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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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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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Jin Z, Jia K. A temporal multi-scale hybrid attention network for sleep stage classification. Med Biol Eng Comput 2023; 61:2291-2303. [PMID: 36997808 DOI: 10.1007/s11517-023-02808-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 02/13/2023] [Indexed: 04/01/2023]
Abstract
Sleep is crucial for human health. Automatic sleep stage classification based on polysomnogram (PSG) is meaningful for the diagnosis of sleep disorders, which has attracted extensive attention in recent years. Most existing methods could not fully consider the different transitions of sleep stages and fit the visual inspection of sleep experts simultaneously. To this end, we propose a temporal multi-scale hybrid attention network, namely TMHAN, to automatically achieve sleep staging. The temporal multi-scale mechanism incorporates short-term abrupt and long-term periodic transitions of the successive PSG epochs. Furthermore, the hybrid attention mechanism includes 1-D local attention, 2-D global attention, and 2-D contextual sparse multi-head self-attention for three kinds of sequence-level representations. The concatenated representation is subsequently fed into a softmax layer to train an end-to-end model. Experimental results on two benchmark sleep datasets show that TMHAN obtains the best performance compared with several baselines, demonstrating the effectiveness of our model. In general, our work not only provides good classification performance, but also fits the actual sleep staging processes, which makes contribution for the combination of deep learning and sleep medicine.
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Affiliation(s)
- Zheng Jin
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
- Beijing Laboratory of Advanced Information Networks, Beijing, 100124, China
| | - Kebin Jia
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.
- Beijing Laboratory of Advanced Information Networks, Beijing, 100124, China.
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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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Kourtidou-Papadeli C, Nday CM, Samara M, Frantzidis CA. Editorial: Assessing sleep neuroplasticity in pathological conditions and in extreme environments through neurophysiological and multi-faceted daily lifestyle patterns. Front Neurosci 2023; 17:1216794. [PMID: 37284661 PMCID: PMC10240046 DOI: 10.3389/fnins.2023.1216794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 05/09/2023] [Indexed: 06/08/2023] Open
Affiliation(s)
- Chrysoula Kourtidou-Papadeli
- Laboratory of Medical Physics and Digital Innovation, Biomedical Engineering and Aerospace Neuroscience (BEAN), School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Greek Aerospace Medical Association and Space Research (GASMA-SR), Thessaloniki, Greece
- Aeromedical Center of Thessaloniki (AeMC), Thessaloniki, Greece
| | - Christiane M. Nday
- Laboratory of Medical Physics and Digital Innovation, Biomedical Engineering and Aerospace Neuroscience (BEAN), School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Myrto Samara
- Department of Psychiatry, Faculty of Medicine, University of Thessaly, Larisa, Greece
| | - Christos A. Frantzidis
- Laboratory of Medical Physics and Digital Innovation, Biomedical Engineering and Aerospace Neuroscience (BEAN), School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Greek Aerospace Medical Association and Space Research (GASMA-SR), Thessaloniki, Greece
- School of Computer Science, University of Lincoln, Lincoln, United Kingdom
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Chen C, Meng J, Belkacem AN, Lu L, Liu F, Yi W, Li P, Liang J, Huang Z, Ming D. Hierarchical fusion detection algorithm for sleep spindle detection. Front Neurosci 2023; 17:1105696. [PMID: 36968486 PMCID: PMC10035334 DOI: 10.3389/fnins.2023.1105696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 02/07/2023] [Indexed: 03/11/2023] Open
Abstract
BackgroundSleep spindles are a vital sign implying that human beings have entered the second stage of sleep. In addition, they can effectively reflect a person’s learning and memory ability, and clinical research has shown that their quantity and density are crucial markers of brain function. The “gold standard” of spindle detection is based on expert experience; however, the detection cost is high, and the detection time is long. Additionally, the accuracy of detection is influenced by subjectivity.MethodsTo improve detection accuracy and speed, reduce the cost, and improve efficiency, this paper proposes a layered spindle detection algorithm. The first layer used the Morlet wavelet and RMS method to detect spindles, and the second layer employed an improved k-means algorithm to improve spindle detection efficiency. The fusion algorithm was compared with other spindle detection algorithms to prove its effectiveness.ResultsThe hierarchical fusion spindle detection algorithm showed good performance stability, and the fluctuation range of detection accuracy was minimal. The average value of precision was 91.6%, at least five percentage points higher than other methods. The average value of recall could reach 89.1%, and the average value of specificity was close to 95%. The mean values of accuracy and F1-score in the subject sample data were 90.4 and 90.3%, respectively. Compared with other methods, the method proposed in this paper achieved significant improvement in terms of precision, recall, specificity, accuracy, and F1-score.ConclusionA spindle detection method with high steady-state accuracy and fast detection speed is proposed, which combines the Morlet wavelet with window RMS and an improved k-means algorithm. This method provides a powerful tool for the automatic detection of spindles and improves the efficiency of spindle detection. Through simulation experiments, the sampled data were analyzed and verified to prove the feasibility and effectiveness of this method.
