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Paul Y, Singh R, Sharma S, Singh S, Ra IH. Efficient Sleep Stage Identification Using Piecewise Linear EEG Signal Reduction: A Novel Algorithm for Sleep Disorder Diagnosis. SENSORS (BASEL, SWITZERLAND) 2024; 24:5265. [PMID: 39204960 PMCID: PMC11360799 DOI: 10.3390/s24165265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2024] [Revised: 08/05/2024] [Accepted: 08/12/2024] [Indexed: 09/04/2024]
Abstract
Sleep is a vital physiological process for human health, and accurately detecting various sleep states is crucial for diagnosing sleep disorders. This study presents a novel algorithm for identifying sleep stages using EEG signals, which is more efficient and accurate than the state-of-the-art methods. The key innovation lies in employing a piecewise linear data reduction technique called the Halfwave method in the time domain. This method simplifies EEG signals into a piecewise linear form with reduced complexity while preserving sleep stage characteristics. Then, a features vector with six statistical features is built using parameters obtained from the reduced piecewise linear function. We used the MIT-BIH Polysomnographic Database to test our proposed method, which includes more than 80 h of long data from different biomedical signals with six main sleep classes. We used different classifiers and found that the K-Nearest Neighbor classifier performs better in our proposed method. According to experimental findings, the average sensitivity, specificity, and accuracy of the proposed algorithm on the Polysomnographic Database considering eight records is estimated as 94.82%, 96.65%, and 95.73%, respectively. Furthermore, the algorithm shows promise in its computational efficiency, making it suitable for real-time applications such as sleep monitoring devices. Its robust performance across various sleep classes suggests its potential for widespread clinical adoption, making significant advances in the knowledge, detection, and management of sleep problems.
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Affiliation(s)
- Yash Paul
- Department of Information Technology, Central University of Kashmir, Ganderbal 191201, India;
| | - Rajesh Singh
- Institute of Foreign Trade, New Delhi 110016, India;
| | - Surbhi Sharma
- Department of Information Technology, National Institute of Technology, Srinagar 190006, India;
| | - Saurabh Singh
- Department of AI and Big Data, Woosong University, Seoul 34606, Republic of Korea;
| | - In-Ho Ra
- School of Computer, Information and Communication Engineering, Kunsan National University, Gunsan 54150, Republic of Korea
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McMahon M, Goldin J, Kealy ES, Wicks DJ, Zilberg E, Freeman W, Aliahmad B. Performance Investigation of Somfit Sleep Staging Algorithm. Nat Sci Sleep 2024; 16:1027-1043. [PMID: 39071546 PMCID: PMC11277903 DOI: 10.2147/nss.s463026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 07/01/2024] [Indexed: 07/30/2024] Open
Abstract
Purpose To investigate accuracy of the sleep staging algorithm in a new miniaturized home sleep monitoring device - Compumedics® Somfit. Somfit is attached to patient's forehead and combines channels specified for a pulse arterial tonometry (PAT)-based home sleep apnea testing (HSAT) device with the neurological signals. Somfit sleep staging deep learning algorithm is based on convolutional neural network architecture. Patients and Methods One hundred and ten participants referred for sleep investigation with suspected or preexisting obstructive sleep apnea (OSA) in need of a review were enrolled into the study involving simultaneous recording of full overnight polysomnography (PSG) and Somfit data. The recordings were conducted at three centers in Australia. The reported statistics include standard measures of agreement between Somfit automatic hypnogram and consensus PSG hypnogram. Results Overall percent agreement across five sleep stages (N1, N2, N3, REM, and wake) between Somfit automatic and consensus PSG hypnograms was 76.14 (SE: 0.79). The percent agreements between different pairs of sleep technologists' PSG hypnograms varied from 74.36 (1.93) to 85.50 (0.64), with interscorer agreement being greater for scorers from the same sleep laboratory. The estimate of kappa between Somfit and consensus PSG was 0.672 (0.002). Percent agreement for sleep/wake discrimination was 89.30 (0.37). The accuracy of Somfit sleep staging algorithm varied with increasing OSA severity - percent agreement was 79.67 (1.87) for the normal subjects, 77.38 (1.06) for mild OSA, 74.83 (1.79) for moderate OSA and 72.93 (1.68) for severe OSA. Conclusion Agreement between Somfit and PSG hypnograms was non-inferior to PSG interscorer agreement for a number of scorers, thus confirming acceptability of electrode placement at the center of the forehead. The directions for algorithm improvement include additional arousal detection, integration of motion and oximetry signals and separate inference models for individual sleep stages.
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Affiliation(s)
- Marcus McMahon
- Department of Respiratory and Sleep Medicine, Epworth Hospital, Richmond, Victoria, Australia and Department of Respiratory and Sleep Medicine, Austin Health, Heidelberg, Victoria, Australia
| | - Jeremy Goldin
- Department of Respiratory and Sleep Medicine, Royal Melbourne Hospital, Parkvile, Victoria, Australia
| | | | | | - Eugene Zilberg
- Medical Innovations, Compumedics Limited, Abbotsford, Victoria, Australia
| | - Warwick Freeman
- Medical Innovations, Compumedics Limited, Abbotsford, Victoria, Australia
| | - Behzad Aliahmad
- Medical Innovations, Compumedics Limited, Abbotsford, Victoria, Australia
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Satapathy SK, Brahma B, Panda B, Barsocchi P, Bhoi AK. Machine learning-empowered sleep staging classification using multi-modality signals. BMC Med Inform Decis Mak 2024; 24:119. [PMID: 38711099 DOI: 10.1186/s12911-024-02522-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 04/29/2024] [Indexed: 05/08/2024] Open
Abstract
The goal is to enhance an automated sleep staging system's performance by leveraging the diverse signals captured through multi-modal polysomnography recordings. Three modalities of PSG signals, namely electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG), were considered to obtain the optimal fusions of the PSG signals, where 63 features were extracted. These include frequency-based, time-based, statistical-based, entropy-based, and non-linear-based features. We adopted the ReliefF (ReF) feature selection algorithms to find the suitable parts for each signal and superposition of PSG signals. Twelve top features were selected while correlated with the extracted feature sets' sleep stages. The selected features were fed into the AdaBoost with Random Forest (ADB + RF) classifier to validate the chosen segments and classify the sleep stages. This study's experiments were investigated by obtaining two testing schemes: epoch-wise testing and subject-wise testing. The suggested research was conducted using three publicly available datasets: ISRUC-Sleep subgroup1 (ISRUC-SG1), sleep-EDF(S-EDF), Physio bank CAP sleep database (PB-CAPSDB), and S-EDF-78 respectively. This work demonstrated that the proposed fusion strategy overestimates the common individual usage of PSG signals.
