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Shao Y, Huang B, Du L, Wang P, Li Z, Liu Z, Zhou L, Song Y, Chen X, Fang Z. Reliable automatic sleep stage classification based on hybrid intelligence. Comput Biol Med 2024; 173:108314. [PMID: 38513392 DOI: 10.1016/j.compbiomed.2024.108314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 02/10/2024] [Accepted: 03/12/2024] [Indexed: 03/23/2024]
Abstract
Sleep staging is a vital aspect of sleep assessment, serving as a critical tool for evaluating the quality of sleep and identifying sleep disorders. Manual sleep staging is a laborious process, while automatic sleep staging is seldom utilized in clinical practice due to issues related to the inadequate accuracy and interpretability of classification results in automatic sleep staging models. In this work, a hybrid intelligent model is presented for automatic sleep staging, which integrates data intelligence and knowledge intelligence, to attain a balance between accuracy, interpretability, and generalizability in the sleep stage classification. Specifically, it is built on any combination of typical electroencephalography (EEG) and electrooculography (EOG) channels, including a temporal fully convolutional network based on the U-Net architecture and a multi-task feature mapping structure. The experimental results show that, compared to current interpretable automatic sleep staging models, our model achieves a Macro-F1 score of 0.804 on the ISRUC dataset and 0.780 on the Sleep-EDFx dataset. Moreover, we use knowledge intelligence to address issues of excessive jumps and unreasonable sleep stage transitions in the coarse sleep graphs obtained by the model. We also explore the different ways knowledge intelligence affects coarse sleep graphs by combining different sleep graph correction methods. Our research can offer convenient support for sleep physicians, indicating its significant potential in improving the efficiency of clinical sleep staging.
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Affiliation(s)
- Yizi Shao
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
| | - Bokai Huang
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
| | - Lidong Du
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Personalized Management of Chronic Respiratory Disease, Chinese Academy of Medical Sciences, Beijing, China.
| | - Peng Wang
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Personalized Management of Chronic Respiratory Disease, Chinese Academy of Medical Sciences, Beijing, China.
| | - Zhenfeng Li
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Personalized Management of Chronic Respiratory Disease, Chinese Academy of Medical Sciences, Beijing, China.
| | - Zhe Liu
- Hunan VentMed Medical Technology Co., Ltd, Shaoyang, China.
| | - Lei Zhou
- Qingpu Branch of Zhongshan Hospital, Fudan University, Shanghai, China.
| | - Yuanlin Song
- Zhongshan Hospital Fudan University, Shanghai, China.
| | - Xianxiang Chen
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Personalized Management of Chronic Respiratory Disease, Chinese Academy of Medical Sciences, Beijing, China.
| | - Zhen Fang
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Personalized Management of Chronic Respiratory Disease, Chinese Academy of Medical Sciences, Beijing, China.
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Camargo-Marín L, Guzmán-Huerta M, Piña-Ramirez O, Perez-Gonzalez J. Multimodal Early Birth Weight Prediction Using Multiple Kernel Learning. Sensors (Basel) 2023; 24:2. [PMID: 38202864 PMCID: PMC10780741 DOI: 10.3390/s24010002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 12/08/2023] [Accepted: 12/14/2023] [Indexed: 01/12/2024]
Abstract
In this work, a novel multimodal learning approach for early prediction of birth weight is presented. Fetal weight is one of the most relevant indicators in the assessment of fetal health status. The aim is to predict early birth weight using multimodal maternal-fetal variables from the first trimester of gestation (Anthropometric data, as well as metrics obtained from Fetal Biometry, Doppler and Maternal Ultrasound). The proposed methodology starts with the optimal selection of a subset of multimodal features using an ensemble-based approach of feature selectors. Subsequently, the selected variables feed the nonparametric Multiple Kernel Learning regression algorithm. At this stage, a set of kernels is selected and weighted to maximize performance in birth weight prediction. The proposed methodology is validated and compared with other computational learning algorithms reported in the state of the art. The obtained results (absolute error of 234 g) suggest that the proposed methodology can be useful as a tool for the early evaluation and monitoring of fetal health status through indicators such as birth weight.