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Affiliation(s)
- Chao Chen
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Key Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin, China
| | - Jiayuan Meng
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Abdelkader Nasreddine Belkacem
- Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Lin Lu
- Zhonghuan Information College Tianjin University of Technology, Tianjin, China
| | - Fengyue Liu
- Key Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin, China
| | - Weibo Yi
- Beijing Machine and Equipment Institute, Beijing, China
| | - Penghai Li
- Key Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin, China
| | - Jun Liang
- Department of Rehabilitation, Tianjin Medical University General Hospital, Tianjin, China
| | - Zhaoyang Huang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neuromodulation, Beijing, China
- *Correspondence: Zhaoyang Huang,
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Dong Ming,
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Abdulla S, Diykh M, Siuly S, Ali M. An intelligent model involving multi-channels spectrum patterns based features for automatic sleep stage classification. Int J Med Inform 2023; 171:105001. [PMID: 36708665 DOI: 10.1016/j.ijmedinf.2023.105001] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 01/05/2023] [Accepted: 01/14/2023] [Indexed: 01/21/2023]
Abstract
Effective sleep monitoring from electroencephalogram (EEG) signals is meaningful for the diagnosis of sleep disorders, such as sleep Apnea, Insomnia, Snoring, Sleep Hypoventilation, and restless legs syndrome. Hence, developing an automatic sleep stage scoring method based on EEGs has attracted extensive research attention in recent years. The existing methods of sleep stage classification are insufficient to investigate waveform patterns, texture patterns, and temporal transformation of EEG signals, which are most associated with sleep stages scoring. To address these issues, we proposed an intelligence model based on multi-channels texture colour analysis to automatically classify sleep staging. In the proposed model, a short-time Fourier transform is applied to each EEG 30 s segment to convert it into an image form. Then the resulted spectrum image is analysed using Multiple channels Information Local Binary Pattern (MILBP). The extracted information using MILBP is then deployed to differentiate EEG sleep stages. The extracted features are tested, and the most effective ones are used to the represented EEG sleep stages. The selected characteristics are fed to an ensemble classifier integrated with a genetic algorithm which is used to select the optimal weight for each classifier, to classify EEG signal into designated sleep stages. The experimental results on two benchmark sleep datasets showed that the proposed model obtained the best performance compared with several baseline methods, including accuracy of 0.96 and 0.95, and F1-score of 0.94 and 0.93, thus demonstrating the effectiveness of our proposed model.
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Affiliation(s)
- Shahab Abdulla
- UinSQ College, University of Southern Queensland, QLD, Australia; Information and Communication Technology Research Group, Scientific Research Centre, Al-Ayen University, Iraq.
| | - Mohammed Diykh
- University of Thi-Qar, College of Education for Pure Science, Iraq; Information and Communication Technology Research Group, Scientific Research Centre, Al-Ayen University, Iraq.
| | - Siuly Siuly
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Australia.
| | - Mumtaz Ali
- UinSQ College, University of Southern Queensland, QLD, Australia.