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Affiliation(s)
- Santosh Kumar Satapathy
- Department of Information and Communication Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, 382007, India.
| | - Biswajit Brahma
- McKesson Corporation, 1 Post St, San Francisco, CA, 94104, USA
| | - Baidyanath Panda
- LTIMindtree, 1 American Row, 3Rd Floor, Hartford, CT, 06103, USA
| | - Paolo Barsocchi
- Institute of Information Science and Technologies, National Research Council, 56124, Pisa, Italy.
| | - Akash Kumar Bhoi
- Directorate of Research, Sikkim Manipal University, Gangtok, 737102, Sikkim, India.
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Li Y, Chen J, Ma W, Zhao G, Fan X. MVF-SleepNet: Multi-View Fusion Network for Sleep Stage Classification. IEEE J Biomed Health Inform 2024; 28:2485-2495. [PMID: 36129857 DOI: 10.1109/jbhi.2022.3208314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Sleep stage classification is of great importance in human health monitoring and disease diagnosing. Clinically, visual-inspected classifying sleep into different stages is quite time consuming and highly relies on the expertise of sleep specialists. Many automated models for sleep stage classification have been proposed in previous studies but their performances still exist a gap to the real clinical application. In this work, we propose a novel multi-view fusion network named MVF-SleepNet based on multi-modal physiological signals of electroencephalography (EEG), electrocardiography (ECG), electrooculography (EOG), and electromyography (EMG). To capture the relationship representation among multi-modal physiological signals, we construct two views of Time-frequency images (TF images) and Graph-learned graphs (GL graphs). To learn the spectral-temporal representation from sequentially timed TF images, the combination of VGG-16 and GRU networks is utilized. To learn the spatial-temporal representation from sequentially timed GL graphs, the combination of Chebyshev graph convolution and temporal convolution networks is employed. Fusing the spectral-temporal representation and spatial-temporal representation can further boost the performance of sleep stage classification. A large number of experiment results on the publicly available datasets of ISRUC-S1 and ISRUC-S3 show that the MVF-SleepNet achieves overall accuracy of 0.821, F1 score of 0.802 and Kappa of 0.768 on ISRUC-S1 dataset, and accuracy of 0.841, F1 score of 0.828 and Kappa of 0.795 on ISRUC-S3 dataset. The MVF-SleepNet achieves competitive results on both datasets of ISRUC-S1 and ISRUC-S3 for sleep stage classification compared to the state-of-the-art baselines. The source code of MVF-SleepNet is available on Github (https://github.com/YJPai65/MVF-SleepNet).
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Rashidi S, Asl BM. Strength of ensemble learning in automatic sleep stages classification using single-channel EEG and ECG signals. Med Biol Eng Comput 2024; 62:997-1015. [PMID: 38114690 DOI: 10.1007/s11517-023-02980-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 11/26/2023] [Indexed: 12/21/2023]
Abstract
Healthy sleep plays an essential role in human daily life. Classification of sleep stages is a crucial tool for assisting physicians in diagnosing and treating sleep disorders. In this study, a strong ensemble learning model is proposed to enhance the ability of classification models in accurate sleep staging, particularly in multi-class classification. We asserted that high-accuracy sleep classification is achievable using only single-channel electroencephalogram (EEG) and electrocardiogram (ECG) by combining their best-extractable features in the time and frequency domains we recommended. More importantly, the superiority of the recommended method, which is the simultaneous use of stacking and bagging, over conventional machine learning classifiers in sleep staging was demonstrated, using the MIT-BIH Polysomnographic and Sleep-EDF expanded databases. Finally, K-fold cross-validation was used to fairly estimate these models. The best mean test accuracy rates for distinguishing between two classes of "sleep vs. wake," "rapid vs. non-rapid eye movement," and "deep vs. light sleep," were obtained 99.93%, 99.64%, and 99.69%, respectively. Furthermore, our proposed method achieved accuracies of 97.14%, 95.18%, 92.7%, and 85.64% for separating three, four, five, and six sleep classes, respectively. Compared to recent studies, our method outperforms other sleep stage classification schemes, especially in multi-class staging.
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Affiliation(s)
- Samandokht Rashidi
- Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
| | - Babak Mohammadzadeh Asl
- Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
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Yue H, Chen Z, Guo W, Sun L, Dai Y, Wang Y, Ma W, Fan X, Wen W, Lei W. Research and application of deep learning-based sleep staging: Data, modeling, validation, and clinical practice. Sleep Med Rev 2024; 74:101897. [PMID: 38306788 DOI: 10.1016/j.smrv.2024.101897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 12/30/2023] [Accepted: 01/04/2024] [Indexed: 02/04/2024]
Abstract
Over the past few decades, researchers have attempted to simplify and accelerate the process of sleep stage classification through various approaches; however, only a few such approaches have gained widespread acceptance. Artificial intelligence technology, particularly deep learning, is promising for earning the trust of the sleep medicine community in automated sleep-staging systems, thus facilitating its application in clinical practice and integration into daily life. We aimed to comprehensively review the latest methods that are applying deep learning for enhancing sleep staging efficiency and accuracy. Starting from the requisite "data" for constructing deep learning algorithms, we elucidated the current landscape of this domain and summarized the fundamental modeling process, encompassing signal selection, data pre-processing, model architecture, classification tasks, and performance metrics. Furthermore, we reviewed the applications of automated sleep staging in scenarios such as sleep-disorder screening, diagnostic procedures, and health monitoring and management. Finally, we conducted an in-depth analysis and discussion of the challenges and future in intelligent sleep staging, particularly focusing on large-scale sleep datasets, interdisciplinary collaborations, and human-computer interactions.