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Affiliation(s)
- Lisbeth Camargo-Marín
- Departamento de Medicina Traslacional, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Montes Urales 800, Lomas de Virreyes, Miguel Hidalgo, Mexico City 11000, Mexico; (L.C.-M.); (M.G.-H.)
| | - Mario Guzmán-Huerta
- Departamento de Medicina Traslacional, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Montes Urales 800, Lomas de Virreyes, Miguel Hidalgo, Mexico City 11000, Mexico; (L.C.-M.); (M.G.-H.)
| | - Omar Piña-Ramirez
- Departamento de Bioinformática y Análisis Estadístico, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Montes Urales 800, Lomas de Virreyes, Miguel Hidalgo, Mexico City 11000, Mexico;
| | - Jorge Perez-Gonzalez
- Unidad Académica del Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Km 4.5 Carretera Mérida-Tetiz, Municipio de Ucú, Yucatán 97357, Mexico
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Abbasi SF, Abbas A, Ahmad I, Alshehri MS, Almakdi S, Ghadi YY, Ahmad J. Automatic neonatal sleep stage classification: A comparative study. Heliyon 2023; 9:e22195. [PMID: 38058619 PMCID: PMC10695968 DOI: 10.1016/j.heliyon.2023.e22195] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 09/21/2023] [Accepted: 11/06/2023] [Indexed: 12/08/2023] Open
Abstract
Sleep is an essential feature of living beings. For neonates, it is vital for their mental and physical development. Sleep stage cycling is an important parameter to assess neonatal brain and physical development. Therefore, it is crucial to administer newborn's sleep in the neonatal intensive care unit (NICU). Currently, Polysomnography (PSG) is used as a gold standard method for classifying neonatal sleep patterns, but it is expensive and requires a lot of human involvement. Over the last two decades, multiple researchers are working on automatic sleep stage classification algorithms using electroencephalography (EEG), electrocardiography (ECG), and video. In this study, we present a comprehensive review of existing algorithms for neonatal sleep, their limitations and future recommendations. Additionally, a brief comparison of the extracted features, classification algorithms and evaluation parameters is reported in the proposed study.
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Affiliation(s)
- Saadullah Farooq Abbasi
- Department of Electronic, Electrical and System Engineering, University of Birmingham, Birmingham, United Kingdom
| | - Awais Abbas
- Department of Electronic, Electrical and System Engineering, University of Birmingham, Birmingham, United Kingdom
| | - Iftikhar Ahmad
- James Watt School of Engineering, University of Glasgow, United Kingdom
| | - Mohammed S. Alshehri
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
| | - Sultan Almakdi
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
| | - Yazeed Yasin Ghadi
- Department of Computer Science, Al Ain University, Abu Dhabi P.O. Box 112612, United Arab Emirates
| | - Jawad Ahmad
- School of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Dakhale BJ, Sharma M, Arif M, Asthana K, Bhurane AA, Kothari AG, Rajendra Acharya U. An automatic sleep-scoring system in elderly women with osteoporosis fractures using frequency localized finite orthogonal quadrature Fejer Korovkin kernels. Med Eng Phys 2023; 112:103956. [PMID: 36842776 DOI: 10.1016/j.medengphy.2023.103956] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 01/04/2023] [Accepted: 02/04/2023] [Indexed: 02/10/2023]
Abstract
Healthy sleep signifies a good physical and mental state of the body. However, factors such as inappropriate work schedules, medical complications, and others can make it difficult to get enough sleep, leading to various sleep disorders. The identification of these disorders requires sleep stage classification. Visual evaluation of sleep stages is time intensive, placing a significant strain on sleep experts and prone to human errors. As a result, it is crucial to develop machine learning algorithms to score sleep stages to acquire an accurate diagnosis. Hence, a new methodology for automated sleep stage classification is suggested using machine learning and filtering electroencephalogram (EEG) signals. The national sleep research resource's (NSRR) study of osteoporotic fractures (SOF) dataset comprising 453 subjects' polysomnograph (PSG) data is used in this study. Only two unipolar EEG derivations C4-A1 and C3-A2 are employed individually and jointly in this work. The EEG signals are decomposed into sub-bands using a frequency-localized finite orthogonal quadrature Fejer Korovkin wavelet filter bank. The wavelet-based entropy features are extracted from sub-bands. Subsequently, extracted features are classified using machine learning techniques. Our developed model obtained the highest classification accuracy of 81.3%, using an ensembled bagged trees classifier with a 10-fold cross-validation method and Cohen's Kappa coefficient of 0.72. The proposed model is accurate, dependable, and easy to implement and can be employed as an alternative to a PSG-based system at home with minimal resources. It is also ready to be tested on other EEG data to evaluate the sleep stages of healthy and unhealthy subjects.
