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Cheng H, Yang Y, Shi J, Li Z, Feng Y, Wang X. Comparison of automated deep neural network against manual sleep stage scoring in clinical data. Comput Biol Med 2024; 179:108855. [PMID: 39029432 DOI: 10.1016/j.compbiomed.2024.108855] [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: 10/29/2023] [Revised: 07/04/2024] [Accepted: 07/05/2024] [Indexed: 07/21/2024]
Abstract
OBJECTIVE To compare the accuracy and generalizability of an automated deep neural network and the Philip Sleepware G3™ Somnolyzer system (Somnolyzer) for sleep stage scoring using American Academy of Sleep Medicine (AASM) guidelines. METHODS Sleep recordings from 104 participants were analyzed by a convolutional neural network (CNN), the Somnolyzer and skillful technicians. Evaluation metrics were derived for different combinations of sleep stages. A further comparison between the Somnolyzer and the CNN model using a single-channel signal as input was also performed. Sleep recordings from 263 participants with a lower prevalence of OSA served as a cross-validation dataset to validate the generalizability of the CNN model. RESULTS The overall agreement between automated and manual scoring for sleep staging in 104 participants outperformed that of the Somnolyzer according to various metrics (accuracy: 81.81 % vs. 77.07 %; F1: 76.36 % vs. 73.80 %; Cohen's kappa: 0.7403 vs. 0.6848). The results showed that the left electrooculography (EOG) single-channel model had minor advantages over the Somnolyzer. In terms of consistency with manual sleep staging, the CNN model demonstrated superior performance in identifying more pronounced sleep transitions, particularly in the N2 stage and sleep latency metrics. Conversely, the Somnolyzer showed enhanced proficiency in the analysis of REM stages, notably in measuring REM latency. The accuracy in the cross-validation set of 263 participants was also above 80 %. CONCLUSIONS The CNN-based automated deep neural network outperformed the Somnolyzer and is sufficiently accurate for sleep study analyses using the AASM classification criteria.
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Affiliation(s)
- Hanrong Cheng
- Department of Sleep Medicine, Institute of Respiratory Diseases, Shenzhen People's Hospital, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, 518020, China.
| | - Yifei Yang
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China
| | - Jingshu Shi
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China
| | - Zhangbo Li
- Shenzhen Gianta Information Technology Co., LTD, Shenzhen, 518048, China
| | - Yang Feng
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China
| | - Xingjun Wang
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China.
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2
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Huang GS, Yen MH, Chang CC, Lai CL, Chen CC. Development of an individualized stable and force-reducing lower-limb exoskeleton. Biomed Phys Eng Express 2024; 10:055039. [PMID: 39212326 DOI: 10.1088/2057-1976/ad686f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 07/29/2024] [Indexed: 09/04/2024]
Abstract
In this study, an individualized and stable passive-control lower-limb exoskeleton robot was developed. Users' joint angles and the center of pressure (CoP) of one of their soles were input into a convolutional neural network (CNN)-long short-term memory (LSTM) model to evaluate and adjust the exoskeleton control scheme. The CNN-LSTM model predicted the fitness of the control scheme and output the results to the exoskeleton robot, which modified its control parameters accordingly to enhance walking stability. The sole's CoP had similar trends during normal walking and passive walking with the developed exoskeleton; they-coordinates of the CoPs with and without the exoskeleton had a correlation of 91%. Moreover, electromyography signals from the rectus femoris muscle revealed that it exerted 40% less force when walking with a stable stride length in the developed system than when walking with an unstable stride length. Therefore, the developed lower-limb exoskeleton can be used to assist users in achieving balanced and stable walking with reduced force application. In the future, this exoskeleton can be used by patients with stroke and lower-limb weakness to achieve stable walking.
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Affiliation(s)
- Guo-Shing Huang
- Department of Electronic Engineering, National Chin-Yi University of Technology, Taichung, Taiwan
| | - Meng-Hua Yen
- Department of Electronic Engineering, National Chin-Yi University of Technology, Taichung, Taiwan
| | - Chia-Chun Chang
- Department of Electronic Engineering, National Chin-Yi University of Technology, Taichung, Taiwan
| | - Chung-Liang Lai
- Department of Physical Medicine and Rehabilitation, Puzi Hospital, Ministry of Health and Welfare, Chiayi, Taiwan
- Department of Occupational Therapy, Asia University, Taichung, Taiwan
| | - Chi-Chun Chen
- Department of Electronic Engineering, National Chin-Yi University of Technology, Taichung, Taiwan
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3
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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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4
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Pradeepkumar J, Anandakumar M, Kugathasan V, Suntharalingham D, Kappel SL, De Silva AC, Edussooriya CUS. Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2893-2904. [PMID: 39102323 DOI: 10.1109/tnsre.2024.3438610] [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: 08/07/2024]
Abstract
Accurate sleep stage classification is significant for sleep health assessment. In recent years, several machine-learning based sleep staging algorithms have been developed, and in particular, deep-learning based algorithms have achieved performance on par with human annotation. Despite improved performance, a limitation of most deep-learning based algorithms is their black-box behavior, which have limited their use in clinical settings. Here, we propose a cross-modal transformer, which is a transformer-based method for sleep stage classification. The proposed cross-modal transformer consists of a cross-modal transformer encoder architecture along with a multi-scale one-dimensional convolutional neural network for automatic representation learning. The performance of our method is on-par with the state-of-the-art methods and eliminates the black-box behavior of deep-learning models by utilizing the interpretability aspect of the attention modules. Furthermore, our method provides considerable reductions in the number of parameters and training time compared to the state-of-the-art methods. Our code is available at https://github.com/Jathurshan0330/Cross-Modal-Transformer. A demo of our work can be found at https://bit.ly/Cross_modal_transformer_demo.
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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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Loh HW, Ooi CP, Oh SL, Barua PD, Tan YR, Acharya UR, Fung DSS. ADHD/CD-NET: automated EEG-based characterization of ADHD and CD using explainable deep neural network technique. Cogn Neurodyn 2024; 18:1609-1625. [PMID: 39104684 PMCID: PMC11297883 DOI: 10.1007/s11571-023-10028-2] [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: 08/08/2023] [Revised: 10/04/2023] [Accepted: 10/23/2023] [Indexed: 08/07/2024] Open
Abstract
In this study, attention deficit hyperactivity disorder (ADHD), a childhood neurodevelopmental disorder, is being studied alongside its comorbidity, conduct disorder (CD), a behavioral disorder. Because ADHD and CD share commonalities, distinguishing them is difficult, thus increasing the risk of misdiagnosis. It is crucial that these two conditions are not mistakenly identified as the same because the treatment plan varies depending on whether the patient has CD or ADHD. Hence, this study proposes an electroencephalogram (EEG)-based deep learning system known as ADHD/CD-NET that is capable of objectively distinguishing ADHD, ADHD + CD, and CD. The 12-channel EEG signals were first segmented and converted into channel-wise continuous wavelet transform (CWT) correlation matrices. The resulting matrices were then used to train the convolutional neural network (CNN) model, and the model's performance was evaluated using 10-fold cross-validation. Gradient-weighted class activation mapping (Grad-CAM) was also used to provide explanations for the prediction result made by the 'black box' CNN model. Internal private dataset (45 ADHD, 62 ADHD + CD and 16 CD) and external public dataset (61 ADHD and 60 healthy controls) were used to evaluate ADHD/CD-NET. As a result, ADHD/CD-NET achieved classification accuracy, sensitivity, specificity, and precision of 93.70%, 90.83%, 95.35% and 91.85% for the internal evaluation, and 98.19%, 98.36%, 98.03% and 98.06% for the external evaluation. Grad-CAM also identified significant channels that contributed to the diagnosis outcome. Therefore, ADHD/CD-NET can perform temporal localization and choose significant EEG channels for diagnosis, thus providing objective analysis for mental health professionals and clinicians to consider when making a diagnosis. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-023-10028-2.
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Affiliation(s)
- Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore
| | - Shu Lih Oh
- Cogninet Australia, Sydney, NSW 2010 Australia
| | - Prabal Datta Barua
- Cogninet Australia, Sydney, NSW 2010 Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007 Australia
- School of Business (Information System), University of Southern Queensland, Darling Heights, Australia
- Australian International Institute of Higher Education, Sydney, NSW 2000 Australia
- School of Science & Technology, University of New England, Armidale, Australia
- School of Biosciences, Taylor’s University, Selangor, Malaysia
- School of Computing, SRM Institute of Science and Technology, Kattankulathur, India
- School of Science and Technology, Kumamoto University, Kumamoto, Japan
- Sydney School of Education and Social work, University of Sydney, Camperdown, Australia
| | - Yi Ren Tan
- Developmental Psychiatry, Institute of Mental Health, Singapore, Singapore
| | - U. Rajendra Acharya
- School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Darling Heights, Australia
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia
- Centre for Health Research, University of Southern Queensland, Springfield, Australia
| | - Daniel Shuen Sheng Fung
- Developmental Psychiatry, Institute of Mental Health, Singapore, Singapore
- Lee Kong Chian School of Medicine, DUKE NUS Medical School, Yong Loo Lin School of Medicine, Nanyang Technological University, National University of Singapore, Singapore, Singapore
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7
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Ray LB, Baena D, Fogel SM. "Counting sheep PSG": EEGLAB-compatible open-source matlab software for signal processing, visualization, event marking and staging of polysomnographic data. J Neurosci Methods 2024; 407:110162. [PMID: 38740142 DOI: 10.1016/j.jneumeth.2024.110162] [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: 04/22/2024] [Accepted: 05/06/2024] [Indexed: 05/16/2024]
Abstract
BACKGROUND Progress in advancing sleep research employing polysomnography (PSG) has been negatively impacted by the limited availability of widely available, open-source sleep-specific analysis tools. NEW METHOD Here, we introduce Counting Sheep PSG, an EEGLAB-compatible software for signal processing, visualization, event marking and manual sleep stage scoring of PSG data for MATLAB. RESULTS Key features include: (1) signal processing tools including bad channel interpolation, down-sampling, re-referencing, filtering, independent component analysis, artifact subspace reconstruction, and power spectral analysis, (2) customizable display of polysomnographic data and hypnogram, (3) event marking mode including manual sleep stage scoring, (4) automatic event detections including movement artifact, sleep spindles, slow waves and eye movements, and (5) export of main descriptive sleep architecture statistics, event statistics and publication-ready hypnogram. COMPARISON WITH EXISTING METHODS Counting Sheep PSG was built on the foundation created by sleepSMG (https://sleepsmg.sourceforge.net/). The scope and functionalities of the current software have made significant advancements in terms of EEGLAB integration/compatibility, preprocessing, artifact correction, event detection, functionality and ease of use. By comparison, commercial software can be costly and utilize proprietary data formats and algorithms, thereby restricting the ability to distribute and share data and analysis results. CONCLUSIONS The field of sleep research remains shackled by an industry that resists standardization, prevents interoperability, builds-in planned obsolescence, maintains proprietary black-box data formats and analysis approaches. This presents a major challenge for the field of sleep research. The need for free, open-source software that can read open-format data is essential for scientific advancement to be made in the field.