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Affiliation(s)
- Huijun Yue
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Zhuqi Chen
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Wenbin Guo
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Lin Sun
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Yidan Dai
- School of Computer Science, South China Normal University, Guangzhou, People's Republic of China
| | - Yiming Wang
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Wenjun Ma
- School of Computer Science, South China Normal University, Guangzhou, People's Republic of China
| | - Xiaomao Fan
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, People's Republic of China
| | - Weiping Wen
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China; Department of Otolaryngology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.
| | - Wenbin Lei
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.
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Wei Y, Zhu Y, Zhou Y, Yu X, Luo Y. Automatic Sleep Staging Based on Contextual Scalograms and Attention Convolution Neural Network Using Single-Channel EEG. IEEE J Biomed Health Inform 2024; 28:801-811. [PMID: 37955995 DOI: 10.1109/jbhi.2023.3332503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Single-channel EEG based sleep staging is of interest to researchers due to its broad application prospect in daily sleep monitoring recently. We proposed using contextual scalograms as input and developed a convolutional neural network with attention modules named Co-ScaleNet for sleep staging. The contextual scalograms were obtained by combining the same color channels of three original RGB scalograms from consecutive epochs, and a simple and efficient data augmentation was designed according to their various forms. The Co-ScaleNet consists of two main parts. Firstly, three parallel convolutional branches with attention modules correspondingly extract and fuse features from contextual scalograms at the top layers. The remaining part is a stack of lightweight blocks. We achieved an overall accuracy of 87.0% for healthy individuals, 84.7% for depressed patients. And we obtained comparable performance on the public Sleep-EDFx (82.8%), ISRUC (84.6%) and SHHS datasets (87.7%), including a high recall of N1. The contextual scalograms of R channel as input achieved the best performance, which conform to the features of interest in visual scoring. The attention modules improved the recall of N1 and N3. Overall, the contextual scalograms provided a novel scheme for both contextual information extraction and data augmentation. Our study successfully expanded its application to depression datasets, as well as patients with sleep apnea, demonstrating its wide applicability.
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Ji X, Li Y, Wen P, Barua P, Acharya UR. MixSleepNet: A Multi-Type Convolution Combined Sleep Stage Classification Model. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107992. [PMID: 38218118 DOI: 10.1016/j.cmpb.2023.107992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 12/09/2023] [Accepted: 12/19/2023] [Indexed: 01/15/2024]
Abstract
BACKGROUND AND OBJECTIVE Sleep staging is an essential step for sleep disorder diagnosis, which is time-intensive and laborious for experts to perform this work manually. Automatic sleep stage classification methods not only alleviate experts from these demanding tasks but also enhance the accuracy and efficiency of the classification process. METHODS A novel multi-channel biosignal-based model constructed by the combination of a 3D convolutional operation and a graph convolutional operation is proposed for the automated sleep stages using various physiological signals. Both the 3D convolution and graph convolution can aggregate information from neighboring brain areas, which helps to learn intrinsic connections from the biosignals. Electroencephalogram (EEG), electromyogram (EMG), electrooculogram (EOG) and electrocardiogram (ECG) signals are employed to extract time domain and frequency domain features. Subsequently, these signals are input to the 3D convolutional and graph convolutional branches, respectively. The 3D convolution branch can explore the correlations between multi-channel signals and multi-band waves in each channel in the time series, while the graph convolution branch can explore the connections between each channel and each frequency band. In this work, we have developed the proposed multi-channel convolution combined sleep stage classification model (MixSleepNet) using ISRUC datasets (Subgroup 3 and 50 random samples from Subgroup 1). RESULTS Based on the first expert's label, our generated MixSleepNet yielded an accuracy, F1-score and Cohen kappa scores of 0.830, 0.821 and 0.782, respectively for ISRUC-S3. It obtained accuracy, F1-score and Cohen kappa scores of 0.812, 0.786, and 0.756, respectively for the ISRUC-S1 dataset. In accordance with the evaluations conducted by the second expert, the comprehensive accuracies, F1-scores, and Cohen kappa coefficients for the ISRUC-S3 and ISRUC-S1 datasets are determined to be 0.837, 0.820, 0.789, and 0.829, 0.791, 0.775, respectively. CONCLUSION The results of the performance metrics by the proposed method are much better than those from all the compared models. Additional experiments were carried out on the ISRUC-S3 sub-dataset to evaluate the contributions of each module towards the classification performance.
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Affiliation(s)
- Xiaopeng Ji
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
| | - Yan Li
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
| | - Peng Wen
- School of Engineering, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
| | - Prabal Barua
- Cogninet Brain Team, Sydney, NSW 2010, Australia.