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8
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Chen D, Li X, Li S. A Novel Convolutional Neural Network Model Based on Beetle Antennae Search Optimization Algorithm for Computerized Tomography Diagnosis. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:1418-1429. [PMID: 34460391 DOI: 10.1109/tnnls.2021.3105384] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Convolutional neural networks (CNNs) are widely used in the field of medical imaging diagnosis but have the disadvantages of slow training speed and low diagnostic accuracy due to the initialization of parameters before training. In this article, a CNN optimization method based on the beetle antennae search (BAS) optimization algorithm is proposed. The method optimizes the initial parameters of the CNN through the BAS optimization algorithm. Based on this optimization approach, a novel CNN model with a pretrained BAS optimization algorithm was developed and applied to the analysis and diagnosis of medical imaging data for intracranial hemorrhage. Experimental results on 330 test images show that the proposed method has a better diagnostic performance than the traditional CNN. The proposed method achieves a diagnostic accuracy of 93.9394% and 100% recall, and the diagnosis of 66 human head computerized tomography image data only takes 0.1596 s. Moreover, the proposed method has more advantages than the three other optimization algorithms.
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9
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Wang Q, Guo Y, Shen Y, Tong S, Guo H. Multi-Layer Graph Attention Network for Sleep Stage Classification Based on EEG. SENSORS (BASEL, SWITZERLAND) 2022; 22:9272. [PMID: 36501974 PMCID: PMC9735886 DOI: 10.3390/s22239272] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 11/24/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
Graph neural networks have been successfully applied to sleep stage classification, but there are still challenges: (1) How to effectively utilize epoch information of EEG-adjacent channels owing to their different interaction effects. (2) How to extract the most representative features according to confused transitional information in confused stages. (3) How to improve classification accuracy of sleep stages compared with existing models. To address these shortcomings, we propose a multi-layer graph attention network (MGANet). Node-level attention prompts the graph attention convolution and GRU to focus on and differentiate the interaction between channels in the time-frequency domain and the spatial domain, respectively. The multi-head spatial-temporal mechanism balances the channel weights and dynamically adjusts channel features, and a multi-layer graph attention network accurately expresses the spatial sleep information. Moreover, stage-level attention is applied to easily confused sleep stages, which effectively improves the limitations of a graph convolutional network in large-scale graph sleep stages. The experimental results demonstrated classification accuracy; MF1 and Kappa reached 0.825, 0.814, and 0.775 and 0.873, 0.801, and 0.827 for the ISRUC and SHHS datasets, respectively, which showed that MGANet outperformed the state-of-the-art baselines.
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Affiliation(s)
| | - Yecai Guo
- Correspondence: ; Tel.: +86-151-8980-2968
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10
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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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11
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Zhuang L, Dai M, Zhou Y, Sun L. Intelligent automatic sleep staging model based on CNN and LSTM. Front Public Health 2022; 10:946833. [PMID: 35968483 PMCID: PMC9364961 DOI: 10.3389/fpubh.2022.946833] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 06/22/2022] [Indexed: 01/10/2023] Open
Abstract
Since electroencephalogram (EEG) is a significant basis to treat and diagnose somnipathy, sleep electroencephalogram automatic staging methods play important role in the treatment and diagnosis of sleep disorders. Due to the characteristics of weak signals, EEG needs accurate and efficient algorithms to extract feature information before applying it in the sleep stages. Conventional feature extraction methods have low efficiency and are difficult to meet the time validity of fast staging. In addition, it can easily lead to the omission of key features owing to insufficient a priori knowledge. Deep learning networks, such as convolutional neural networks (CNNs), have powerful processing capabilities in data analysis and data mining. In this study, a deep learning network is introduced into the study of the sleep stage. In this study, the feature fusion method is presented, and long-term and short-term memory (LSTM) is selected as the classification network to improve the accuracy of sleep stage recognition. First, based on EEG and deep learning network, an automatic sleep phase method based on a multi-channel EGG is proposed. Second, CNN-LSTM is used to monitor EEG and EOG samples during sleep. In addition, without any signal preprocessing or feature extraction, data expansion (DA) can be realized for unbalanced data, and special data and non-general data can be deleted. Finally, the MIT-BIH dataset is used to train and evaluate the proposed model. The experimental results show that the EEG-based sleep phase method proposed in this paper provides an effective method for the diagnosis and treatment of sleep disorders, and hence has a practical application value.