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Affiliation(s)
- L B Ray
- School of Psychology, University of Ottawa, Ottawa K1N 6N5, Canada
| | - D Baena
- School of Psychology, University of Ottawa, Ottawa K1N 6N5, Canada; Sleep Unit, University of Ottawa Institute of Mental Health Research at The Royal, Ottawa K1Z 7K4, Canada
| | - S M Fogel
- School of Psychology, University of Ottawa, Ottawa K1N 6N5, Canada; Sleep Unit, University of Ottawa Institute of Mental Health Research at The Royal, Ottawa K1Z 7K4, Canada; University of Ottawa Brain & Mind Research Institute, Ottawa K1H 8M5, Canada.
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8
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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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9
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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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10
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Waqar S, Ghani Khan MU. Sleep stage prediction using multimodal body network and circadian rhythm. PeerJ Comput Sci 2024; 10:e1988. [PMID: 38686009 PMCID: PMC11057653 DOI: 10.7717/peerj-cs.1988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 03/21/2024] [Indexed: 05/02/2024]
Abstract
Quality sleep plays a vital role in living beings as it contributes extensively to the healing process and the removal of waste products from the body. Poor sleep may lead to depression, memory deficits, heart, and metabolic problems, etc. Sleep usually works in cycles and repeats itself by transitioning into different stages of sleep. This study is unique in that it uses wearable devices to collect multiple parameters from subjects and uses this information to predict sleep stages and sleep patterns. For the multivariate multiclass sleep stage prediction problem, we have experimented with both memoryless (ML) and memory-based models on seven database instances, that is, five from the collected dataset and two from the existing datasets. The Random Forest classifier outclassed the ML models that are LR, MLP, kNN, and SVM with accuracy (ACC) of 0.96 and Cohen Kappa 0.96, and the memory-based model long short-term memory (LSTM) performed well on all the datasets with the maximum attained accuracy of 0.88 and Kappa 0.82. The proposed methodology was also validated on a longitudinal dataset, the Multiethnic Study of Atherosclerosis (MESA), with ACC and Kappa of 0.75 and 0.64 for ML models and 0.86 and 0.78 for memory-based models, respectively, and from another benchmarked Apple Watch dataset available on Physio-Net with ACC and Kappa of 0.93 and 0.93 for ML and 0.92 and 0.87 for memory-based models, respectively. The given methodology showed better results than the original work and indicates that the memory-based method works better to capture the sleep pattern.
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Affiliation(s)
- Sahar Waqar
- Department of Computer Engineering, University of Engineering and Technology, Lahore, Lahore, Punjab, Pakistan
| | - Muhammad Usman Ghani Khan
- Department of Computer Science, University of Engineering and Technology, Lahore, Lahore, Punjab, Pakistan
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Yue H, Chen Z, Guo W, Sun L, Dai Y, Wang Y, Ma W, Fan X, Wen W, Lei W. Research and application of deep learning-based sleep staging: Data, modeling, validation, and clinical practice. Sleep Med Rev 2024; 74:101897. [PMID: 38306788 DOI: 10.1016/j.smrv.2024.101897] [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: 10/02/2023] [Revised: 12/30/2023] [Accepted: 01/04/2024] [Indexed: 02/04/2024]
Abstract
Over the past few decades, researchers have attempted to simplify and accelerate the process of sleep stage classification through various approaches; however, only a few such approaches have gained widespread acceptance. Artificial intelligence technology, particularly deep learning, is promising for earning the trust of the sleep medicine community in automated sleep-staging systems, thus facilitating its application in clinical practice and integration into daily life. We aimed to comprehensively review the latest methods that are applying deep learning for enhancing sleep staging efficiency and accuracy. Starting from the requisite "data" for constructing deep learning algorithms, we elucidated the current landscape of this domain and summarized the fundamental modeling process, encompassing signal selection, data pre-processing, model architecture, classification tasks, and performance metrics. Furthermore, we reviewed the applications of automated sleep staging in scenarios such as sleep-disorder screening, diagnostic procedures, and health monitoring and management. Finally, we conducted an in-depth analysis and discussion of the challenges and future in intelligent sleep staging, particularly focusing on large-scale sleep datasets, interdisciplinary collaborations, and human-computer interactions.
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Affiliation(s)
- Huijun Yue
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Zhuqi Chen
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Wenbin Guo
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Lin Sun
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Yidan Dai
- School of Computer Science, South China Normal University, Guangzhou, People's Republic of China
| | - Yiming Wang
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Wenjun Ma
- School of Computer Science, South China Normal University, Guangzhou, People's Republic of China
| | - Xiaomao Fan
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, People's Republic of China
| | - Weiping Wen
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China; Department of Otolaryngology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.
| | - Wenbin Lei
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.
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12
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Masad IS, Alqudah A, Qazan S. Automatic classification of sleep stages using EEG signals and convolutional neural networks. PLoS One 2024; 19:e0297582. [PMID: 38277364 PMCID: PMC10817107 DOI: 10.1371/journal.pone.0297582] [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: 10/19/2023] [Accepted: 01/08/2024] [Indexed: 01/28/2024] Open
Abstract
Sleep stages classification is one of the new topics in studying human life quality because it plays a crucial role in getting a healthy lifestyle. Abnormal changes or absence of normal sleep may lead to different diseases such as heart-related diseases, diabetes, and obesity. In general, sleep staging analysis can be performed using electroencephalography (EEG) signals. This study proposes a convolutional neural network (CNN) based methodology for sleep stage classification using EEG signals taken by six channels and transformed into time-frequency analysis images. The proposed methodology consists of three major steps: (i) segment the EEG signal into epochs with 30 seconds in length, (ii) convert epochs into 2D representation using time-frequency analysis, and (iii) feed the 2D time-frequency analysis to the 2D CNN. The results showed that the proposed methodology is robust and achieved a very high accuracy of 99.39% for channel C4-A1. All other channels have accuracy values above 98.5%, which indicates that any channel can be used for sleep stage classification with high accuracy. The proposed methodology outperformed the methods in the literature in terms of overall accuracy or single channel accuracy. It is expected to provide a great benefit for physicians, especially neurologists; by providing them with a new powerful tool to support the clinical diagnosis of sleep-related diseases.
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Affiliation(s)
- Ihssan S. Masad
- Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid, Jordan
| | - Amin Alqudah
- Department of Computer Engineering, Yarmouk University, Irbid, Jordan
| | - Shoroq Qazan
- Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid, Jordan
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13
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Lee J, Kim HC, Lee YJ, Lee S. Development of generalizable automatic sleep staging using heart rate and movement based on large databases. Biomed Eng Lett 2023; 13:649-658. [PMID: 37872992 PMCID: PMC10590335 DOI: 10.1007/s13534-023-00288-6] [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: 02/14/2023] [Revised: 05/12/2023] [Accepted: 05/14/2023] [Indexed: 10/25/2023] Open
Abstract
Purpose With the advancement of deep neural networks in biosignals processing, the performance of automatic sleep staging algorithms has improved significantly. However, sleep staging using only non-electroencephalogram features has not been as successful, especially following the current American Association of Sleep Medicine (AASM) standards. This study presents a fine-tuning based approach to widely generalizable automatic sleep staging using heart rate and movement features trained and validated on large databases of polysomnography. Methods A deep neural network is used to predict sleep stages using heart rate and movement features. The model is optimized on a dataset of 8731 nights of polysomnography recordings labeled using the Rechtschaffen & Kales scoring system, and fine-tuned to a smaller dataset of 1641 AASM-labeled recordings. The model prior to and after fine-tuning is validated on two AASM-labeled external datasets totaling 1183 recordings. In order to measure the performance of the model, the output of the optimized model is compared to reference expert-labeled sleep stages using accuracy and Cohen's κ as key metrics. Results The fine-tuned model showed accuracy of 76.6% with Cohen's κ of 0.606 in one of the external validation datasets, outperforming a previously reported result, and showed accuracy of 81.0% with Cohen's κ of 0.673 in another external validation dataset. Conclusion These results indicate that the proposed model is generalizable and effective in predicting sleep stages using features which can be extracted from non-contact sleep monitors. This holds valuable implications for future development of home sleep evaluation systems.
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Affiliation(s)
| | - Hee Chan Kim
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, 03080 South Korea
- Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul, 08826 South Korea
| | - Yu Jin Lee
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, 03080 South Korea
- Center for Sleep and Chronobiology, Seoul National University Hospital, Seoul, 03080 South Korea
| | - Saram Lee
- Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul, 03080 South Korea
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14
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Loh HW, Ooi CP, Oh SL, Barua PD, Tan YR, Molinari F, March S, Acharya UR, Fung DSS. Deep neural network technique for automated detection of ADHD and CD using ECG signal. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 241:107775. [PMID: 37651817 DOI: 10.1016/j.cmpb.2023.107775] [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: 05/24/2023] [Revised: 08/09/2023] [Accepted: 08/22/2023] [Indexed: 09/02/2023]
Abstract
BACKGROUND AND OBJECTIVE Attention Deficit Hyperactivity problem (ADHD) is a common neurodevelopment problem in children and adolescents that can lead to long-term challenges in life outcomes if left untreated. Also, ADHD is frequently associated with Conduct Disorder (CD), and multiple research have found similarities in clinical signs and behavioral symptoms between both diseases, making differentiation between ADHD, ADHD comorbid with CD (ADHD+CD), and CD a subjective diagnosis. Therefore, the goal of this pilot study is to create the first explainable deep learning (DL) model for objective ECG-based ADHD/CD diagnosis as having an objective biomarker may improve diagnostic accuracy. METHODS The dataset used in this study consist of ECG data collected from 45 ADHD, 62 ADHD+CD, and 16 CD patients at the Child Guidance Clinic in Singapore. The ECG data were segmented into 2 s epochs and directly used to train our 1-dimensional (1D) convolutional neural network (CNN) model. RESULTS The proposed model yielded 96.04% classification accuracy, 96.26% precision, 95.99% sensitivity, and 96.11% F1-score. The Gradient-weighted class activation mapping (Grad-CAM) function was also used to highlight the important ECG characteristics at specific time points that most impact the classification score. CONCLUSION In addition to achieving model performance results with our suggested DL method, Grad-CAM's implementation also offers vital temporal data that clinicians and other mental healthcare professionals can use to make wise medical judgments. We hope that by conducting this pilot study, we will be able to encourage larger-scale research with a larger biosignal dataset. Hence allowing biosignal-based computer-aided diagnostic (CAD) tools to be implemented in healthcare and ambulatory settings, as ECG can be easily obtained via wearable devices such as smartwatches.
