseases. The key to sleep staging is the feature representation of EEG signals. Existing approaches rarely consider local features in feature extraction, and fail to distinguish the importance of critical and non-critical local features. We propose an innovative model for automatic sleep staging with single-channel EEG, named CAttSleepNet. We add an attention module to the convolutional neural network (CNN) that can learn the weights of local sequences of EEG signals by exploiting intra-epoch contextual information. Then, a two-layer bidirectional-Long Short-Term Memory (Bi-LSTM) is used to encode the global correlations of successive epochs. Therefore, the feature representations of EEG signals are enhanced by both local and global context correlation. Experimental results achieved on two real-world sleep datasets indicate that the CAttSleepNet model outperforms existing models. Moreover, ablation experiments demonstrate the validity of our proposed attention module.
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Affiliation(s)
- Tingting Li
- School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China; (T.L.); (H.L.); (S.H.); (Z.X.)
| | - Bofeng Zhang
- School of Computer and Communication Engineering, Shanghai Polytechnic University, Shanghai 201209, China
- School of Computer Science and Technology, Kashi University, Kashi 844008, China;
| | - Hehe Lv
- School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China; (T.L.); (H.L.); (S.H.); (Z.X.)
| | - Shengxiang Hu
- School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China; (T.L.); (H.L.); (S.H.); (Z.X.)
| | - Zhikang Xu
- School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China; (T.L.); (H.L.); (S.H.); (Z.X.)
| | - Yierxiati Tuergong
- School of Computer Science and Technology, Kashi University, Kashi 844008, China;
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An Attention-Guided Spatiotemporal Graph Convolutional Network for Sleep Stage Classification. Life (Basel) 2022; 12:life12050622. [PMID: 35629290 PMCID: PMC9144567 DOI: 10.3390/life12050622] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 04/19/2022] [Accepted: 04/19/2022] [Indexed: 12/25/2022] Open
Abstract
Sleep staging has been widely used as an approach in sleep diagnoses at sleep clinics. Graph neural network (GNN)-based methods have been extensively applied for automatic sleep stage classifications with significant results. However, the existing GNN-based methods rely on a static adjacency matrix to capture the features of the different electroencephalogram (EEG) channels, which cannot grasp the information of each electrode. Meanwhile, these methods ignore the importance of spatiotemporal relations in classifying sleep stages. In this work, we propose a combination of a dynamic and static spatiotemporal graph convolutional network (ST-GCN) with inter-temporal attention blocks to overcome two shortcomings. The proposed method consists of a GCN with a CNN that takes into account the intra-frame dependency of each electrode in the brain region to extract spatial and temporal features separately. In addition, the attention block was used to capture the long-range dependencies between the different electrodes in the brain region, which helps the model to classify the dynamics of each sleep stage more accurately. In our experiments, we used the sleep-EDF and the subgroup III of the ISRUC-SLEEP dataset to compare with the most current methods. The results show that our method performs better in accuracy from 4.6% to 5.3%, in Kappa from 0.06 to 0.07, and in macro-F score from 4.9% to 5.7%. The proposed method has the potential to be an effective tool for improving sleep disorders.
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Radhakrishnan BL, Kirubakaran E, Jebadurai IJ, Selvakumar AI, Peter JD. Efficacy of Single-Channel EEG: A Propitious Approach for In-home Sleep Monitoring. Front Public Health 2022; 10:839838. [PMID: 35493356 PMCID: PMC9039057 DOI: 10.3389/fpubh.2022.839838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 03/21/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- B. L. Radhakrishnan
- Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Karunya Nagar, Coimbatore, India
- *Correspondence: B. L. Radhakrishnan
| | - E. Kirubakaran
- Department of Computer Science and Engineering, Grace College of Engineering, HWP Colony, Thoothukudi, India
| | - Immanuel Johnraja Jebadurai
- Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Karunya Nagar, Coimbatore, India
| | - A. Immanuel Selvakumar
- Department of Electrical and Electronics Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
| | - J. Dinesh Peter
- Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Karunya Nagar, Coimbatore, India
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Automated Classification of Sleep Stages Using Single-Channel EEG A Machine Learning-Based Method. INTERNATIONAL JOURNAL OF INFORMATION RETRIEVAL RESEARCH 2022. [DOI: 10.4018/ijirr.299941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The main contribution of this paper is to present a novel approach for classifying the sleep stages based on optimal feature selection with ensemble learning stacking model using single-channel EEG signals.To find the suitable features from extracted feature vector, we obtained the ReliefF (ReF), Fisher Score (FS) and Online Stream Feature Selection (OSFS) selection algorithms.The proposed research work was performed on two different subgroups of sleep data of ISRUC-Sleep dataset. The experimental results of the proposed methodology signify that single-channel of EEG signal superior to other machine learning classification models with overall accuracies of 97.93%, 97%, and 95.96% using ISRUC-Sleep subgroup-I (SG-I) data and similarly the proposed model achieved an overall accuracies of 98.16%, 98.78%, and 95.26% using ISRUC-Sleep subgroup-III (SG-III) data with FS, ReF and OSFS respectively.