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Affiliation(s)
- Bharti Jogi Dakhale
- Department of Electonics and Communication, Indian Institute of Information Technology Nagpur, Maharashtra, India.
| | - Manish Sharma
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, India.
| | - Mohammad Arif
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, India.
| | - Kushagra Asthana
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, India.
| | - Ankit A Bhurane
- Department of Electronics and Communication, Visvesvaraya National Institute of Technology Nagpur, Maharashtra, India.
| | - Ashwin G Kothari
- Department of Electronics and Communication, Visvesvaraya National Institute of Technology Nagpur, Maharashtra, India.
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 639798, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan; Department of Biomedical Engineering, School of Science and Technology, Singapore 639798, Singapore.
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Kazemi A, McKeown MJ, Mirian MS. Sleep staging using semi-unsupervised clustering of EEG: Application to REM sleep behavior disorder. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Xu Z, Zhu Y, Zhao H, Guo F, Wang H, Zheng M. Sleep Stage Classification Based on Multi-Centers: Comparison Between Different Ages, Mental Health Conditions and Acquisition Devices. Nat Sci Sleep 2022; 14:995-1007. [PMID: 35637772 PMCID: PMC9148176 DOI: 10.2147/nss.s355702] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 05/16/2022] [Indexed: 11/23/2022] Open
Abstract
PURPOSE To investigate the general sleep stage classification performance of deep learning networks, three datasets, across different age groups, mental health conditions, and acquisition devices, comprising adults (SHHS) and children without mental health conditions (CCSHS), and subjects with mental health conditions (XJ), were included in this study. METHODS A long short-term memory (LSTM) network was used to evaluate the effect of different ages, mental health conditions, and acquisition devices on the sleep stage classification performance and the general performance. RESULTS Results showed that the age and different mental health conditions may affect the sleep stage classification performance of the network. The same acquisition device using different parameters may not have an obvious effect on the classification performance. When using a single dataset and two datasets for training, the network performed better only on the training dataset. When training was conducted with three datasets, the network performed well for all datasets with a Cohen's Kappa of 0.8192 and 0.8472 for the SHHS and CCSHS, respectively, but performed relatively worse (0.6491) for the XJ, which indicated the complexity effect of different mental health conditions on the sleep stage classification task. Moreover, the performance of the network trained using three datasets was similar for each dataset to that of the network trained using a single dataset and tested on the same dataset. CONCLUSION These results suggested that when more datasets across different age groups, mental health conditions, and acquisition devices (ie, more datasets with different feature distributions for each sleep stage) are used for training, the general performance of a deep learning network will be superior for sleep stage classification tasks with varied conditions.