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Affiliation(s)
- Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Shu Lih Oh
- Cogninet Australia, Sydney, NSW 2010, Australia
| | - Prabal Datta Barua
- Cogninet Australia, Sydney, NSW 2010, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia; School of Business (Information System), University of Southern Queensland, Australia; Australian International Institute of Higher Education, Sydney, NSW 2000, Australia; School of Science & Technology, University of New England, Australia; School of Biosciences, Taylor's University, Malaysia; School of Computing, SRM Institute of Science and Technology, India; School of Science and Technology, Kumamoto University, Japan; Sydney School of Education and Social work, University of Sydney, Australia
| | - Yi Ren Tan
- Developmental Psychiatry, Institute of Mental Health, Singapore
| | - Filippo Molinari
- Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy
| | - Sonja March
- Centre for Health Research and School of Psychology and Wellbeing, University of Southern Queensland, Springfield, Australia
| | - U Rajendra Acharya
- School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, Australia.
| | - Daniel Shuen Sheng Fung
- Developmental Psychiatry, Institute of Mental Health, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, DUKE NUS Medical School, Yong Loo Lin School of Medicine, National University of Singapore
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15
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Huang X, Schmelter F, Irshad MT, Piet A, Nisar MA, Sina C, Grzegorzek M. Optimizing sleep staging on multimodal time series: Leveraging borderline synthetic minority oversampling technique and supervised convolutional contrastive learning. Comput Biol Med 2023; 166:107501. [PMID: 37742416 DOI: 10.1016/j.compbiomed.2023.107501] [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: 07/03/2023] [Revised: 08/15/2023] [Accepted: 09/15/2023] [Indexed: 09/26/2023]
Abstract
Sleep is an important research area in nutritional medicine that plays a crucial role in human physical and mental health restoration. It can influence diet, metabolism, and hormone regulation, which can affect overall health and well-being. As an essential tool in the sleep study, the sleep stage classification provides a parsing of sleep architecture and a comprehensive understanding of sleep patterns to identify sleep disorders and facilitate the formulation of targeted sleep interventions. However, the class imbalance issue is typically salient in sleep datasets, which severely affects classification performances. To address this issue and to extract optimal multimodal features of EEG, EOG, and EMG that can improve the accuracy of sleep stage classification, a Borderline Synthetic Minority Oversampling Technique (B-SMOTE)-Based Supervised Convolutional Contrastive Learning (BST-SCCL) is proposed, which can avoid the risk of data mismatch between various sleep knowledge domains (varying health conditions and annotation rules) and strengthening learning characteristics of the N1 stage from the pair-wise segments comparison strategy. The lightweight residual network architecture with a novel truncated cross-entropy loss function is designed to accommodate multimodal time series and boost the training speed and performance stability. The proposed model has been validated on four well-known public sleep datasets (Sleep-EDF-20, Sleep-EDF-78, ISRUC-1, and ISRUC-3) and its superior performance (overall accuracy of 91.31-92.34%, MF1 of 88.21-90.08%, and Cohen's Kappa coefficient k of 0.87-0.89) has further demonstrated its effectiveness. It shows the great potential of contrastive learning for cross-domain knowledge interaction in precision medicine.
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Affiliation(s)
- Xinyu Huang
- Institute of Medical Informatics, University of Lübeck, Germany.
| | - Franziska Schmelter
- Institute of Nutritional Medicine, University of Lübeck and University Medical Center Schleswig-Holstein, Lübeck, Germany.
| | | | - Artur Piet
- Institute of Medical Informatics, University of Lübeck, Germany.
| | | | - Christian Sina
- Institute of Nutritional Medicine, University of Lübeck and University Medical Center Schleswig-Holstein, Lübeck, Germany; Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering (IMTE), Lübeck, Germany.
| | - Marcin Grzegorzek
- Institute of Medical Informatics, University of Lübeck, Germany; Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering (IMTE), Lübeck, Germany.
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16
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Hangaragi S, Nizampatnam N, Kaliyaperumal D, Özer T. An evolutionary model for sleep quality analytics using fuzzy system. Proc Inst Mech Eng H 2023; 237:1215-1227. [PMID: 37667998 DOI: 10.1177/09544119231195177] [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: 09/06/2023]
Abstract
Electroencephalography (EEG) is a neuro signal reflecting brain activity. These signals provide information about brain activity, eye movements, and muscle tone, which can be used to determine the sleep stage. Categorizing sleep stages can be done manually by visually. Alternatively, automated algorithms can be developed using machine learning techniques to classify sleep stages based on signal features and patterns. This paper aims to automatically classify sleep stages based on extracted patterns from EEG signals. A fuzzy min-max neural network is proposed and implemented for sleep stage classification and clustering. The paper concludes that the fuzzy min-max neural network outperforms other tested methods in sleep stage classification. The models implemented in the study include K-Nearest Neighbor (KNN), Random Forest, Decision Tree, XGBoost, AdaBoost, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Convolutional Neural Network (CNN), and the fuzzy min-max classifier. The results indicate that the fuzzy classifier achieves the highest accuracy of 86%, followed by the CNN model with 81%. Among the machine learning algorithms, Random Forest with an accuracy of 55.46%, followed by XGBoost with 53.18%, surpassing the other algorithms used in the experiment. AdaBoost and Gaussian Naive Bayes both achieve an accuracy of 45.10%. Decision Tree, KNN, LDA, and QDA yield accuracies of 37.66%, 16.46%, 28.57%, and 29.5% respectively. These findings demonstrate the efficiency of the fuzzy min-max neural network and the superiority of the fuzzy classifier and CNN models in sleep stage classification, indicating their potential for accurate automated sleep stage analysis.
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Affiliation(s)
- Shivalila Hangaragi
- Department of Electrionics & Communication Engineering, Amrita School of Engineering, Bengaluru-Amrita Vishwa Vidyapeetham, Bengaluru, Karnataka, India
| | - Neelima Nizampatnam
- Department of Electrionics & Communication Engineering, Amrita School of Engineering, Bengaluru-Amrita Vishwa Vidyapeetham, Bengaluru, Karnataka, India
| | - Deepa Kaliyaperumal
- Department of Electrical & Electronics Engineering, Amrita School of Engineering, Bengaluru-Amrita Vishwa Vidyapeetham, Bengaluru, Karnataka, India
| | - Tolga Özer
- Department of Electrical & Electronics Engineering, Afyon Kocatepe University, Afyonkarahisar, Turkey
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17
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Khan MB, AbuAli N, Hayajneh M, Ullah F, Rehman MU, Chong KT. Software defined radio frequency sensing framework for intelligent monitoring of sleep apnea syndrome. Methods 2023; 218:14-24. [PMID: 37385419 DOI: 10.1016/j.ymeth.2023.06.010] [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: 01/18/2023] [Revised: 04/17/2023] [Accepted: 06/26/2023] [Indexed: 07/01/2023] Open
Abstract
Healthy sleep is vital to all functions in the body. It improves physical and mental health, strengthens resistance against diseases, and develops strong immunity against metabolism and chronic diseases. However, a sleep disorder can cause the inability to sleep well. Sleep apnea syndrome is a critical breathing disorder that occurs during sleeping when breathing stops suddenly and starts when awake, causing sleep disturbance. If it is not treated timely, it can produce loud snoring and drowsiness or causes more acute health problems such as high blood pressure or heart attack. The accepted standard for diagnosing sleep apnea syndrome is full-night polysomnography. However, its limitations include a high cost and inconvenience. This article aims to develop an intelligent monitoring framework for detecting breathing events based on Software Defined Radio Frequency (SDRF) sensing and verify its feasibility for diagnosing sleep apnea syndrome. We extract the wireless channel state information (WCSI) for breathing motion using channel frequency response (CFR) recorded in time at every instant at the receiver. The proposed approach simplifies the receiver structure with the added functionality of communication and sensing together. Initially, simulations are conducted to test the feasibility of the SDRF sensing design for the simulated wireless channel. Then, a real-time experimental setup is developed in a lab environment to address the challenges of the wireless channel. We conducted 100 experiments to collect the dataset of 25 subjects for four breathing patterns. SDRF sensing system accurately detected breathing events during sleep without subject contact. The developed intelligent framework uses machine learning classifiers to classify sleep apnea syndrome and other breathing patterns with an acceptable accuracy of 95.9%. The developed framework aims to build a non-invasive sensing system to diagnose patients conveniently suffering from sleep apnea syndrome. Furthermore, this framework can easily be further extended for E-health applications.
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Affiliation(s)
- Muhammad Bilal Khan
- College of Information Technology, United Arab Emirates University (UAEU), Abu Dhabi 15551, United Arab Emirates.
| | - Najah AbuAli
- College of Information Technology, United Arab Emirates University (UAEU), Abu Dhabi 15551, United Arab Emirates.
| | - Mohammad Hayajneh
- College of Information Technology, United Arab Emirates University (UAEU), Abu Dhabi 15551, United Arab Emirates.
| | - Farman Ullah
- College of Information Technology, United Arab Emirates University (UAEU), Abu Dhabi 15551, United Arab Emirates.
| | - Mobeen Ur Rehman
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea.
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea.
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18
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Sharma M, Verma S, Anand D, Gadre VM, Acharya UR. CAPSCNet: A novel scattering network for automated identification of phasic cyclic alternating patterns of human sleep using multivariate EEG signals. Comput Biol Med 2023; 164:107259. [PMID: 37544251 DOI: 10.1016/j.compbiomed.2023.107259] [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: 05/13/2023] [Revised: 07/03/2023] [Accepted: 07/04/2023] [Indexed: 08/08/2023]
Abstract
The Cyclic Alternating Pattern (CAP) can be considered a physiological marker of sleep instability. The CAP can examine various sleep-related disorders. Certain short events (A and B phases) manifest related to a specific physiological process or pathology during non-rapid eye movement (NREM) sleep. These phases unexpectedly modify EEG oscillations; hence, manual detection is challenging. Therefore, it is highly desirable to have an automated system for detecting the A-phases (AP). Deep convolution neural networks (CNN) have shown high performance in various healthcare applications. A variant of the deep neural network called the Wavelet Scattering Network (WSN) has been used to overcome the specific limitations of CNN, such as the need for a large amount of data to train the model. WSN is an optimized network that can learn features that help discriminate patterns hidden inside signals. Also, WSNs are invariant to local perturbations, making the network significantly more reliable and effective. It can also help improve performance on tasks where data is minimal. In this study, we proposed a novel WSN-based CAPSCNet to automatically detect AP using EEG signals. Seven dataset variants of cyclic alternating pattern (CAP) sleep cohort is employed for this study. Two electroencephalograms (EEG) derivations, namely: C4-A1 and F4-C4, are used to develop the CAPSCNet. The model is examined using healthy subjects and patients tormented by six different sleep disorders, namely: sleep-disordered breathing (SDB), insomnia, nocturnal frontal lobe epilepsy (NFLE), narcolepsy, periodic leg movement disorder (PLM) and rapid eye movement behavior disorder (RBD) subjects. Several different machine-learning algorithms were used to classify the features obtained from the WSN. The proposed CAPSCNet has achieved the highest average classification accuracy of 83.4% using a trilayered neural network classifier for the healthy data variant. The proposed CAPSCNet is efficient and computationally faster.
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Affiliation(s)
- Manish Sharma
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, India.
| | - Sarv Verma
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, India.
| | - Divyansh Anand
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, India.
| | - Vikram M Gadre
- Department of Electrical Engineering, Indian Institute of Technology, Bombay, Mumbai, India.