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Affiliation(s)
- Lan Zhuang
- Staff Hospital, Central South University, Changsha, China
| | - Minhui Dai
- Teaching and Research Section of Clinical Nursing, Xiangya Hospital of Central South University, Changsha, China
- Department of Ophthalmology, Xiangya Hospital, Central South University, Changsha, China
| | - Yi Zhou
- Department of Ophthalmology, Xiangya Hospital, Central South University, Changsha, China
| | - Lingyu Sun
- Teaching and Research Section of Clinical Nursing, Xiangya Hospital of Central South University, Changsha, China
- Department of Ophthalmology, Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Lingyu Sun
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Petmezas G, Stefanopoulos L, Kilintzis V, Tzavelis A, Rogers JA, Katsaggelos AK, Maglaveras N. State-of-the-art Deep Learning Methods on Electrocardiogram Data: A Systematic Review (Preprint). JMIR Med Inform 2022; 10:e38454. [PMID: 35969441 PMCID: PMC9425174 DOI: 10.2196/38454] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 06/03/2022] [Accepted: 07/03/2022] [Indexed: 11/13/2022] Open
Abstract
Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals. Objective This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications. Methods The PubMed search engine was systematically searched by combining “deep learning” and keywords such as “ecg,” “ekg,” “electrocardiogram,” “electrocardiography,” and “electrocardiology.” Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches. Results We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models. Conclusions We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.
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Affiliation(s)
- Georgios Petmezas
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Leandros Stefanopoulos
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vassilis Kilintzis
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Andreas Tzavelis
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States
| | - John A Rogers
- Department of Material Science, Northwestern University, Evanston, IL, United States
| | - Aggelos K Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
| | - Nicos Maglaveras
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
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13
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Adversarial learning for semi-supervised pediatric sleep staging with single-EEG channel. Methods 2022; 204:84-91. [DOI: 10.1016/j.ymeth.2022.03.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 03/12/2022] [Accepted: 03/21/2022] [Indexed: 11/18/2022] Open
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14
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Nday CM, Frantzidis CA, Plomariti C, Gilou SC, Ntakakis G, Jackson G, Chatziioannidis L, Bamidis PD, Kourtidou-Papadeli C. Human blood adenosine biomarkers and non-rapid eye movement sleep stage 3 (NREM3) cortical functional connectivity associations during a 30-day head-down-tilt bed rest analogue: Potential effectiveness of a reactive sledge jump as a countermeasure. J Sleep Res 2021; 30:e13323. [PMID: 33829595 DOI: 10.1111/jsr.13323] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 01/31/2021] [Accepted: 02/10/2021] [Indexed: 11/30/2022]
Abstract
We investigated the alterations of sleep regulation and promotion biomarkers as adenosine through its enzymes total adenosine deaminase (tADA)/adenosine deaminase (ADA2) in a microgravity analogue environment of head-down-tilt bed rest and their association with brain connectivity networks during non-rapid eye movement sleep stage 3 (NREM3), as well as the effectiveness of the reactive sledge (RSL) jump countermeasure to promote sleep. A total of 23 healthy male volunteers were maintained in 6° head-down-tilt position for 30 days and assigned either to a control or to a RSL group. Blood collection and polysomnographic recordings were performed on data acquisition day 1, 14, 30 and -14, 21, respectively. Immunochemical techniques and network-based statistics were employed for adenosine enzymes and cortical connectivity estimation. Our findings indicate that human blood adenosine biomarkers as well as NREM3 cortical functional connectivity are impaired in simulated microgravity. RSL physical activity intervened in sleep quality via tADA/ADA2 fluctuations lack, minor cortical connectivity increases, and limited degree of node and resting-state networks. Statistically significant decreases in adenosine biomarkers and NREM3 functional connectivity involving regions (left superior temporal gyrus, right postcentral gyrus, precuneus, left middle frontal gyrus, left postcentral gyrus, left angular gyrus and precuneus) of the auditory, sensorimotor default-mode and executive networks highlight the sleep disturbances due to simulated microgravity and the sleep-promoting role of RSL countermeasure. The head-down-tilt environment led to sleep deterioration projected through NREM3 cortical brain connectivity or/and adenosine biomarkers shift. This decline was more pronounced in the absence of the RSL countermeasure, thereby highlighting its likely exploitation during space missions.