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Affiliation(s)
- Ziliang Xu
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, People's Republic of China
| | - Yuanqiang Zhu
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, People's Republic of China
| | - Hongliang Zhao
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, People's Republic of China
| | - Fan Guo
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, People's Republic of China
| | - Huaning Wang
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, People's Republic of China
| | - Minwen Zheng
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, People's Republic of China
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Zhu T, Luo W, Yu F. Multi-Branch Convolutional Neural Network for Automatic Sleep Stage Classification with Embedded Stage Refinement and Residual Attention Channel Fusion. Sensors (Basel) 2020; 20:s20226592. [PMID: 33218040 PMCID: PMC7698838 DOI: 10.3390/s20226592] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Revised: 11/13/2020] [Accepted: 11/17/2020] [Indexed: 01/21/2023]
Abstract
Automatic sleep stage classification of multi-channel sleep signals can help clinicians efficiently evaluate an individual's sleep quality and assist in diagnosing a possible sleep disorder. To obtain accurate sleep classification results, the processing flow of results from signal preprocessing and machine-learning-based classification is typically employed. These classification results are refined based on sleep transition rules. Neural networks-i.e., machine learning algorithms-are powerful at solving classification problems. Some methods apply them to the first two processes above; however, the refinement process continues to be based on traditional methods. In this study, the sleep stage refinement process was incorporated into the neural network model to form real end-to-end processing. In addition, for multi-channel signals, the multi-branch convolutional neural network was combined with a proposed residual attention method. This approach further improved the model classification accuracy. The proposed method was evaluated on the Sleep-EDF Expanded Database (Sleep-EDFx) and University College Dublin Sleep Apnea Database (UCDDB). It achieved respective accuracy rates of 85.7% and 79.4%. The results also showed that sleep stage refinement based on a neural network is more effective than the traditional refinement method. Moreover, the proposed residual attention method was determined to have a more robust channel-information fusion ability than the respective average and concatenation methods.
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Abstract
Developing reliable and user-friendly electroencephalography (EEG) electrodes remains a challenge for emerging real-world EEG applications. Classic wet electrodes are the gold standard for recording EEG; however, they are difficult to implement and make users uncomfortable, thus severely restricting their widespread application in real-life scenarios. An alternative is dry electrodes, which do not require conductive gels or skin preparation. Despite their quick setup and improved user-friendliness, dry electrodes still have some inherent problems (invasive, relatively poor signal quality, or sensitivity to motion artifacts), which limit their practical utilization. In recent years, semi-dry electrodes, which require only a small amount of electrolyte fluid, have been successfully developed, combining the advantages of both wet and dry electrodes while addressing their respective drawbacks. Semi-dry electrodes can collect reliable EEG signals comparable to wet electrodes. Moreover, their setup is as fast and convenient similar to that of dry electrodes. Hence, semi-dry electrodes have shown tremendous application prospects for real-world EEG acquisition. Herein, we systematically summarize the development, evaluation methods, and practical design considerations of semi-dry electrodes. Some feasible suggestions and new ideas for the development of semi-dry electrodes have been presented. This review provides valuable technical support for the development of semi-dry electrodes toward emerging practical applications.
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Affiliation(s)
- Guang-Li Li
- Hunan Key Laboratory of Biomedical Nanomaterials and Devices, College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou 412007, People's Republic of China
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Diykh M, Li Y, Abdulla S. EEG sleep stages identification based on weighted undirected complex networks. Comput Methods Programs Biomed 2020; 184:105116. [PMID: 31629158 DOI: 10.1016/j.cmpb.2019.105116] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Revised: 09/14/2019] [Accepted: 10/02/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Sleep scoring is important in sleep research because any errors in the scoring of the patient's sleep electroencephalography (EEG) recordings can cause serious problems such as incorrect diagnosis, medication errors, and misinterpretations of patient's EEG recordings. The aim of this research is to develop a new automatic method for EEG sleep stages classification based on a statistical model and weighted brain networks. METHODS Each EEG segment is partitioned into a number of blocks using a sliding window technique. A set of statistical features are extracted from each block. As a result, a vector of features is obtained to represent each EEG segment. Then, the vector of features is mapped into a weighted undirected network. Different structural and spectral attributes of the networks are extracted and forwarded to a least square support vector machine (LS-SVM) classifier. At the same time the network's attributes are also thoroughly investigated. It is found that the network's characteristics vary with their sleep stages. Each sleep stage is best represented using the key features of their networks. RESULTS In this paper, the proposed method is evaluated using two datasets acquired from different channels of EEG (Pz-Oz and C3-A2) according to the R&K and the AASM without pre-processing the original EEG data. The obtained results by the LS-SVM are compared with those by Naïve, k-nearest and a multi-class-SVM. The proposed method is also compared with other benchmark sleep stages classification methods. The comparison results demonstrate that the proposed method has an advantage in scoring sleep stages based on single channel EEG signals. CONCLUSIONS An average accuracy of 96.74% is obtained with the C3-A2 channel according to the AASM standard, and 96% with the Pz-Oz channel based on the R&K standard.