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield 4300, Australia.
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19
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Lee M, Kwak HG, Kim HJ, Won DO, Lee SW. SeriesSleepNet: an EEG time series model with partial data augmentation for automatic sleep stage scoring. Front Physiol 2023; 14:1188678. [PMID: 37700762 PMCID: PMC10494443 DOI: 10.3389/fphys.2023.1188678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 08/10/2023] [Indexed: 09/14/2023] Open
Abstract
Introduction: We propose an automatic sleep stage scoring model, referred to as SeriesSleepNet, based on convolutional neural network (CNN) and bidirectional long short-term memory (bi-LSTM) with partial data augmentation. We used single-channel raw electroencephalography signals for automatic sleep stage scoring. Methods: Our framework was focused on time series information, so we applied partial data augmentation to learn the connected time information in small series. In specific, the CNN module learns the time information of one epoch (intra-epoch) whereas the bi-LSTM trains the sequential information between the adjacent epochs (inter-epoch). Note that the input of the bi-LSTM is the augmented CNN output. Moreover, the proposed loss function was used to fine-tune the model by providing additional weights. To validate the proposed framework, we conducted two experiments using the Sleep-EDF and SHHS datasets. Results and Discussion: The results achieved an overall accuracy of 0.87 and 0.84 and overall F1-score of 0.80 and 0.78 and kappa value of 0.81 and 0.78 for five-class classification, respectively. We showed that the SeriesSleepNet was superior to the baselines based on each component in the proposed framework. Our architecture also outperformed the state-of-the-art methods with overall F1-score, accuracy, and kappa value. Our framework could provide information on sleep disorders or quality of sleep to automatically classify sleep stages with high performance.
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Affiliation(s)
- Minji Lee
- Department of Biomedical Software Engineering, The Catholic University of Korea, Bucheon, Republic of Korea
| | - Heon-Gyu Kwak
- Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea
| | - Hyeong-Jin Kim
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Dong-Ok Won
- Department of Artificial Intelligence Convergence, Hallym University, Chuncheon, Republic of Korea
| | - Seong-Whan Lee
- Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea
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20
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Lal U, Mathavu Vasanthsena S, Hoblidar A. Temporal Feature Extraction and Machine Learning for Classification of Sleep Stages Using Telemetry Polysomnography. Brain Sci 2023; 13:1201. [PMID: 37626557 PMCID: PMC10452545 DOI: 10.3390/brainsci13081201] [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: 06/29/2023] [Revised: 08/09/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023] Open
Abstract
Accurate sleep stage detection is crucial for diagnosing sleep disorders and tailoring treatment plans. Polysomnography (PSG) is considered the gold standard for sleep assessment since it captures a diverse set of physiological signals. While various studies have employed complex neural networks for sleep staging using PSG, our research emphasises the efficacy of a simpler and more efficient architecture. We aimed to integrate a diverse set of feature extraction measures with straightforward machine learning, potentially offering a more efficient avenue for sleep staging. We also aimed to conduct a comprehensive comparative analysis of feature extraction measures, including the power spectral density, Higuchi fractal dimension, singular value decomposition entropy, permutation entropy, and detrended fluctuation analysis, coupled with several machine-learning models, including XGBoost, Extra Trees, Random Forest, and LightGBM. Furthermore, data augmentation methods like the Synthetic Minority Oversampling Technique were also employed to rectify the inherent class imbalance in sleep data. The subsequent results highlighted that the XGBoost classifier, when used with a combination of all feature extraction measures as an ensemble, achieved the highest performance, with accuracies of 87%, 90%, 93%, 96%, and 97% and average F1-scores of 84.6%, 89%, 90.33%, 93.5%, and 93.5% for distinguishing between five-stage, four-stage, three-stage, and two distinct two-stage sleep configurations, respectively. This combined feature extraction technique represents a novel addition to the body of research since it achieves higher performance than many recently developed deep neural networks by utilising simpler machine-learning models.
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Affiliation(s)
- Utkarsh Lal
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India;
| | - Suhas Mathavu Vasanthsena
- Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India;
| | - Anitha Hoblidar
- Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India;
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21
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Chaddad A, Wu Y, Kateb R, Bouridane A. Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques. SENSORS (BASEL, SWITZERLAND) 2023; 23:6434. [PMID: 37514728 PMCID: PMC10385593 DOI: 10.3390/s23146434] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 06/16/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023]
Abstract
The electroencephalography (EEG) signal is a noninvasive and complex signal that has numerous applications in biomedical fields, including sleep and the brain-computer interface. Given its complexity, researchers have proposed several advanced preprocessing and feature extraction methods to analyze EEG signals. In this study, we analyze a comprehensive review of numerous articles related to EEG signal processing. We searched the major scientific and engineering databases and summarized the results of our findings. Our survey encompassed the entire process of EEG signal processing, from acquisition and pretreatment (denoising) to feature extraction, classification, and application. We present a detailed discussion and comparison of various methods and techniques used for EEG signal processing. Additionally, we identify the current limitations of these techniques and analyze their future development trends. We conclude by offering some suggestions for future research in the field of EEG signal processing.
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Affiliation(s)
- Ahmad Chaddad
- School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, China
- The Laboratory for Imagery, Vision and Artificial Intelligence, Ecole de Technologie Supérieure, Montreal, QC H3C 1K3, Canada
| | - Yihang Wu
- School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, China
| | - Reem Kateb
- College of Computer Science and Engineering, Taibah University, Madinah 41477, Saudi Arabia
| | - Ahmed Bouridane
- Centre for Data Analytics and Cybersecurity, University of Sharjah, Sharjah 27272, United Arab Emirates
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22
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Hasan MN, Koo I. Mixed-Input Deep Learning Approach to Sleep/Wake State Classification by Using EEG Signals. Diagnostics (Basel) 2023; 13:2358. [PMID: 37510104 PMCID: PMC10378260 DOI: 10.3390/diagnostics13142358] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/04/2023] [Accepted: 07/08/2023] [Indexed: 07/30/2023] Open
Abstract
Sleep stage classification plays a pivotal role in predicting and diagnosing numerous health issues from human sleep data. Manual sleep staging requires human expertise, which is occasionally prone to error and variation. In recent times, availability of polysomnography data has aided progress in automatic sleep-stage classification. In this paper, a hybrid deep learning model is proposed for classifying sleep and wake states based on a single-channel electroencephalogram (EEG) signal. The model combines an artificial neural network (ANN) and a convolutional neural network (CNN) trained using mixed-input features. The ANN makes use of statistical features calculated from EEG epochs, and the CNN operates on Hilbert spectrum images generated during each epoch. The proposed method is assessed using single-channel Pz-Oz EEG signals from the Sleep-EDF database Expanded. The classification performance on four randomly selected individuals shows that the proposed model can achieve accuracy of around 96% in classifying between sleep and wake states from EEG recordings.
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Affiliation(s)
- Md Nazmul Hasan
- Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea
| | - Insoo Koo
- Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea
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23
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Mohammed Hussein R, George LE, Sabar Miften F. Accurate method for sleep stages classification using discriminated features and single EEG channel. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/26/2023]
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24
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Zheng Y, Liu C, Lai NYG, Wang Q, Xia Q, Sun X, Zhang S. Current development of biosensing technologies towards diagnosis of mental diseases. Front Bioeng Biotechnol 2023; 11:1190211. [PMID: 37456720 PMCID: PMC10342212 DOI: 10.3389/fbioe.2023.1190211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 06/16/2023] [Indexed: 07/18/2023] Open
Abstract
The biosensor is an instrument that converts the concentration of biomarkers into electrical signals for detection. Biosensing technology is non-invasive, lightweight, automated, and biocompatible in nature. These features have significantly advanced medical diagnosis, particularly in the diagnosis of mental disorder in recent years. The traditional method of diagnosing mental disorders is time-intensive, expensive, and subject to individual interpretation. It involves a combination of the clinical experience by the psychiatrist and the physical symptoms and self-reported scales provided by the patient. Biosensors on the other hand can objectively and continually detect disease states by monitoring abnormal data in biomarkers. Hence, this paper reviews the application of biosensors in the detection of mental diseases, and the diagnostic methods are divided into five sub-themes of biosensors based on vision, EEG signal, EOG signal, and multi-signal. A prospective application in clinical diagnosis is also discussed.
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Affiliation(s)
- Yuhan Zheng
- Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China
- Ningbo Research Center, Ningbo Innovation Center, Zhejiang University, Ningbo, China
- Robotics Institute, Ningbo University of Technology, Ningbo, China
| | - Chen Liu
- Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China
- Ningbo Research Center, Ningbo Innovation Center, Zhejiang University, Ningbo, China
| | - Nai Yeen Gavin Lai
- Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China
| | - Qingfeng Wang
- Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, University of Nottingham Ningbo China, Ningbo, China
| | - Qinghua Xia
- Ningbo Research Center, Ningbo Innovation Center, Zhejiang University, Ningbo, China
| | - Xu Sun
- Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China
- Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, University of Nottingham Ningbo China, Ningbo, China
| | - Sheng Zhang
- Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China
- Ningbo Research Center, Ningbo Innovation Center, Zhejiang University, Ningbo, China
- School of Mechanical Engineering, Zhejiang University, Hangzhou, China
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Wenjian W, Qian X, Jun X, Zhikun H. DynamicSleepNet: a multi-exit neural network with adaptive inference time for sleep stage classification. Front Physiol 2023; 14:1171467. [PMID: 37250117 PMCID: PMC10213983 DOI: 10.3389/fphys.2023.1171467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 04/26/2023] [Indexed: 05/31/2023] Open
Abstract
Sleep is an essential human physiological behavior, and the quality of sleep directly affects a person's physical and mental state. In clinical medicine, sleep stage is an important basis for doctors to diagnose and treat sleep disorders. The traditional method of classifying sleep stages requires sleep experts to classify them manually, and the whole process is time-consuming and laborious. In recent years, with the help of deep learning, automatic sleep stage classification has made great progress, especially networks using multi-modal electrophysiological signals, which have greatly improved in terms of accuracy. However, we found that the existing multimodal networks have a large number of redundant calculations in the process of using multiple electrophysiological signals, and the networks become heavier due to the use of multiple signals, and difficult to be used in small devices. To solve these two problems, this paper proposes DynamicSleepNet, a network that can maximize the use of multiple electrophysiological signals and can dynamically adjust between accuracy and efficiency. DynamicSleepNet consists of three effective feature extraction modules (EFEMs) and three classifier modules, each EFEM is connected to a classifier. Each EFEM is able to extract signal features while making the effective features more prominent and the invalid features are suppressed. The samples processed by the EFEM are given to the corresponding classifier for classification, and if the classifier considers the uncertainty of the sample to be below the threshold we set, the sample can be output early without going through the whole network. We validated our model on four datasets. The results show that the highest accuracy of our model outperforms all baselines. With accuracy close to baselines, our model is faster than the baselines by a factor of several to several tens, and the number of parameters of the model is lower or close. The implementation code is available at: https://github.com/Quinella7291/A-Multi-exit-Neural-Network-with-Adaptive-Inference-Time-for-Sleep-Stage-Classification/.