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Affiliation(s)
- Christiane M Nday
- Laboratory of Medical Physics, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Christos A Frantzidis
- Laboratory of Medical Physics, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.,Greek Aerospace Medical Association and Space Research (GASMA-SR), Thessaloniki, Greece
| | - Christina Plomariti
- Laboratory of Medical Physics, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Sotiria C Gilou
- Laboratory of Medical Physics, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Giorgos Ntakakis
- Laboratory of Medical Physics, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Graham Jackson
- Department of Chemistry, University of Cape Town, Cape Town, South Africa
| | | | - Panagiotis D Bamidis
- Laboratory of Medical Physics, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.,Greek Aerospace Medical Association and Space Research (GASMA-SR), Thessaloniki, Greece
| | - Chrysoula Kourtidou-Papadeli
- Laboratory of Medical Physics, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.,Greek Aerospace Medical Association and Space Research (GASMA-SR), Thessaloniki, Greece
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Zhang J, Wu Y. Competition convolutional neural network for sleep stage classification. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102318] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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16
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Muhammad K, Khan S, Ser JD, Albuquerque VHCD. Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021; 32:507-522. [PMID: 32603291 DOI: 10.1109/tnnls.2020.2995800] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Brain tumor is one of the most dangerous cancers in people of all ages, and its grade recognition is a challenging problem for radiologists in health monitoring and automated diagnosis. Recently, numerous methods based on deep learning have been presented in the literature for brain tumor classification (BTC) in order to assist radiologists for a better diagnostic analysis. In this overview, we present an in-depth review of the surveys published so far and recent deep learning-based methods for BTC. Our survey covers the main steps of deep learning-based BTC methods, including preprocessing, features extraction, and classification, along with their achievements and limitations. We also investigate the state-of-the-art convolutional neural network models for BTC by performing extensive experiments using transfer learning with and without data augmentation. Furthermore, this overview describes available benchmark data sets used for the evaluation of BTC. Finally, this survey does not only look into the past literature on the topic but also steps on it to delve into the future of this area and enumerates some research directions that should be followed in the future, especially for personalized and smart healthcare.