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
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Tang Z, Wang H, Cui Z, Jin X, Zhang L, Peng Y, Xing B. An Upper-Limb Rehabilitation Exoskeleton System Controlled by MI Recognition Model With Deep Emphasized Informative Features in a VR Scene. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4390-4401. [PMID: 37910412 DOI: 10.1109/tnsre.2023.3329059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2023]
Abstract
The prevalence of stroke continues to increase with the global aging. Based on the motor imagery (MI) brain-computer interface (BCI) paradigm and virtual reality (VR) technology, we designed and developed an upper-limb rehabilitation exoskeleton system (VR-ULE) in the VR scenes for stroke patients. The VR-ULE system makes use of the MI electroencephalogram (EEG) recognition model with a convolutional neural network and squeeze-and-excitation (SE) blocks to obtain the patient's motion intentions and control the exoskeleton to move during rehabilitation training movement. Due to the individual differences in EEG, the frequency bands with optimal MI EEG features for each patient are different. Therefore, the weight of different feature channels is learned by combining SE blocks to emphasize the useful information frequency band features. The MI cues in the VR-based virtual scenes can improve the interhemispheric balance and the neuroplasticity of patients. It also makes up for the disadvantages of the current MI-BCIs, such as single usage scenarios, poor individual adaptability, and many interfering factors. We designed the offline training experiment to evaluate the feasibility of the EEG recognition strategy, and designed the online control experiment to verify the effectiveness of the VR-ULE system. The results showed that the MI classification method with MI cues in the VR scenes improved the accuracy of MI classification (86.49% ± 3.02%); all subjects performed two types of rehabilitation training tasks under their own models trained in the offline training experiment, with the highest average completion rates of 86.82% ± 4.66% and 88.48% ± 5.84%. The VR-ULE system can efficiently help stroke patients with hemiplegia complete upper-limb rehabilitation training tasks, and provide the new methods and strategies for BCI-based rehabilitation devices.
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Abbasi SF, Abbasi QH, Saeed F, Alghamdi NS. A convolutional neural network-based decision support system for neonatal quiet sleep detection. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:17018-17036. [PMID: 37920045 DOI: 10.3934/mbe.2023759] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
Sleep plays an important role in neonatal brain and physical development, making its detection and characterization important for assessing early-stage development. In this study, we propose an automatic and computationally efficient algorithm to detect neonatal quiet sleep (QS) using a convolutional neural network (CNN). Our study used 38-hours of electroencephalography (EEG) recordings, collected from 19 neonates at Fudan Children's Hospital in Shanghai, China (Approval No. (2020) 22). To train and test the CNN, we extracted 12 prominent time and frequency domain features from 9 bipolar EEG channels. The CNN architecture comprised two convolutional layers with pooling and rectified linear unit (ReLU) activation. Additionally, a smoothing filter was applied to hold the sleep stage for 3 minutes. Through performance testing, our proposed method achieved impressive results, with 94.07% accuracy, 89.70% sensitivity, 94.40% specificity, 79.82% F1-score and a 0.74 kappa coefficient when compared to human expert annotations. A notable advantage of our approach is its computational efficiency, with the entire training and testing process requiring only 7.97 seconds. The proposed algorithm has been validated using leave one subject out (LOSO) validation, which demonstrates its consistent performance across a diverse range of neonates. Our findings highlight the potential of our algorithm for real-time neonatal sleep stage classification, offering a fast and cost-effective solution. This research opens avenues for further investigations in early-stage development monitoring and the assessment of neonatal health.
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Affiliation(s)
- Saadullah Farooq Abbasi
- Department of Biomedical Engineering, Riphah International University, Islamabad 44000, Pakistan
| | - Qammer Hussain Abbasi
- James Watt School of Engineering, University of Glasgow, Glasgow, G4 0PE, United Kingdom
| | - Faisal Saeed
- DAAI Research Group, Department of Computing and Data Science, School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK
| | - Norah Saleh Alghamdi
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
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Lal U, Mathavu Vasanthsena S, Hoblidar A. Temporal Feature Extraction and Machine Learning for Classification of Sleep Stages Using Telemetry Polysomnography. Brain Sci 2023; 13:1201. [PMID: 37626557 PMCID: PMC10452545 DOI: 10.3390/brainsci13081201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 08/09/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023] Open
Abstract
Accurate sleep stage detection is crucial for diagnosing sleep disorders and tailoring treatment plans. Polysomnography (PSG) is considered the gold standard for sleep assessment since it captures a diverse set of physiological signals. While various studies have employed complex neural networks for sleep staging using PSG, our research emphasises the efficacy of a simpler and more efficient architecture. We aimed to integrate a diverse set of feature extraction measures with straightforward machine learning, potentially offering a more efficient avenue for sleep staging. We also aimed to conduct a comprehensive comparative analysis of feature extraction measures, including the power spectral density, Higuchi fractal dimension, singular value decomposition entropy, permutation entropy, and detrended fluctuation analysis, coupled with several machine-learning models, including XGBoost, Extra Trees, Random Forest, and LightGBM. Furthermore, data augmentation methods like the Synthetic Minority Oversampling Technique were also employed to rectify the inherent class imbalance in sleep data. The subsequent results highlighted that the XGBoost classifier, when used with a combination of all feature extraction measures as an ensemble, achieved the highest performance, with accuracies of 87%, 90%, 93%, 96%, and 97% and average F1-scores of 84.6%, 89%, 90.33%, 93.5%, and 93.5% for distinguishing between five-stage, four-stage, three-stage, and two distinct two-stage sleep configurations, respectively. This combined feature extraction technique represents a novel addition to the body of research since it achieves higher performance than many recently developed deep neural networks by utilising simpler machine-learning models.