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Affiliation(s)
- Wang Wenjian
- School of Information Science, Yunnan University, Kunming, China
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Wang S, Xuan W, Chen D, Gu Y, Liu F, Chen J, Xia S, Dong S, Luo J. Machine Learning Assisted Wearable Wireless Device for Sleep Apnea Syndrome Diagnosis. BIOSENSORS 2023; 13:bios13040483. [PMID: 37185558 PMCID: PMC10136920 DOI: 10.3390/bios13040483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/31/2023] [Accepted: 04/14/2023] [Indexed: 05/17/2023]
Abstract
Sleep apnea syndrome (SAS) is a common but underdiagnosed health problem related to impaired quality of life and increased cardiovascular risk. In order to solve the problem of complicated and expensive operation procedures for clinical diagnosis of sleep apnea, here we propose a small and low-cost wearable apnea diagnostic system. The system uses a photoplethysmography (PPG) optical sensor to collect human pulse wave signals and blood oxygen saturation synchronously. Then multiscale entropy and random forest algorithms are used to process the PPG signal for analysis and diagnosis of sleep apnea. The SAS determination is based on the comprehensive diagnosis of the PPG signal and blood oxygen saturation signal, and the blood oxygen is used to exclude the error induced by non-pathological factors. The performance of the system is compared with the Compumedics Grael PSG (Polysomnography) sleep monitoring system. This simple diagnostic system provides a feasible technical solution for portable and low-cost screening and diagnosis of SAS patients with a high accuracy of over 85%.
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Affiliation(s)
- Shaokui Wang
- Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
| | - Weipeng Xuan
- Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
| | - Ding Chen
- Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
| | - Yexin Gu
- Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
| | - Fuhai Liu
- Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
| | - Jinkai Chen
- Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
| | - Shudong Xia
- The Fourth Affiliated Hospital Zhejiang University School of Medicine, Yiwu 322000, China
| | - Shurong Dong
- Key Laboratory of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang, College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou 310027, China
| | - Jikui Luo
- Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
- Key Laboratory of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang, College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou 310027, China
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Li N, Zhu Y, Liu F, Zhang X, Liu Y, Wang X, Gao Z, Guan J, Yin S. Integrative Analysis and Experimental Validation of Competing Endogenous RNAs in Obstructive Sleep Apnea. Biomolecules 2023; 13:biom13040639. [PMID: 37189386 DOI: 10.3390/biom13040639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 03/27/2023] [Accepted: 03/30/2023] [Indexed: 04/05/2023] Open
Abstract
Background: Obstructive sleep apnea (OSA) is highly prevalent yet underdiagnosed. This study aimed to develop a predictive signature, as well as investigate competing endogenous RNAs (ceRNAs) and their potential functions in OSA. Methods: The GSE135917, GSE38792, and GSE75097 datasets were collected from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. Weighted gene correlation network analysis (WGCNA) and differential expression analysis were used to identify OSA-specific mRNAs. Machine learning methods were applied to establish a prediction signature for OSA. Furthermore, several online tools were used to establish the lncRNA-mediated ceRNAs in OSA. The hub ceRNAs were screened using the cytoHubba and validated by real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Correlations between ceRNAs and the immune microenvironment of OSA were also investigated. Results: Two gene co-expression modules closely related to OSA and 30 OSA-specific mRNAs were obtained. They were significantly enriched in the antigen presentation and lipoprotein metabolic process categories. A signature that consisted of five mRNAs was established, which showed a good diagnostic performance in both independent datasets. A total of twelve lncRNA-mediated ceRNA regulatory pathways in OSA were proposed and validated, including three mRNAs, five miRNAs, and three lncRNAs. Of note, we found that upregulation of lncRNAs in ceRNAs could lead to activation of the nuclear factor kappa B (NF-κB) pathway. In addition, mRNAs in the ceRNAs were closely correlated to the increased infiltration level of effector memory of CD4 T cells and CD56bright natural killer cells in OSA. Conclusions: In conclusion, our research opens new possibilities for diagnosis of OSA. The newly discovered lncRNA-mediated ceRNA networks and their links to inflammation and immunity may provide potential research spots for future studies.
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Affiliation(s)
- Niannian Li
- Department of Otolaryngology Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200030, China
- Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200030, China
- Otolaryngology Institute, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200030, China
| | - Yaxin Zhu
- Department of Otolaryngology Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200030, China
- Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200030, China
- Otolaryngology Institute, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200030, China
| | - Feng Liu
- Department of Otolaryngology Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200030, China
- Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200030, China
- Otolaryngology Institute, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200030, China
| | - Xiaoman Zhang
- Department of Otolaryngology Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200030, China
- Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200030, China
- Otolaryngology Institute, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200030, China
| | - Yuenan Liu
- Department of Otolaryngology Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200030, China
- Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200030, China
- Otolaryngology Institute, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200030, China
| | - Xiaoting Wang
- Department of Otolaryngology Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200030, China
- Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200030, China
- Otolaryngology Institute, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200030, China
| | - Zhenfei Gao
- Department of Otolaryngology Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200030, China
- Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200030, China
- Otolaryngology Institute, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200030, China
| | - Jian Guan
- Department of Otolaryngology Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200030, China
- Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200030, China
- Otolaryngology Institute, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200030, China
| | - Shankai Yin
- Department of Otolaryngology Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200030, China
- Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200030, China
- Otolaryngology Institute, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200030, China
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Aristimunha B, Bayerlein AJ, Cardoso MJ, Pinaya WHL, De Camargo RY. Sleep-Energy: An Energy Optimization Method to Sleep Stage Scoring. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2023; 11:34595-34602. [PMID: 38292346 PMCID: PMC10824396 DOI: 10.1109/access.2023.3263477] [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: 02/27/2023] [Accepted: 03/17/2023] [Indexed: 02/01/2024]
Abstract
Sleep is essential for physical and mental health. Polysomnography (PSG) procedures are labour-intensive and time-consuming, making diagnosing sleep disorders difficult. Automatic sleep staging using Machine Learning (ML) - based methods has been studied extensively, but frequently provides noisier predictions incompatible with typical manually annotated hypnograms. We propose an energy optimization method to improve the quality of hypnograms generated by automatic sleep staging procedures. The method evaluates the system's total energy based on conditional probabilities for each epoch's stage and employs an energy minimisation procedure. It can be used as a meta-optimisation layer over the sleep stage sequences generated by any classifier that generates prediction probabilities. The method improved the accuracy of state-of-the-art Deep Learning models in the Sleep EDFx dataset by 4.0% and in the DRM-SUB dataset by 2.8%.
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Affiliation(s)
- Bruno Aristimunha
- Center for Mathematics, Computing and Cognition (CMCC)Federal University of ABC (UFABC)São Paulo09210-580Brazil
- Department of Biomedical EngineeringSchool of Biomedical Engineering and Imaging SciencesKing's College LondonWC2R 2LSLondonU.K
| | - Alexandre Janoni Bayerlein
- Center for Mathematics, Computing and Cognition (CMCC)Federal University of ABC (UFABC)São Paulo09210-580Brazil
| | - M. Jorge Cardoso
- Department of Biomedical EngineeringSchool of Biomedical Engineering and Imaging SciencesKing's College LondonWC2R 2LSLondonU.K
| | - Walter Hugo Lopez Pinaya
- Department of Biomedical EngineeringSchool of Biomedical Engineering and Imaging SciencesKing's College LondonWC2R 2LSLondonU.K
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Huang X, Shirahama K, Irshad MT, Nisar MA, Piet A, Grzegorzek M. Sleep Stage Classification in Children Using Self-Attention and Gaussian Noise Data Augmentation. SENSORS (BASEL, SWITZERLAND) 2023; 23:3446. [PMID: 37050506 PMCID: PMC10098613 DOI: 10.3390/s23073446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 03/20/2023] [Accepted: 03/22/2023] [Indexed: 06/19/2023]
Abstract
The analysis of sleep stages for children plays an important role in early diagnosis and treatment. This paper introduces our sleep stage classification method addressing the following two challenges: the first is the data imbalance problem, i.e., the highly skewed class distribution with underrepresented minority classes. For this, a Gaussian Noise Data Augmentation (GNDA) algorithm was applied to polysomnography recordings to seek the balance of data sizes for different sleep stages. The second challenge is the difficulty in identifying a minority class of sleep stages, given their short sleep duration and similarities to other stages in terms of EEG characteristics. To overcome this, we developed a DeConvolution- and Self-Attention-based Model (DCSAM) which can inverse the feature map of a hidden layer to the input space to extract local features and extract the correlations between all possible pairs of features to distinguish sleep stages. The results on our dataset show that DCSAM based on GNDA obtains an accuracy of 90.26% and a macro F1-score of 86.51% which are higher than those of our previous method. We also tested DCSAM on a well-known public dataset-Sleep-EDFX-to prove whether it is applicable to sleep data from adults. It achieves a comparable performance to state-of-the-art methods, especially accuracies of 91.77%, 92.54%, 94.73%, and 95.30% for six-stage, five-stage, four-stage, and three-stage classification, respectively. These results imply that our DCSAM based on GNDA has a great potential to offer performance improvements in various medical domains by considering the data imbalance problems and correlations among features in time series data.
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Affiliation(s)
- Xinyu Huang
- Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Kimiaki Shirahama
- Department of Informatics, Kindai University, 3-4-1 Kowakae, Higashiosaka City 577-8502, Osaka, Japan
| | - Muhammad Tausif Irshad
- Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
- Department of IT, University of the Punjab, Lahore 54000, Pakistan
| | | | - Artur Piet
- Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Marcin Grzegorzek
- Institute of Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
- Department of Knowledge Engineering, University of Economics, Bogucicka 3, 40287 Katowice, Poland
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30
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Fiorillo L, Monachino G, van der Meer J, Pesce M, Warncke JD, Schmidt MH, Bassetti CLA, Tzovara A, Favaro P, Faraci FD. U-Sleep's resilience to AASM guidelines. NPJ Digit Med 2023; 6:33. [PMID: 36878957 PMCID: PMC9988983 DOI: 10.1038/s41746-023-00784-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 02/21/2023] [Indexed: 03/08/2023] Open
Abstract
AASM guidelines are the result of decades of efforts aiming at standardizing sleep scoring procedure, with the final goal of sharing a worldwide common methodology. The guidelines cover several aspects from the technical/digital specifications, e.g., recommended EEG derivations, to detailed sleep scoring rules accordingly to age. Automated sleep scoring systems have always largely exploited the standards as fundamental guidelines. In this context, deep learning has demonstrated better performance compared to classical machine learning. Our present work shows that a deep learning-based sleep scoring algorithm may not need to fully exploit the clinical knowledge or to strictly adhere to the AASM guidelines. Specifically, we demonstrate that U-Sleep, a state-of-the-art sleep scoring algorithm, can be strong enough to solve the scoring task even using clinically non-recommended or non-conventional derivations, and with no need to exploit information about the chronological age of the subjects. We finally strengthen a well-known finding that using data from multiple data centers always results in a better performing model compared with training on a single cohort. Indeed, we show that this latter statement is still valid even by increasing the size and the heterogeneity of the single data cohort. In all our experiments we used 28528 polysomnography studies from 13 different clinical studies.