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48
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Krikid F, Karfoul A, Chaibi S, Kachenoura A, Nica A, Kachouri A, Le Bouquin Jeannès R. Classification of High Frequency Oscillations in intracranial EEG signals based on coupled time-frequency and image-related features. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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49
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Gangadharan K S, Vinod AP. Drowsiness detection using portable wireless EEG. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 214:106535. [PMID: 34861615 DOI: 10.1016/j.cmpb.2021.106535] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Revised: 10/23/2021] [Accepted: 11/12/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE The ever-increasing fatality rate due to traffic and workplace accidents, resulting from drowsiness have been a persistent concern during the past years. An efficient technology capable of monitoring and detecting drowsiness can help to alleviate this concern and has potential applications in driver vigilance monitoring, vigilance monitoring in air traffic control rooms and other safety critical work places. In this paper, we present the feasibility of a wearable light weight wireless consumer grade Electroencephalogram (EEG)-based drowsiness detection. METHODS A set of informative features were extracted from short daytime nap EEG signals and their applicability in discriminating between alert and drowsy state was studied. We derived an optimal set of EEG features, that give maximum detection rate for the drowsy state. In addition, heart rate was also recorded concurrently with EEG and correlation between heart rate and the EEG features corresponding to drowsiness was also studied. RESULTS Using the selected features, the EEG data is shown to be capable of classifying alert and drowsy states with an accuracy of 78.3% using Support Vector Machine classifier employing cross subject validation. The feature selection results also revealed that, the EEG features extracted from the temporal electrodes are more significant for drowsiness detection than the features from frontal electrodes. In addition, EEG features extracted from the temporal electrodes yielded higher correlation coefficient with heart rate, which was in concordance with the feature selection results. CONCLUSIONS The results reveal that using the proposed drowsiness detection algorithm, it is possible to perform drowsiness detection using a single EEG electrode placed behind the ear.
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Affiliation(s)
- Sagila Gangadharan K
- Department of Electrical Engineering, Indian Institute of Technology Palakkad, Palakkad, India.
| | - A P Vinod
- Department of Electrical Engineering, Indian Institute of Technology Palakkad, Palakkad, India; Department of Electronics and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong
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50
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Abstract
Autonomous vehicles (AVs) enable drivers to devote their primary attention to non-driving-related tasks (NDRTs). Consequently, AVs must provide intelligibility services appropriate to drivers’ in-situ states and in-car activities to ensure driver safety, and accounting for the type of NDRT being performed can result in higher intelligibility. We discovered that sleeping is drivers’ most preferred NDRT, and this could also result in a critical scenario when a take-over request (TOR) occurs. In this study, we designed TOR situations where drivers are woken from sleep in a high-fidelity AV simulator with motion systems, aiming to examine how drivers react to a TOR provided with our experimental conditions. We investigated how driving performance, perceived task workload, AV acceptance, and physiological responses in a TOR vary according to two factors: (1) feedforward timings and (2) presentation modalities. The results showed that when awakened by a TOR alert delivered >10 s prior to an event, drivers were more focused on the driving context and were unlikely to be influenced by TOR modality, whereas TOR alerts delivered <5 s prior needed a visual accompaniment to quickly inform drivers of on-road situations. This study furthers understanding of how a driver’s cognitive and physical demands interact with TOR situations at the moment of waking from sleep and designs effective interventions for intelligibility services to best comply with safety and driver experience in AVs.
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