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Affiliation(s)
- Utkarsh Lal
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India;
| | - Suhas Mathavu Vasanthsena
- Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India;
| | - Anitha Hoblidar
- Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India;
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Sholeyan AE, Rahatabad FN, Setarehdan SK. Designing an Automatic Sleep Staging System Using Deep Convolutional Neural Network Fed by Nonlinear Dynamic Transformation. J Med Biol Eng 2022. [DOI: 10.1007/s40846-022-00771-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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Montazeri S, Nevalainen P, Stevenson NJ, Vanhatalo S. Sleep State Trend (SST), a bedside measure of neonatal sleep state fluctuations based on single EEG channels. Clin Neurophysiol 2022; 143:75-83. [PMID: 36155385 DOI: 10.1016/j.clinph.2022.08.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 07/27/2022] [Accepted: 08/31/2022] [Indexed: 11/03/2022]
Abstract
OBJECTIVE To develop and validate an automated method for bedside monitoring of sleep state fluctuations in neonatal intensive care units. METHODS A deep learning-based algorithm was designed and trained using 53 EEG recordings from a long-term (a)EEG monitoring in 30 near-term neonates. The results were validated using an independent dataset from 30 polysomnography recordings. In addition, we constructed Sleep State Trend (SST), a bedside-ready means for visualizing classifier outputs. RESULTS The accuracy of quiet sleep detection in the training data was 90%, and the accuracy was comparable (85-86 %) in all bipolar derivations available from the 4-electrode recordings. The algorithm generalized well to a polysomnography dataset, showing 81% overall accuracy despite different signal derivations. SST allowed an intuitive, clear visualization of the classifier output. CONCLUSIONS Fluctuations in sleep states can be detected at high fidelity from a single EEG channel, and the results can be visualized as a transparent and intuitive trend in the bedside monitors. SIGNIFICANCE The Sleep State Trend (SST) may provide caregivers and clinical studies a real-time view of sleep state fluctuations and its cyclicity.
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Affiliation(s)
- Saeed Montazeri
- BABA Center, Department of Clinical Neurophysiology, HUS diagnostic center, Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Department of Physiology, University of Helsinki, Helsinki, Finland.
| | - Päivi Nevalainen
- BABA Center, Department of Clinical Neurophysiology, HUS diagnostic center, Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Nathan J Stevenson
- Brain Modeling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Sampsa Vanhatalo
- BABA Center, Department of Clinical Neurophysiology, HUS diagnostic center, Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Department of Physiology, University of Helsinki, Helsinki, Finland
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L-Tetrolet Pattern-Based Sleep Stage Classification Model Using Balanced EEG Datasets. Diagnostics (Basel) 2022; 12:diagnostics12102510. [PMID: 36292199 PMCID: PMC9600064 DOI: 10.3390/diagnostics12102510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 10/10/2022] [Accepted: 10/13/2022] [Indexed: 11/24/2022] Open
Abstract
Background: Sleep stage classification is a crucial process for the diagnosis of sleep or sleep-related diseases. Currently, this process is based on manual electroencephalogram (EEG) analysis, which is resource-intensive and error-prone. Various machine learning models have been recommended to standardize and automate the analysis process to address these problems. Materials and methods: The well-known cyclic alternating pattern (CAP) sleep dataset is used to train and test an L-tetrolet pattern-based sleep stage classification model in this research. By using this dataset, the following three cases are created, and they are: Insomnia, Normal, and Fused cases. For each of these cases, the machine learning model is tasked with identifying six sleep stages. The model is structured in terms of feature generation, feature selection, and classification. Feature generation is established with a new L-tetrolet (Tetris letter) function and multiple pooling decomposition for level creation. We fuse ReliefF and iterative neighborhood component analysis (INCA) feature selection using a threshold value. The hybrid and iterative feature selectors are named threshold selection-based ReliefF and INCA (TSRFINCA). The selected features are classified using a cubic support vector machine. Results: The presented L-tetrolet pattern and TSRFINCA-based sleep stage classification model yield 95.43%, 91.05%, and 92.31% accuracies for Insomnia, Normal dataset, and Fused cases, respectively. Conclusion: The recommended L-tetrolet pattern and TSRFINCA-based model push the envelope of current knowledge engineering by accurately classifying sleep stages even in the presence of sleep disorders.
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Towards precision sleep medicine: Self-attention GAN as an innovative data augmentation technique for developing personalized automatic sleep scoring classification. Comput Biol Med 2022; 148:105828. [PMID: 35816855 DOI: 10.1016/j.compbiomed.2022.105828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 05/07/2022] [Accepted: 07/03/2022] [Indexed: 11/03/2022]
Abstract
It is very important to have good quality sleep, which can affect aspects such as memory consolidation, emotional regulation, learning, physical development, and quality of life. Diagnosing human sleep quality and problems quickly and accurately is an important issue for human well-being. Therefore, many automatic sleep scoring methods have been proposed. However, the methods have been developed using sleep data from different individuals or groups. The accuracies of these proposed methods might decrease, due to existing individual differences. In this study, the self-attention generative adversarial network (SAGAN) was applied as an advanced data augmentation technique to propose an improved personalized automatic sleep scoring classification. First, the spectrograms were converted from electroencephalography (EEG). Then, SAGAN was used to generate synthesized spectrograms for each subject. Finally, the real and synthesized spectrograms of each subject were utilized to train a personalized classifier. The averaged accuracy and standard deviation of the proposed method are 95.74% and 3.78%, respectively. Compared to the classifier trained with all subjects' training data, the average accuracy increased by 8.08%. The results proved that the generated spectrograms significantly improved the performance of the personalized automatic sleep scoring classification. The contributions of the proposed method were that made the medical staff and subjects save massive medical resources and time for manual recording and scoring.