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Affiliation(s)
- Mohammed Diykh
- School of Agricultural, Computational and Environmental Sciences, University of Southern Queensland, Australia; College of Education for Pure Science, University of Thi-Qar, Iraq.
| | - Yan Li
- School of Agricultural, Computational and Environmental Sciences, University of Southern Queensland, Australia.
| | - Shahab Abdulla
- Open Access College, University of Southern Queensland, Australia.
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Jadhav P, Rajguru G, Datta D, Mukhopadhyay S. Automatic sleep stage classification using time–frequency images of CWT and transfer learning using convolution neural network. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.01.010] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Abstract
BACKGROUND Sleep is a complex and dynamic biological process characterized by different sleep patterns. Comprehensive sleep monitoring and analysis using multivariate polysomnography (PSG) records has achieved significant efforts to prevent sleep-related disorders. To alleviate the time consumption caused by manual visual inspection of PSG, automatic multivariate sleep stage classification has become an important research topic in medical and bioinformatics. RESULTS We present a unified hybrid self-attention deep learning framework, namely HybridAtt, to automatically classify sleep stages by capturing channel and temporal correlations from multivariate PSG records. We construct a new multi-view convolutional representation module to learn channel-specific and global view features from the heterogeneous PSG inputs. The hybrid attention mechanism is designed to further fuse the multi-view features by inferring their dependencies without any additional supervision. The learned attentional representation is subsequently fed through a softmax layer to train an end-to-end deep learning model. CONCLUSIONS We empirically evaluate our proposed HybridAtt model on a benchmark PSG dataset in two feature domains, referred to as the time and frequency domains. Experimental results show that HybridAtt consistently outperforms ten baseline methods in both feature spaces, demonstrating the effectiveness of HybridAtt in the task of sleep stage classification.
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Affiliation(s)
- Ye Yuan
- College of Information and Communication Engineering, Beijing University of Technology, Beijing, China.,Beijing Laboratory of Advanced Information Networks, Beijing, China.,Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China
| | - Kebin Jia
- College of Information and Communication Engineering, Beijing University of Technology, Beijing, China. .,Beijing Laboratory of Advanced Information Networks, Beijing, China. .,Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China.
| | - Fenglong Ma
- Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY, USA
| | - Guangxu Xun
- Department of Computer Science, University of Virginia, Charlottesville, NV, USA
| | - Yaqing Wang
- Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY, USA
| | - Lu Su
- Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY, USA
| | - Aidong Zhang
- Department of Computer Science, University of Virginia, Charlottesville, NV, USA
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Faust O, Razaghi H, Barika R, Ciaccio EJ, Acharya UR. A review of automated sleep stage scoring based on physiological signals for the new millennia. Comput Methods Programs Biomed 2019; 176:81-91. [PMID: 31200914 DOI: 10.1016/j.cmpb.2019.04.032] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 04/03/2019] [Accepted: 04/29/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiological signals. Prerequisite for this interpretation is an understanding of the way in which sleep stage changes manifest themselves in the signal waveform. With that understanding it is possible to build automated sleep stage scoring systems. Apart from their practical relevance for automating sleep disorder diagnosis, these systems provide a good indication of the amount of sleep stage related information communicated by a specific physiological signal. METHODS This article provides a comprehensive review of automated sleep stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals. RESULTS Our review shows that all of these signals contain information for sleep stage scoring. CONCLUSIONS The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost.