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Affiliation(s)
- Luigi Fiorillo
- Institute of Informatics, University of Bern, Bern, Switzerland.
- Institute of Digital Technologies for Personalized Healthcare ∣ MeDiTech, Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Lugano, Switzerland.
| | - Giuliana Monachino
- Institute of Informatics, University of Bern, Bern, Switzerland
- Institute of Digital Technologies for Personalized Healthcare ∣ MeDiTech, Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Lugano, Switzerland
| | - Julia van der Meer
- Sleep Wake Epilepsy Center ∣ NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Marco Pesce
- Sleep Wake Epilepsy Center ∣ NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Jan D Warncke
- Sleep Wake Epilepsy Center ∣ NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Markus H Schmidt
- Sleep Wake Epilepsy Center ∣ NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Claudio L A Bassetti
- Sleep Wake Epilepsy Center ∣ NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Athina Tzovara
- Institute of Informatics, University of Bern, Bern, Switzerland
- Sleep Wake Epilepsy Center ∣ NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Paolo Favaro
- Institute of Informatics, University of Bern, Bern, Switzerland
| | - Francesca D Faraci
- Institute of Digital Technologies for Personalized Healthcare ∣ MeDiTech, Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Lugano, Switzerland
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Bandyopadhyay A, Goldstein C. Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective. Sleep Breath 2023; 27:39-55. [PMID: 35262853 PMCID: PMC8904207 DOI: 10.1007/s11325-022-02592-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 01/25/2022] [Accepted: 03/02/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND The past few years have seen a rapid emergence of artificial intelligence (AI)-enabled technology in the field of sleep medicine. AI refers to the capability of computer systems to perform tasks conventionally considered to require human intelligence, such as speech recognition, decision-making, and visual recognition of patterns and objects. The practice of sleep tracking and measuring physiological signals in sleep is widely practiced. Therefore, sleep monitoring in both the laboratory and ambulatory environments results in the accrual of massive amounts of data that uniquely positions the field of sleep medicine to gain from AI. METHOD The purpose of this article is to provide a concise overview of relevant terminology, definitions, and use cases of AI in sleep medicine. This was supplemented by a thorough review of relevant published literature. RESULTS Artificial intelligence has several applications in sleep medicine including sleep and respiratory event scoring in the sleep laboratory, diagnosing and managing sleep disorders, and population health. While still in its nascent stage, there are several challenges which preclude AI's generalizability and wide-reaching clinical applications. Overcoming these challenges will help integrate AI seamlessly within sleep medicine and augment clinical practice. CONCLUSION Artificial intelligence is a powerful tool in healthcare that may improve patient care, enhance diagnostic abilities, and augment the management of sleep disorders. However, there is a need to regulate and standardize existing machine learning algorithms prior to its inclusion in the sleep clinic.
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Affiliation(s)
- Anuja Bandyopadhyay
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Cathy Goldstein
- Department of Neurology, University of Michigan, Ann Arbor, MI, USA
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Yang CY, Chen PC, Huang WC. Cross-Domain Transfer of EEG to EEG or ECG Learning for CNN Classification Models. SENSORS (BASEL, SWITZERLAND) 2023; 23:2458. [PMID: 36904661 PMCID: PMC10007254 DOI: 10.3390/s23052458] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 02/19/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
Electroencephalography (EEG) is often used to evaluate several types of neurological brain disorders because of its noninvasive and high temporal resolution. In contrast to electrocardiography (ECG), EEG can be uncomfortable and inconvenient for patients. Moreover, deep-learning techniques require a large dataset and a long time for training from scratch. Therefore, in this study, EEG-EEG or EEG-ECG transfer learning strategies were applied to explore their effectiveness for the training of simple cross-domain convolutional neural networks (CNNs) used in seizure prediction and sleep staging systems, respectively. The seizure model detected interictal and preictal periods, whereas the sleep staging model classified signals into five stages. The patient-specific seizure prediction model with six frozen layers achieved 100% accuracy for seven out of nine patients and required only 40 s of training time for personalization. Moreover, the cross-signal transfer learning EEG-ECG model for sleep staging achieved an accuracy approximately 2.5% higher than that of the ECG model; additionally, the training time was reduced by >50%. In summary, transfer learning from an EEG model to produce personalized models for a more convenient signal can both reduce the training time and increase the accuracy; moreover, challenges such as data insufficiency, variability, and inefficiency can be effectively overcome.
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Haghayegh S, Hu K, Stone K, Redline S, Schernhammer E. Automated Sleep Stages Classification Using Convolutional Neural Network From Raw and Time-Frequency Electroencephalogram Signals: Systematic Evaluation Study. J Med Internet Res 2023; 25:e40211. [PMID: 36763454 PMCID: PMC9960035 DOI: 10.2196/40211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 09/09/2022] [Accepted: 01/09/2023] [Indexed: 02/11/2023] Open
Abstract
BACKGROUND Most existing automated sleep staging methods rely on multimodal data, and scoring a specific epoch requires not only the current epoch but also a sequence of consecutive epochs that precede and follow the epoch. OBJECTIVE We proposed and tested a convolutional neural network called SleepInceptionNet, which allows sleep classification of a single epoch using a single-channel electroencephalogram (EEG). METHODS SleepInceptionNet is based on our systematic evaluation of the effects of different EEG preprocessing methods, EEG channels, and convolutional neural networks on automatic sleep staging performance. The evaluation was performed using polysomnography data of 883 participants (937,975 thirty-second epochs). Raw data of individual EEG channels (ie, frontal, central, and occipital) and 3 specific transformations of the data, including power spectral density, continuous wavelet transform, and short-time Fourier transform, were used separately as the inputs of the convolutional neural network models. To classify sleep stages, 7 sequential deep neural networks were tested for the 1D data (ie, raw EEG and power spectral density), and 16 image classifier convolutional neural networks were tested for the 2D data (ie, continuous wavelet transform and short-time Fourier transform time-frequency images). RESULTS The best model, SleepInceptionNet, which uses time-frequency images developed by the continuous wavelet transform method from central single-channel EEG data as input to the InceptionV3 image classifier algorithm, achieved a Cohen κ agreement of 0.705 (SD 0.077) in reference to the gold standard polysomnography. CONCLUSIONS SleepInceptionNet may allow real-time automated sleep staging in free-living conditions using a single-channel EEG, which may be useful for on-demand intervention or treatment during specific sleep stages.
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Affiliation(s)
- Shahab Haghayegh
- Harvard Medical School, Boston, MA, United States
- Brigham and Women's Hospital, Boston, MA, United States
| | - Kun Hu
- Harvard Medical School, Boston, MA, United States
- Brigham and Women's Hospital, Boston, MA, United States
| | - Katie Stone
- California Pacific Medical Center Research Institute, San Francisco, CA, United States
| | - Susan Redline
- Harvard Medical School, Boston, MA, United States
- Brigham and Women's Hospital, Boston, MA, United States
| | - Eva Schernhammer
- Harvard Medical School, Boston, MA, United States
- Brigham and Women's Hospital, Boston, MA, United States
- Medical University of Vienna, Vienna, Austria
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Sharma M, Makwana P, Chad RS, Acharya UR. A novel automated robust dual-channel EEG-based sleep scoring system using optimal half-band pair linear-phase biorthogonal wavelet filter bank. APPL INTELL 2023; 53:1-19. [PMID: 36777881 PMCID: PMC9906594 DOI: 10.1007/s10489-022-04432-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/21/2022] [Indexed: 02/11/2023]
Abstract
Nowadays, the hectic work life of people has led to sleep deprivation. This may further result in sleep-related disorders and adverse physiological conditions. Therefore, sleep study has become an active research area. Sleep scoring is crucial for detecting sleep-related disorders like sleep apnea, insomnia, narcolepsy, periodic leg movement (PLM), and restless leg syndrome (RLS). Sleep is conventionally monitored in a sleep laboratory using polysomnography (PSG) which is the recording of various physiological signals. The traditional sleep stage scoring (SSG) done by professional sleep scorers is a tedious, strenuous, and time-consuming process as it is manual. Hence, developing a machine-learning model for automatic SSG is essential. In this study, we propose an automated SSG approach based on the biorthogonal wavelet filter bank's (BWFB) novel least squares (LS) design. We have utilized a huge Wisconsin sleep cohort (WSC) database in this study. The proposed study is a pioneering work on automatic sleep stage classification using the WSC database, which includes good sleepers and patients suffering from various sleep-related disorders, including apnea, insomnia, hypertension, diabetes, and asthma. To investigate the generalization of the proposed system, we evaluated the proposed model with the following publicly available databases: cyclic alternating pattern (CAP), sleep EDF, ISRUC, MIT-BIH, and the sleep apnea database from St. Vincent's University. This study uses only two unipolar EEG channels, namely O1-M2 and C3-M2, for the scoring. The Hjorth parameters (HP) are extracted from the wavelet subbands (SBS) that are obtained from the optimal BWFB. To classify sleep stages, the HP features are fed to several supervised machine learning classifiers. 12 different datasets have been created to develop a robust model. A total of 12 classification tasks (CT) have been conducted employing various classification algorithms. Our developed model achieved the best accuracy of 83.2% and Cohen's Kappa of 0.7345 to reliably distinguish five sleep stages, using an ensemble bagged tree classifier with 10-fold cross-validation using WSC data. We also observed that our system is either better or competitive with existing state-of-art systems when we tested with the above-mentioned five databases other than WSC. This method yielded promising results using only two EEG channels using a huge WSC database. Our approach is simple and hence, the developed model can be installed in home-based clinical systems and wearable devices for sleep scoring.
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Affiliation(s)
- Manish Sharma
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, 380026 India
| | - Paresh Makwana
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, 380026 India
| | - Rajesh Singh Chad
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, 380026 India
| | - U Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Singapore, 599489 Singapore
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung, 41354 Taiwan
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore, Singapore
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Sun L, Wu J, Xu Y, Zhang Y. A federated learning and blockchain framework for physiological signal classification based on continual learning. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
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A novel approach to automatic sleep stage classification using forehead electrophysiological signals. Heliyon 2022; 8:e12136. [PMID: 36590566 PMCID: PMC9798185 DOI: 10.1016/j.heliyon.2022.e12136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 06/29/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
Background Sleep stage scoring is very important for the effective diagnosis and intervention of sleep disorders. However, the current automatic sleep staging methods generally have the problems of poor model generalization ability and non-portable acquisition equipment. Method In this paper, we propose a novel automatic sleep scoring system based on forehead electrophysiological signals that is more effective and convenient than other systems. We extract 3 channel signals from the forehead, named forehead electroencephalogram 1 (Fh1), forehead electroencephalogram 2 (Fh2), and forehead center electrooculogram (Fhz). Spectral features, statistical features, and entropy features are extracted using the discrete wavelet transform (DWT) method. Light gradient boosting machine (LGB), random forest (RF), and support vector machine (SVM) are employed to classify four stages: awake, light sleep (LS), deep sleep (DS), and rapid eye movement (REM). Result The performance of the proposed method is validated using databases of the Sleep-EDFX and our Own-data, which include polysomnograms (PSGs) and forehead signals of 28 subjects. The overall classification accuracy of using the combination of Fh1, Fh2, and Fhz can reach up to 90.25% accuracy with a kappa coefficient of 0.857. Conclusions The proposed method could provide state-of-art multichannel sleep stage scoring performance with higher portability. This will facilitate the application of long-term monitoring of sleep quality in the future.