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16
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Wu YY, Hu YS, Wang J, Zang YF, Zhang Y. Toward Precise Localization of Abnormal Brain Activity: 1D CNN on Single Voxel fMRI Time-Series. Front Comput Neurosci 2022; 16:822237. [PMID: 35573265 PMCID: PMC9094401 DOI: 10.3389/fncom.2022.822237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 03/31/2022] [Indexed: 11/13/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) is one of the best techniques for precise localization of abnormal brain activity non-invasively. Machine-learning approaches have been widely used in neuroimaging studies; however, few studies have investigated the single-voxel modeling of fMRI data under cognitive tasks. We proposed a hybrid one-dimensional (1D) convolutional neural network (1D-CNN) based on the temporal dynamics of single-voxel fMRI time-series and successfully differentiated two continuous task states, namely, self-initiated (SI) and visually guided (VG) motor tasks. First, 25 activation peaks were identified from the contrast maps of SI and VG tasks in a blocked design. Then, the fMRI time-series of each peak voxel was transformed into a temporal-frequency domain by using continuous wavelet transform across a broader frequency range (0.003–0.313 Hz, with a step of 0.01 Hz). The transformed time-series was inputted into a 1D-CNN model for the binary classification of SI and VG continuous tasks. Compared with the univariate analysis, e.g., amplitude of low-frequency fluctuation (ALFF) at each frequency band, including, wavelet-ALFF, the 1D-CNN model highly outperformed wavelet-ALFF, with more efficient decoding models [46% of 800 models showing area under the curve (AUC) > 0.61] and higher decoding accuracies (94% of the efficient models), especially on the high-frequency bands (>0.1 Hz). Moreover, our results also demonstrated the advantages of wavelet decompositions over the original fMRI series by showing higher decoding performance on all peak voxels. Overall, this study suggests a great potential of single-voxel analysis using 1D-CNN and wavelet transformation of fMRI series with continuous, naturalistic, steady-state task design or resting-state design. It opens new avenues to precise localization of abnormal brain activity and fMRI-guided precision brain stimulation therapy.
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Affiliation(s)
- Yun-Ying Wu
- Center for Cognition and Brain Disorders and the Affiliated Hospital, Hangzhou Normal University, Hangzhou, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
- Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China
| | - Yun-Song Hu
- Center for Cognition and Brain Disorders and the Affiliated Hospital, Hangzhou Normal University, Hangzhou, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
- Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China
| | - Jue Wang
- Institute of Sports Medicine and Health, Chengdu Sport University, Chengdu, China
| | - Yu-Feng Zang
- Center for Cognition and Brain Disorders and the Affiliated Hospital, Hangzhou Normal University, Hangzhou, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, China
- Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China
- Transcranial Magnetic Stimulation Center, Deqing Hospital of Hangzhou Normal University, Huzhou, China
- *Correspondence: Yu-Feng Zang
| | - Yu Zhang
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
- Yu Zhang
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17
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Huang Z, Ling BWK. Sleeping stage classification based on joint quaternion valued singular spectrum analysis and ensemble empirical mode decomposition. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103086] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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18
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Zhao Y, Dai G, Borghini G, Zhang J, Li X, Zhang Z, Aricò P, Di Flumeri G, Babiloni F, Zeng H. Label-Based Alignment Multi-Source Domain Adaptation for Cross-Subject EEG Fatigue Mental State Evaluation. Front Hum Neurosci 2021; 15:706270. [PMID: 34658814 PMCID: PMC8519604 DOI: 10.3389/fnhum.2021.706270] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 08/27/2021] [Indexed: 11/13/2022] Open
Abstract
Accurate detection of driving fatigue is helpful in significantly reducing the rate of road traffic accidents. Electroencephalogram (EEG) based methods are proven to be efficient to evaluate mental fatigue. Due to its high non-linearity, as well as significant individual differences, how to perform EEG fatigue mental state evaluation across different subjects still keeps challenging. In this study, we propose a Label-based Alignment Multi-Source Domain Adaptation (LA-MSDA) for cross-subject EEG fatigue mental state evaluation. Specifically, LA-MSDA considers the local feature distributions of relevant labels between different domains, which efficiently eliminates the negative impact of significant individual differences by aligning label-based feature distributions. In addition, the strategy of global optimization is introduced to address the classifier confusion decision boundary issues and improve the generalization ability of LA-MSDA. Experimental results show LA-MSDA can achieve remarkable results on EEG-based fatigue mental state evaluation across subjects, which is expected to have wide application prospects in practical brain-computer interaction (BCI), such as online monitoring of driver fatigue, or assisting in the development of on-board safety systems.
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Affiliation(s)
- Yue Zhao
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Guojun Dai
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Gianluca Borghini
- Industrial NeuroScience Lab, University of Rome "La Sapienza", Rome, Italy
| | - Jiaming Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Xiufeng Li
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Zhenyan Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Pietro Aricò
- Industrial NeuroScience Lab, University of Rome "La Sapienza", Rome, Italy
| | | | - Fabio Babiloni
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China.,Industrial NeuroScience Lab, University of Rome "La Sapienza", Rome, Italy
| | - Hong Zeng
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China.,Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China
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19
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Martinek R, Ladrova M, Sidikova M, Jaros R, Behbehani K, Kahankova R, Kawala-Sterniuk A. Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach-Part II: Brain Signals. SENSORS (BASEL, SWITZERLAND) 2021; 21:6343. [PMID: 34640663 PMCID: PMC8512967 DOI: 10.3390/s21196343] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 09/12/2021] [Accepted: 09/14/2021] [Indexed: 12/14/2022]
Abstract
As it was mentioned in the previous part of this work (Part I)-the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work-various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform.