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Gong S, Xing K, Cichocki A, Li J. Deep Learning in EEG: Advance of the Last Ten-Year Critical Period. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2021.3079712] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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18
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Wang H, Lin G, Li Y, Zhang X, Xu W, Wang X, Han D. Automatic Sleep Stage Classification of Children with Sleep-Disordered Breathing Using the Modularized Network. Nat Sci Sleep 2021; 13:2101-2112. [PMID: 34876865 PMCID: PMC8643215 DOI: 10.2147/nss.s336344] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 10/12/2021] [Indexed: 12/05/2022] Open
Abstract
PURPOSE To develop an automatic sleep stage analysis model for children and evaluate the effect of the model on the diagnosis of sleep-disordered breathing (SDB). PATIENTS AND METHODS Three hundred and forty-four SDB patients aged between 2 to 18 years who completed polysomnography (PSG) to assess the severity of the disease were enrolled in this study. We developed deep neural networks to stage sleep from electroencephalography (EEG), electrooculography (EOG) and electromyogram (EMG). The model performance was estimated by accuracy, precision, recall, F1-score, and Cohen's Kappa coefficient (ĸ). And we compared the difference in calculation of sleep parameters among the technicians, the model ensemble, and the single-channel EEG model. RESULTS The numbers of raw data divided into training, validation, and testing were 240, 36, and 68, respectively. The best performance appeared in the model ensemble of which the accuracy was 83.36% (ĸ=0.7817) in 5-stages, and the accuracy was 96.76% (ĸ=0.8236) in 2-stages. The single-channel EEG model showed the classification satisfyingly as well. There was no significant difference in TST, SE, SOL, time in W, time in N1+N2, time in N3, and OAHI between technician and the model (P>0.05). On the datasets from sleep-EDF-13 and sleep-EDF-18, the average classification accuracies achieved were 92.76% and 91.94% in 5-stages by using the proposed method, respectively. CONCLUSION This research established the model for pediatric automatic sleep stage classification with satisfying reliability and generalizability. In addition, it could be applied for calculating quantitative sleep parameters and evaluating the severity of SDB.
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Affiliation(s)
- Huijun Wang
- Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People's Republic of China.,Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, People's Republic of China.,Key Laboratory of Otolaryngology-Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, People's Republic of China
| | - Guodong Lin
- Department of Electronic Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, People's Republic of China
| | - Yanru Li
- Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People's Republic of China.,Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, People's Republic of China.,Key Laboratory of Otolaryngology-Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, People's Republic of China
| | - Xiaoqing Zhang
- Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People's Republic of China.,Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, People's Republic of China.,Key Laboratory of Otolaryngology-Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, People's Republic of China
| | - Wen Xu
- Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People's Republic of China.,Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, People's Republic of China.,Key Laboratory of Otolaryngology-Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, People's Republic of China
| | - Xingjun Wang
- Department of Electronic Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, People's Republic of China
| | - Demin Han
- Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People's Republic of China.,Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, People's Republic of China.,Key Laboratory of Otolaryngology-Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, People's Republic of China
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Segato A, Marzullo A, Calimeri F, De Momi E. Artificial intelligence for brain diseases: A systematic review. APL Bioeng 2020; 4:041503. [PMID: 33094213 PMCID: PMC7556883 DOI: 10.1063/5.0011697] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 09/09/2020] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for analyzing complex medical data and extracting meaningful relationships in datasets, for several clinical aims. Specifically, in the brain care domain, several innovative approaches have achieved remarkable results and open new perspectives in terms of diagnosis, planning, and outcome prediction. In this work, we present an overview of different artificial intelligent techniques used in the brain care domain, along with a review of important clinical applications. A systematic and careful literature search in major databases such as Pubmed, Scopus, and Web of Science was carried out using "artificial intelligence" and "brain" as main keywords. Further references were integrated by cross-referencing from key articles. 155 studies out of 2696 were identified, which actually made use of AI algorithms for different purposes (diagnosis, surgical treatment, intra-operative assistance, and postoperative assessment). Artificial neural networks have risen to prominent positions among the most widely used analytical tools. Classic machine learning approaches such as support vector machine and random forest are still widely used. Task-specific algorithms are designed for solving specific problems. Brain images are one of the most used data types. AI has the possibility to improve clinicians' decision-making ability in neuroscience applications. However, major issues still need to be addressed for a better practical use of AI in the brain. To this aim, it is important to both gather comprehensive data and build explainable AI algorithms.
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Affiliation(s)
- Alice Segato
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy
| | - Aldo Marzullo
- Department of Mathematics and Computer Science, University of Calabria, Rende 87036, Italy
| | - Francesco Calimeri
- Department of Mathematics and Computer Science, University of Calabria, Rende 87036, Italy
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy
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