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Yu R, Zhou Z, Wu S, Gao X, Bin G. MRASleepNet: a multi-resolution attention network for sleep stage classification using single-channel EEG. J Neural Eng 2022; 19. [PMID: 36379059 DOI: 10.1088/1741-2552/aca2de] [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: 06/09/2022] [Accepted: 11/15/2022] [Indexed: 11/16/2022]
Abstract
Objective. Computerized classification of sleep stages based on single-lead electroencephalography (EEG) signals is important, but still challenging. In this paper, we proposed a deep neural network called MRASleepNet for automatic sleep stage classification using single-channel EEG signals.Approach. The proposed MRASleepNet model consisted of a feature extraction (FE) module, a multi-resolution attention (MRA) module, and a gated multilayer perceptron (gMLP) module, as well as a direct pathway for computing statistical features. The FE, MRA, and gMLP modules were used to extract features, establish feature attention, and obtain temporal relationships between features, respectively. EEG signals were normalized and cut into 30 s segments, and enhanced by incorporating contextual information from adjacent data segments. After data enhancement, the 40 s data segments were input to the MRASleepNet model. The model was evaluated on the SleepEDF and the cyclic alternating pattern (CAP) databases, using such metrics as the accuracy, Kappa, and macro-F1 (MF1).Main results.For the SleepEDF-20 database, the proposed model had an accuracy of 84.5%, an MF1 of 0.789, and a Kappa of 0.786. For the SleepEDF-78 database, the model had an accuracy of 81.4%, an MF1 of 0.754, and a Kappa of 0.743. For the CAP database, the model had an accuracy of 74.3%, an MF1 of 0.656, and a Kappa of 0.652. The proposed model achieved satisfactory performance in automatic sleep stage classification tasks.Significance. The time- and frequency-domain features extracted by the FE module and filtered by the MRA module, together with the temporal features extracted by the gMLP module and the statistical features extracted by the statistical highway, enabled the proposed model to obtain a satisfying performance in sleep staging. The proposed MRASleepNet model may be used as a new deep learning method for automatic sleep stage classification. The code of MRASleepNet will be made available publicly onhttps://github.com/YuRui8879/.
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Affiliation(s)
- Rui Yu
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, People's Republic of China
| | - Zhuhuang Zhou
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, People's Republic of China
| | - Shuicai Wu
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, People's Republic of China
| | - Xiaorong Gao
- Department of Biomedical Engineering, Tsinghua University, 100084 Beijing, People's Republic of China
| | - Guangyu Bin
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, People's Republic of China
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Arslan RS, Ulutas H, Köksal AS, Bakir M, Çiftçi B. Sensitive deep learning application on sleep stage scoring by using all PSG data. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-08037-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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ElMoaqet H, Eid M, Ryalat M, Penzel T. A Deep Transfer Learning Framework for Sleep Stage Classification with Single-Channel EEG Signals. SENSORS (BASEL, SWITZERLAND) 2022; 22:8826. [PMID: 36433422 PMCID: PMC9693852 DOI: 10.3390/s22228826] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 06/16/2023]
Abstract
The polysomnogram (PSG) is the gold standard for evaluating sleep quality and disorders. Attempts to automate this process have been hampered by the complexity of the PSG signals and heterogeneity among subjects and recording hardwares. Most of the existing methods for automatic sleep stage scoring rely on hand-engineered features that require prior knowledge of sleep analysis. This paper presents an end-to-end deep transfer learning framework for automatic feature extraction and sleep stage scoring based on a single-channel EEG. The proposed framework was evaluated over the three primary signals recommended by the American Academy of Sleep Medicine (C4-M1, F4-M1, O2-M1) from two data sets that have different properties and are recorded with different hardware. Different Time-Frequency (TF) imaging approaches were evaluated to generate TF representations for the 30 s EEG sleep epochs, eliminating the need for complex EEG signal pre-processing or manual feature extraction. Several training and detection scenarios were investigated using transfer learning of convolutional neural networks (CNN) and combined with recurrent neural networks. Generating TF images from continuous wavelet transform along with a deep transfer architecture composed of a pre-trained GoogLeNet CNN followed by a bidirectional long short-term memory (BiLSTM) network showed the best scoring performance among all tested scenarios. Using 20-fold cross-validation applied on the C4-M1 channel, the proposed framework achieved an average per-class accuracy of 91.2%, sensitivity of 77%, specificity of 94.1%, and precision of 75.9%. Our results demonstrate that without changing the model architecture and the training algorithm, our model could be applied to different single-channel EEGs from different data sets. Most importantly, the proposed system receives a single EEG epoch as an input at a time and produces a single corresponding output label, making it suitable for real time monitoring outside sleep labs as well as to help sleep lab specialists arrive at a more accurate diagnoses.
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Affiliation(s)
- Hisham ElMoaqet
- Department of Mechatronics Engineering, German Jordanian University, Amman 11180, Jordan
| | - Mohammad Eid
- Department of Biomedical Engineering, German Jordanian University, Amman 11180, Jordan
| | - Mutaz Ryalat
- Department of Mechatronics Engineering, German Jordanian University, Amman 11180, Jordan
| | - Thomas Penzel
- Interdisciplinary Center of Sleep Medicine, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany
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40
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Ileri R, Latifoğlu F, Demirci E. A novel approach for detection of dyslexia using convolutional neural network with EOG signals. Med Biol Eng Comput 2022; 60:3041-3055. [DOI: 10.1007/s11517-022-02656-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 08/19/2022] [Indexed: 10/14/2022]
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41
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van Gorp H, Huijben IAM, Fonseca P, van Sloun RJG, Overeem S, van Gilst MM. Certainty about uncertainty in sleep staging: a theoretical framework. Sleep 2022; 45:6604464. [DOI: 10.1093/sleep/zsac134] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 05/12/2022] [Indexed: 11/14/2022] Open
Abstract
Abstract
Sleep stage classification is an important tool for the diagnosis of sleep disorders. Because sleep staging has such a high impact on clinical outcome, it is important that it is done reliably. However, it is known that uncertainty exists in both expert scorers and automated models. On average, the agreement between human scorers is only 82.6%. In this study, we provide a theoretical framework to facilitate discussion and further analyses of uncertainty in sleep staging. To this end, we introduce two variants of uncertainty, known from statistics and the machine learning community: aleatoric and epistemic uncertainty. We discuss what these types of uncertainties are, why the distinction is useful, where they arise from in sleep staging, and provide recommendations on how this framework can improve sleep staging in the future.
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Affiliation(s)
- Hans van Gorp
- Department of Electrical Engineering, Eindhoven University of Technology , Eindhoven , the Netherlands
- Personal Health, Philips Research , Eindhoven , the Netherlands
| | - Iris A M Huijben
- Department of Electrical Engineering, Eindhoven University of Technology , Eindhoven , the Netherlands
- Onera Health , Eindhoven , the Netherlands
| | - Pedro Fonseca
- Department of Electrical Engineering, Eindhoven University of Technology , Eindhoven , the Netherlands
- Personal Health, Philips Research , Eindhoven , the Netherlands
| | - Ruud J G van Sloun
- Department of Electrical Engineering, Eindhoven University of Technology , Eindhoven , the Netherlands
- Personal Health, Philips Research , Eindhoven , the Netherlands
| | - Sebastiaan Overeem
- Department of Electrical Engineering, Eindhoven University of Technology , Eindhoven , the Netherlands
- Sleep Medicine Centre, Kempenhaeghe Foundation , Eindhoven , the Netherlands
| | - Merel M van Gilst
- Department of Electrical Engineering, Eindhoven University of Technology , Eindhoven , the Netherlands
- Sleep Medicine Centre, Kempenhaeghe Foundation , Eindhoven , the Netherlands
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42
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Horie K, Ota L, Miyamoto R, Abe T, Suzuki Y, Kawana F, Kokubo T, Yanagisawa M, Kitagawa H. Automated sleep stage scoring employing a reasoning mechanism and evaluation of its explainability. Sci Rep 2022; 12:12799. [PMID: 35896616 PMCID: PMC9329306 DOI: 10.1038/s41598-022-16334-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 07/08/2022] [Indexed: 11/16/2022] Open
Abstract
Scoring sleep stages from biological signals is an essential but labor-intensive inspection for sleep diagnosis. The existing automated scoring methods have achieved high accuracy but are not widely applied in clinical practice. In our understanding, the existing methods have failed to establish the trust of sleep experts (e.g., physicians and clinical technologists) due to a lack of ability to explain the evidences/clues for scoring. In this study, we developed a deep-learning-based scoring model with a reasoning mechanism called class activation mapping (CAM) to solve this problem. This mechanism explicitly shows which portions of the signals support our model’s sleep stage decision, and we verified that these portions overlap with the “characteristic waves,” which are evidences/clues used in the manual scoring process. In exchange for the acquisition of explainability, employing CAM makes it difficult to follow some scoring rules. Although we concerned the negative effect of CAM on the scoring accuracy, we have found that the impact is limited. The evaluation experiment shows that the proposed model achieved a scoring accuracy of \documentclass[12pt]{minimal}
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\begin{document}$$86.9\%$$\end{document}86.9%. It is superior to those of some existing methods and the inter-rater reliability among the sleep experts. These results suggest that Sleep-CAM achieved both explainability and required scoring accuracy for practical usage.