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Affiliation(s)
- Radek Martinek
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Martina Ladrova
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Michaela Sidikova
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Rene Jaros
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Khosrow Behbehani
- College of Engineering, The University of Texas in Arlington, Arlington, TX 76019, USA;
| | - Radana Kahankova
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
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20
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Jain R, Ganesan RA. Reliable sleep staging of unseen subjects with fusion of multiple EEG features and RUSBoost. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.103061] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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21
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Taghizadegan Y, Jafarnia Dabanloo N, Maghooli K, Sheikhani A. Prediction of obstructive sleep apnea using ensemble of recurrence plot convolutional neural networks (RPCNNs) from polysomnography signals. Med Hypotheses 2021; 154:110659. [PMID: 34399170 DOI: 10.1016/j.mehy.2021.110659] [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: 04/18/2021] [Revised: 06/28/2021] [Accepted: 07/11/2021] [Indexed: 12/01/2022]
Abstract
Obstructive Sleep Apnea (OSA) is a common disorder characterized by periodic cessation of breathing during sleep. OSA affects daily life and poses a severe threat to human health. The standard clinical method for identifying and predicting OSA events is the use of Polysomnography signals. In this paper, a novel scheme based on an ensemble of recurrence plots (RPs) and pre-trained convolutional neural networks (RPCNNs) is proposed to improve the prediction rate of OSA. First, RPs were used to represent the dynamic behavior of single electroencephalogram (EEG) and electrocardiogram (ECG) signals for 60 s before and during OSA events. Then, using RPs, three prompt CNNs named ResNet-50 were fine-tuned, and their classification results were fused via the Majority Voting (MV) method to produce a final result concerning prediction. Next, the subject-independent Leave-One-Subject-Out Cross-Validation (LOSO-CV) and subject-dependent 10-fold Cross-Validation (10-fold CV) methods were used to validate the prediction rate from signals derived from the University College Dublin Sleep Apnea Database. Finally, the highest achieved average accuracy for the fusion level was 91.74% and 89.45% at the 10-fold CV and LOSO-CV. Additionally, our results outperformed state-of-the-art findings and could be recommended to predict and detect other biomedical signals. As a result, this predictive system can also be used to adjust the air pressure in sleep apnea patients' Automatic Positive Airway Pressure (APAP) devices.
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Affiliation(s)
- Yashar Taghizadegan
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Nader Jafarnia Dabanloo
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
| | - Keivan Maghooli
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ali Sheikhani
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
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22
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Cimr D, Studnicka F, Fujita H, Cimler R, Slegr J. Application of mechanical trigger for unobtrusive detection of respiratory disorders from body recoil micro-movements. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 207:106149. [PMID: 34015736 DOI: 10.1016/j.cmpb.2021.106149] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 04/29/2021] [Indexed: 06/12/2023]
Abstract
Background and Objectives Automatic detection of breathing disorders plays an important role in the early signalization of respiratory diseases. Measuring methods can be based on electrocardiogram (ECG), sound, oximetry, or respiratory analysis. However, these approaches require devices placed on the human body or they are prone to disturbance by environmental influences. To solve these problems, we proposed a heart contraction mechanical trigger for unobtrusive detection of respiratory disorders from the mechanical measurement of cardiac contractions. We designed a novel method to calculate this mechanical trigger purely from measured mechanical signals without the use of ECG. Methods The approach is a built-on calculation of the so-called euclidean arc length from the signals. In comparison to previous researches, this system does not require any equipment attached to a person. This is achieved by locating the tensometers on the bed. Data from sensors are fused by the Cartan curvatures method to beat-to-beat vector input for the Convolutional neural network (CNN) classifier. Results In sum, 2281 disordered and 5130 normal breathing samples was collected for analysis. The experiments with use of 10-fold cross validation show that accuracy, sensitivity, and specificity reach values of 96.37%, 92.46%, and 98.11% respectively. Conclusions By the approach for detection, the system offers a novel way for a completely unobtrusive diagnosis of breathing-related health problems. The proposed solution can effectively be deployed in all clinical or home environments.
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Affiliation(s)
- Dalibor Cimr
- Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove 50003, Czech Republic
| | - Filip Studnicka
- Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove 50003, Czech Republic
| | - Hamido Fujita
- Faculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Vietnam; DaSCI Andalusian Institute of Data Science and Computational Intelligence, University of Granada, Granada, Spain; Regional Research Center, Iwate Prefectural University, Iwate, Japan.
| | - Richard Cimler
- Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove 50003, Czech Republic
| | - Jan Slegr
- Faculty of Science, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove 50003, Czech Republic
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23
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Rechichi I, Zibetti M, Borzì L, Olmo G, Lopiano L. Single-channel EEG classification of sleep stages based on REM microstructure. Healthc Technol Lett 2021; 8:58-65. [PMID: 34035926 PMCID: PMC8136764 DOI: 10.1049/htl2.12007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 12/14/2020] [Accepted: 01/07/2021] [Indexed: 01/05/2023] Open
Abstract
Rapid-eye movement (REM) sleep, or paradoxical sleep, accounts for 20-25% of total night-time sleep in healthy adults and may be related, in pathological cases, to parasomnias. A large percentage of Parkinson's disease patients suffer from sleep disorders, including REM sleep behaviour disorder and hypokinesia; monitoring their sleep cycle and related activities would help to improve their quality of life. There is a need to accurately classify REM and the other stages of sleep in order to properly identify and monitor parasomnias. This study proposes a method for the identification of REM sleep from raw single-channel electroencephalogram data, employing novel features based on REM microstructures. Sleep stage classification was performed by means of random forest (RF) classifier, K-nearest neighbour (K-NN) classifier and random Under sampling boosted trees (RUSBoost); the classifiers were trained using a set of published and novel features. REM detection accuracy ranges from 89% to 92.7%, and the classifiers achieved a F-1 score (REM class) of about 0.83 (RF), 0.80 (K-NN), and 0.70 (RUSBoost). These methods provide encouraging outcomes in automatic sleep scoring and REM detection based on raw single-channel electroencephalogram, assessing the feasibility of a home sleep monitoring device with fewer channels.