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Affiliation(s)
- Oliver Faust
- Department of Engineering and Mathematics, Sheffield Hallam University, United Kingdom.
| | - Hajar Razaghi
- Department of Engineering and Mathematics, Sheffield Hallam University, United Kingdom
| | - Ragab Barika
- Department of Engineering and Mathematics, Sheffield Hallam University, United Kingdom
| | - Edward J Ciaccio
- Department of Medicine - Cardiology, Columbia University, New York, New York, USA
| | - U Rajendra Acharya
- Department of Electronic & Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore; Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
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Yan R, Zhang C, Spruyt K, Wei L, Wang Z, Tian L, Li X, Ristaniemi T, Zhang J, Cong F. Multi-modality of polysomnography signals’ fusion for automatic sleep scoring. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.10.001] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Shokoueinejad M, Fernandez C, Carroll E, Wang F, Levin J, Rusk S, Glattard N, Mulchrone A, Zhang X, Xie A, Teodorescu M, Dempsey J, Webster J. Sleep apnea: a review of diagnostic sensors, algorithms, and therapies. Physiol Meas 2017; 38:R204-R252. [PMID: 28820743 DOI: 10.1088/1361-6579/aa6ec6] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
While public awareness of sleep related disorders is growing, sleep apnea syndrome (SAS) remains a public health and economic challenge. Over the last two decades, extensive controlled epidemiologic research has clarified the incidence, risk factors including the obesity epidemic, and global prevalence of obstructive sleep apnea (OSA), as well as establishing a growing body of literature linking OSA with cardiovascular morbidity, mortality, metabolic dysregulation, and neurocognitive impairment. The US Institute of Medicine Committee on Sleep Medicine estimates that 50-70 million US adults have sleep or wakefulness disorders. Furthermore, the American Academy of Sleep Medicine (AASM) estimates that more than 29 million US adults suffer from moderate to severe OSA, with an estimated 80% of those individuals living unaware and undiagnosed, contributing to more than $149.6 billion in healthcare and other costs in 2015. Although various devices have been used to measure physiological signals, detect apneic events, and help treat sleep apnea, significant opportunities remain to improve the quality, efficiency, and affordability of sleep apnea care. As our understanding of respiratory and neurophysiological signals and sleep apnea physiological mechanisms continues to grow, and our ability to detect and process biomedical signals improves, novel diagnostic and treatment modalities emerge. OBJECTIVE This article reviews the current engineering approaches for the detection and treatment of sleep apnea. APPROACH It discusses signal acquisition and processing, highlights the current nonsurgical and nonpharmacological treatments, and discusses potential new therapeutic approaches. MAIN RESULTS This work has led to an array of validated signal and sensor modalities for acquiring, storing and viewing sleep data; a broad class of computational and signal processing approaches to detect and classify SAS disease patterns; and a set of distinctive therapeutic technologies whose use cases span the continuum of disease severity. SIGNIFICANCE This review provides a current perspective of the classes of tools at hand, along with a sense of their relative strengths and areas for further improvement.