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Affiliation(s)
- Kazumasa Horie
- Center for Computational Sciences, University of Tsukuba, Tsukuba, Japan.
| | - Leo Ota
- Center for Computational Sciences, University of Tsukuba, Tsukuba, Japan.
| | - Ryusuke Miyamoto
- Center for Computational Sciences, University of Tsukuba, Tsukuba, Japan
| | - Takashi Abe
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Japan
| | - Yoko Suzuki
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Japan
| | - Fusae Kawana
- Yumino Heart Clinic, Toshima, Japan.,Juntendo University Graduate School of Medicine, Bunkyo, Japan
| | - Toshio Kokubo
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Japan.,S'UIMIN Inc., Shibuya, Japan
| | - Masashi Yanagisawa
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Japan.,R&D Center for Frontiers of Mirai in Policy and Technology, University of Tsukuba, Tsukuba, Japan.,Tsukuba Advanced Research Alliance (TARA), University of Tsukuba, Tsukuba, Japan.,Department of Molecular Genetics, University of Texas Southwestern Medical Center, Dallas, USA
| | - Hiroyuki Kitagawa
- Center for Computational Sciences, University of Tsukuba, Tsukuba, Japan.,International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Japan
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An Automated Wavelet-Based Sleep Scoring Model Using EEG, EMG, and EOG Signals with More Than 8000 Subjects. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19127176. [PMID: 35742426 PMCID: PMC9223057 DOI: 10.3390/ijerph19127176] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 05/27/2022] [Accepted: 06/07/2022] [Indexed: 01/16/2023]
Abstract
Human life necessitates high-quality sleep. However, humans suffer from a lower quality of life because of sleep disorders. The identification of sleep stages is necessary to predict the quality of sleep. Manual sleep-stage scoring is frequently conducted through sleep experts’ visually evaluations of a patient’s neurophysiological data, gathered in sleep laboratories. Manually scoring sleep is a tough, time-intensive, tiresome, and highly subjective activity. Hence, the need of creating automatic sleep-stage classification has risen due to the limitations imposed by manual sleep-stage scoring methods. In this study, a novel machine learning model is developed using dual-channel unipolar electroencephalogram (EEG), chin electromyogram (EMG), and dual-channel electrooculgram (EOG) signals. Using an optimum orthogonal filter bank, sub-bands are obtained by decomposing 30 s epochs of signals. Tsallis entropies are then calculated from the coefficients of these sub-bands. Then, these features are fed an ensemble bagged tree (EBT) classifier for automated sleep classification. We developed our automated sleep classification model using the Sleep Heart Health Study (SHHS) database, which contains two parts, SHHS-1 and SHHS-2, containing more than 8455 subjects with more than 75,000 h of recordings. The proposed model separated three classes if sleep: rapid eye movement (REM), non-REM, and wake, with a classification accuracy of 90.70% and 91.80% using the SHHS-1 and SHHS-2 datasets, respectively. For the five-class problem, the model produces a classification accuracy of 84.3% and 86.3%, corresponding to the SHHS-1 and SHHS-2 databases, respectively, to classify wake, N1, N2, N3, and REM sleep stages. The model acquired Cohen’s kappa (κ) coefficients as 0.838 with SHHS-1 and 0.86 with SHHS-2 for the three-class classification problem. Similarly, the model achieved Cohen’s κ of 0.7746 for SHHS-1 and 0.8007 for SHHS-2 in five-class classification tasks. The model proposed in this study has achieved better performance than the best existing methods. Moreover, the model that has been proposed has been developed to classify sleep stages for both good sleepers as well as patients suffering from sleep disorders. Thus, the proposed wavelet Tsallis entropy-based model is robust and accurate and may help clinicians to comprehend and interpret sleep stages efficiently.
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44
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Automated classification of cyclic alternating pattern sleep phases in healthy and sleep-disordered subjects using convolutional neural network. Comput Biol Med 2022; 146:105594. [DOI: 10.1016/j.compbiomed.2022.105594] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 05/01/2022] [Accepted: 05/04/2022] [Indexed: 01/26/2023]
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45
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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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47
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Sharma M, Darji J, Thakrar M, Acharya UR. Automated identification of sleep disorders using wavelet-based features extracted from electrooculogram and electromyogram signals. Comput Biol Med 2022; 143:105224. [PMID: 35091364 DOI: 10.1016/j.compbiomed.2022.105224] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 12/20/2021] [Accepted: 01/04/2022] [Indexed: 01/20/2023]
Abstract
Sleep is imperative for a healthy life as it rejuvenates memory, cognitive performance, cell repair and eliminates waste from the muscles. Sleep-related disorders such as insomnia, narcolepsy, sleep-disordered breathing (SDB), periodic leg movement (PLM), and bruxism lead to hormonal imbalance, slower reaction time, memory problems, depression, and headaches. This adversity of sleep disorder gained the attention of many sleep researchers. To examine the reasons for sleep disorders, it is imperative to monitor and analyze the sleep of the affected patients. The conventional method of monitoring sleep and identifying the sleep disorders using polysomnographic (PSG) recording is a complicated and cumbersome task in which multiple physiological signals with multiple modalities are recorded for a long (overnight) duration. The PSG recordings are carried out in sophisticated sleep laboratories and cannot be considered suitable for real-time sleep monitoring. Thus, a simple and patient-convenient system is highly desirable to monitor and analyze the quality of sleep. We proposed an automatic detection of sleep disorders using single modal electrooculogram (EOG) and electromyogram (EMG) signals. We have used a new maximally flat multiplier-less biorthogonal filter bank for obtaining discrete wavelet transform of the signals. We computed Hjorth parameters (HOP) such as activity, mobility, and complexity from the wavelet sub-bands. Highly discriminative HOP features are fed to different machine learning classifiers to develop the model. Our results show that the developed system can classify insomnia, narcolepsy, NFLE, PLM, and REM behaviour disorder (RBD) against normal healthy subjects with an accuracy of 99.7%, 97.6%, 97.5%, 97.5%, and 98.3%, respectively using combined features from EOG and EMG signal. The proposed model has yielded an accuracy of 94.3% in classifying six classes using an ensemble bagged trees classifier (EBTC) with a 10-fold cross-validation technique. Hence, EOG and EMG-based proposed methods can be deployed in a portable home-based environment to identify the type of sleep disorders automatically.
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Affiliation(s)
- Manish Sharma
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, India.
| | - Jay Darji
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, India.
| | - Madhav Thakrar
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, India.
| | - U Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan; School of Science and Technology, Singapore University of Social Sciences, Singapore.
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Siyahjani F, Garcia Molina G, Barr S, Mushtaq F. Performance Evaluation of a Smart Bed Technology against Polysomnography. SENSORS 2022; 22:s22072605. [PMID: 35408220 PMCID: PMC9002520 DOI: 10.3390/s22072605] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 03/22/2022] [Accepted: 03/24/2022] [Indexed: 12/27/2022]
Abstract
The Sleep Number smart bed uses embedded ballistocardiography, together with network connectivity, signal processing, and machine learning, to detect heart rate (HR), breathing rate (BR), and sleep vs. wake states. This study evaluated the performance of the smart bed relative to polysomnography (PSG) in estimating epoch-by-epoch HR, BR, sleep vs. wake, mean overnight HR and BR, and summary sleep variables. Forty-five participants (aged 22–64 years; 55% women) slept one night on the smart bed with standard PSG. Smart bed data were compared to PSG by Bland–Altman analysis and Pearson correlation for epoch-by-epoch HR and epoch-by-epoch BR. Agreement in sleep vs. wake classification was quantified using Cohen’s kappa, ROC analysis, sensitivity, specificity, accuracy, and precision. Epoch-by-epoch HR and BR were highly correlated with PSG (HR: r = 0.81, |bias| = 0.23 beats/min; BR: r = 0.71, |bias| = 0.08 breaths/min), as were estimations of mean overnight HR and BR (HR: r = 0.94, |bias| = 0.15 beats/min; BR: r = 0.96, |bias| = 0.09 breaths/min). Calculated agreement for sleep vs. wake detection included kappa (prevalence and bias-adjusted) = 0.74 ± 0.11, AUC = 0.86, sensitivity = 0.94 ± 0.05, specificity = 0.48 ± 0.18, accuracy = 0.86 ± 0.11, and precision = 0.90 ± 0.06. For all-night summary variables, agreement was moderate to strong. Overall, the findings suggest that the Sleep Number smart bed may provide reliable metrics to unobtrusively characterize human sleep under real life-conditions.
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Islam MR, Afroj S, Beach C, Islam MH, Parraman C, Abdelkader A, Casson AJ, Novoselov KS, Karim N. Fully printed and multifunctional graphene-based wearable e-textiles for personalized healthcare applications. iScience 2022; 25:103945. [PMID: 35281734 PMCID: PMC8914337 DOI: 10.1016/j.isci.2022.103945] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/19/2022] [Accepted: 02/15/2022] [Indexed: 12/19/2022] Open
Abstract
Wearable e-textiles have gained huge tractions due to their potential for non-invasive health monitoring. However, manufacturing of multifunctional wearable e-textiles remains challenging, due to poor performance, comfortability, scalability, and cost. Here, we report a fully printed, highly conductive, flexible, and machine-washable e-textiles platform that stores energy and monitor physiological conditions including bio-signals. The approach includes highly scalable printing of graphene-based inks on a rough and flexible textile substrate, followed by a fine encapsulation to produce highly conductive machine-washable e-textiles platform. The produced e-textiles are extremely flexible, conformal, and can detect activities of various body parts. The printed in-plane supercapacitor provides an aerial capacitance of ∼3.2 mFcm-2 (stability ∼10,000 cycles). We demonstrate such e-textiles to record brain activity (an electroencephalogram, EEG) and find comparable to conventional rigid electrodes. This could potentially lead to a multifunctional garment of graphene-based e-textiles that can act as flexible and wearable sensors powered by the energy stored in graphene-based textile supercapacitors.
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Affiliation(s)
- Md Rashedul Islam
- Centre for Print Research (CFPR), University of the West of England, Frenchay, Bristol BS16 1QY, UK
| | - Shaila Afroj
- Centre for Print Research (CFPR), University of the West of England, Frenchay, Bristol BS16 1QY, UK
| | - Christopher Beach
- Department of EEE, University of Manchester, Oxford Road, Manchester M13 9PL, UK
| | - Mohammad Hamidul Islam
- Centre for Print Research (CFPR), University of the West of England, Frenchay, Bristol BS16 1QY, UK
| | - Carinna Parraman
- Centre for Print Research (CFPR), University of the West of England, Frenchay, Bristol BS16 1QY, UK
| | - Amr Abdelkader
- Department of Design and Engineering, Bournemouth University, Dorset, BH12 5BB UK
| | - Alexander J. Casson
- Department of EEE, University of Manchester, Oxford Road, Manchester M13 9PL, UK
| | - Kostya S. Novoselov
- Department of Materials Science and Engineering, National University of Singapore, Singapore, Singapore
- Institute for Functional Intelligent Materials, National University of Singapore, Singapore 117575, Singapore
- Chongqing 2D Materials Institute, Liangjiang New Area, Chongqing 400714 China
| | - Nazmul Karim
- Centre for Print Research (CFPR), University of the West of England, Frenchay, Bristol BS16 1QY, UK
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50
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Classification of Sleep Stage with Biosignal Images Using Convolutional Neural Networks. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12063028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Clinicians and researchers divide sleep periods into different sleep stages to analyze the quality of sleep. Despite advances in machine learning, sleep-stage classification is still performed manually. The classification process is tedious and time-consuming, but its automation has not yet been achieved. Another problem is low accuracy due to inconsistencies between somnologists. In this paper, we propose a method to classify sleep stages using a convolutional neural network. The network is trained with EEG and EOG images of time and frequency domains. The images of the biosignal are appropriate as inputs to the network, as these are natural inputs provided to somnologists in polysomnography. To validate the network, the sleep-stage classifier was trained and tested using the public Sleep-EDFx dataset. The results show that the proposed method achieves state-of-the-art performance on the Sleep-EDFx (accuracy 94%, F1 94%). The results demonstrate that the classifier is able to learn features described in the sleep scoring manual from the sleep data.
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