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Affiliation(s)
- Mehdi Shokoueinejad
- Department of Biomedical Engineering, University of Wisconsin-Madison, 1550 Engineering Drive, Madison, WI 53706-1609, United States of America. Department of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut St 707, Madison, WI 53726, United States of America. EnsoData Research, EnsoData Inc., 111 N Fairchild St, Suite 240, Madison, WI 53703, United States of America
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Parekh A, Selesnick IW, Osorio RS, Varga AW, Rapoport DM, Ayappa I. Multichannel sleep spindle detection using sparse low-rank optimization. J Neurosci Methods 2017; 288:1-16. [PMID: 28600157 DOI: 10.1016/j.jneumeth.2017.06.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Revised: 06/02/2017] [Accepted: 06/02/2017] [Indexed: 11/29/2022]
Abstract
BACKGROUND Automated single-channel spindle detectors, for human sleep EEG, are blind to the presence of spindles in other recorded channels unlike visual annotation by a human expert. NEW METHOD We propose a multichannel spindle detection method that aims to detect global and local spindle activity in human sleep EEG. Using a non-linear signal model, which assumes the input EEG to be the sum of a transient and an oscillatory component, we propose a multichannel transient separation algorithm. Consecutive overlapping blocks of the multichannel oscillatory component are assumed to be low-rank whereas the transient component is assumed to be piecewise constant with a zero baseline. The estimated oscillatory component is used in conjunction with a bandpass filter and the Teager operator for detecting sleep spindles. RESULTS AND COMPARISON WITH OTHER METHODS The proposed method is applied to two publicly available databases and compared with 7 existing single-channel automated detectors. F1 scores for the proposed spindle detection method averaged 0.66 (0.02) and 0.62 (0.06) for the two databases, respectively. For an overnight 6 channel EEG signal, the proposed algorithm takes about 4min to detect sleep spindles simultaneously across all channels with a single setting of corresponding algorithmic parameters. CONCLUSIONS The proposed method attempts to mimic and utilize, for better spindle detection, a particular human expert behavior where the decision to mark a spindle event may be subconsciously influenced by the presence of a spindle in EEG channels other than the central channel visible on a digital screen.
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Affiliation(s)
- Ankit Parekh
- Dept. of Electrical and Computer Engineering, College of Engineering, University of Iowa, United States.
| | - Ivan W Selesnick
- Dept. of Electrical and Computer Engineering, Tandon School of Engineering, New York University, United States
| | - Ricardo S Osorio
- Center for Brain Health, Department of Psychiatry, School of Medicine, New York University, United States
| | - Andrew W Varga
- Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, United States
| | - David M Rapoport
- Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, United States
| | - Indu Ayappa
- Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, United States
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Boostani R, Karimzadeh F, Nami M. A comparative review on sleep stage classification methods in patients and healthy individuals. Comput Methods Programs Biomed 2017; 140:77-91. [PMID: 28254093 DOI: 10.1016/j.cmpb.2016.12.004] [Citation(s) in RCA: 110] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2016] [Revised: 11/17/2016] [Accepted: 12/05/2016] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVE Proper scoring of sleep stages can give clinical information on diagnosing patients with sleep disorders. Since traditional visual scoring of the entire sleep is highly time-consuming and dependent to experts' experience, automatic schemes based on electroencephalogram (EEG) analysis are broadly developed to solve these problems. This review presents an overview on the most suitable methods in terms of preprocessing, feature extraction, feature selection and classifier adopted to precisely discriminate the sleep stages. METHODS This study round up a wide range of research findings concerning the application of the sleep stage classification. The fundamental qualitative methods along with the state-of-the-art quantitative techniques for sleep stage scoring are comprehensively introduced. Moreover, according to the results of the investigated studies, five research papers are chosen and practically implemented on a well-known public available sleep EEG dataset. They are applied to single-channel EEG of 40 subjects containing equal number of healthy and patient individuals. Feature extraction and classification schemes are assessed in terms of accuracy and robustness against noise. Furthermore, an additional implementation phase is added to this research in which all combinations of the implemented features and classifiers are considered to find the best combination for sleep analysis. RESULTS According to our achieved results on both groups, entropy of wavelet coefficients along with random forest classifier are chosen as the best feature and classifier, respectively. The mentioned feature and classifier provide 87.06% accuracy on healthy subjects and 69.05% on patient group. CONCLUSIONS In this paper, the road map of EEG-base sleep stage scoring methods is clearly sketched. Implementing the state-of-the-art methods and even their combination on both healthy and patient datasets indicates that although the accuracy on healthy subjects are remarkable, the results for the main community (patient group) by the quantitative methods are not promising yet. The reasons rise from adopting non-matched sleep EEG features from other signal processing fields such as communication. As a conclusion, developing sleep pattern-related features deem necessary to enhance the performance of this process.
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Affiliation(s)
- Reza Boostani
- Department of Computer Science and Information technology, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.
| | - Foroozan Karimzadeh
- Department of Computer Science and Information technology, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.
| | - Mohammad Nami
- Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran.
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