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Affiliation(s)
- Irene Rechichi
- Department of Control and Computer EngineeringPolitecnico di TorinoTorinoItaly
| | - Maurizio Zibetti
- Department of Neuroscience “Rita Levi Montalcini”Università degli Studi di TorinoTorinoItaly
| | - Luigi Borzì
- Department of Control and Computer EngineeringPolitecnico di TorinoTorinoItaly
| | - Gabriella Olmo
- Department of Control and Computer EngineeringPolitecnico di TorinoTorinoItaly
| | - Leonardo Lopiano
- Department of Neuroscience “Rita Levi Montalcini”Università degli Studi di TorinoTorinoItaly
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24
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Thara DK, Premasudha BG, Nayak RS, Murthy TV, Ananth Prabhu G, Hanoon N. Electroencephalogram for epileptic seizure detection using stacked bidirectional LSTM_GAP neural network. EVOLUTIONARY INTELLIGENCE 2021. [DOI: 10.1007/s12065-020-00459-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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25
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26
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Zhao C, Liu Z, Chen Z, Ning Y. A novel krill herd algorithm with orthogonality and its application to data clustering. INTELL DATA ANAL 2021. [DOI: 10.3233/ida-195056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Krill herd algorithm (KHA) is an emerging nature-inspired approach that has been successfully applied to optimization. However, KHA may get stuck into local optima owing to its poor exploitation. In this paper, the orthogonal learning (OL) mechanism is incorporated to enhance the performance of KHA for the first time, then an improved method named orthogonal krill herd algorithm (OKHA) is obtained. Compared with the existing hybridizations of KHA, OKHA could discover more useful information from historical data and construct a more promising solution. The proposed algorithm is applied to solve CEC2017 numerical problems, and its robustness is verified based on the simulation results. Moreover, OKHA is applied to tackle data clustering problems selected from the UCI Machine Learning Repository. The experimental results illustrate that OKHA is superior to or at least competitive with other representative clustering techniques.
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Affiliation(s)
- Chen Zhao
- College of Artificial Intelligence, Nankai University, Tianjin, China
- Key Laboratory of Intelligent Robotics of Tianjin, Tianjin, China
| | - Zhongxin Liu
- College of Artificial Intelligence, Nankai University, Tianjin, China
- Key Laboratory of Intelligent Robotics of Tianjin, Tianjin, China
| | - Zengqiang Chen
- College of Artificial Intelligence, Nankai University, Tianjin, China
- Key Laboratory of Intelligent Robotics of Tianjin, Tianjin, China
| | - Yao Ning
- College of Artificial Intelligence, Nankai University, Tianjin, China
- Key Laboratory of Intelligent Robotics of Tianjin, Tianjin, China
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27
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Neng W, Lu J, Xu L. CCRRSleepNet: A Hybrid Relational Inductive Biases Network for Automatic Sleep Stage Classification on Raw Single-Channel EEG. Brain Sci 2021; 11:456. [PMID: 33918506 PMCID: PMC8065855 DOI: 10.3390/brainsci11040456] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 03/20/2021] [Accepted: 03/30/2021] [Indexed: 01/31/2023] Open
Abstract
In the inference process of existing deep learning models, it is usually necessary to process the input data level-wise, and impose a corresponding relational inductive bias on each level. This kind of relational inductive bias determines the theoretical performance upper limit of the deep learning method. In the field of sleep stage classification, only a single relational inductive bias is adopted at the same level in the mainstream methods based on deep learning. This will make the feature extraction method of deep learning incomplete and limit the performance of the method. In view of the above problems, a novel deep learning model based on hybrid relational inductive biases is proposed in this paper. It is called CCRRSleepNet. The model divides the single channel Electroencephalogram (EEG) data into three levels: frame, epoch, and sequence. It applies hybrid relational inductive biases from many aspects based on three levels. Meanwhile, multiscale atrous convolution block (MSACB) is adopted in CCRRSleepNet to learn the features of different attributes. However, in practice, the actual performance of the deep learning model depends on the nonrelational inductive biases, so a variety of matching nonrelational inductive biases are adopted in this paper to optimize CCRRSleepNet. The CCRRSleepNet is tested on the Fpz-Cz and Pz-Oz channel data of the Sleep-EDF dataset. The experimental results show that the method proposed in this paper is superior to many existing methods.
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Affiliation(s)
- Wenpeng Neng
- College of Computer Science and Technology, Heilongjiang University, Harbin 150080, China; (W.N.); (L.X.)
| | - Jun Lu
- College of Computer Science and Technology, Heilongjiang University, Harbin 150080, China; (W.N.); (L.X.)
- Key Laboratory of Database and Parallel Computing of Heilongjiang Province, Heilongjiang University, Harbin 150080, China
| | - Lei Xu
- College of Computer Science and Technology, Heilongjiang University, Harbin 150080, China; (W.N.); (L.X.)
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28
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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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29
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End-to-end sleep staging using convolutional neural network in raw single-channel EEG. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102203] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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30
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Automated Detection of Sleep Stages Using Deep Learning Techniques: A Systematic Review of the Last Decade (2010–2020). APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10248963] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Sleep is vital for one’s general well-being, but it is often neglected, which has led to an increase in sleep disorders worldwide. Indicators of sleep disorders, such as sleep interruptions, extreme daytime drowsiness, or snoring, can be detected with sleep analysis. However, sleep analysis relies on visuals conducted by experts, and is susceptible to inter- and intra-observer variabilities. One way to overcome these limitations is to support experts with a programmed diagnostic tool (PDT) based on artificial intelligence for timely detection of sleep disturbances. Artificial intelligence technology, such as deep learning (DL), ensures that data are fully utilized with low to no information loss during training. This paper provides a comprehensive review of 36 studies, published between March 2013 and August 2020, which employed DL models to analyze overnight polysomnogram (PSG) recordings for the classification of sleep stages. Our analysis shows that more than half of the studies employed convolutional neural networks (CNNs) on electroencephalography (EEG) recordings for sleep stage classification and achieved high performance. Our study also underscores that CNN models, particularly one-dimensional CNN models, are advantageous in yielding higher accuracies for classification. More importantly, we noticed that EEG alone is not sufficient to achieve robust classification results. Future automated detection systems should consider other PSG recordings, such as electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) signals, along with input from human experts, to achieve the required sleep stage classification robustness. Hence, for DL methods to be fully realized as a practical PDT for sleep stage scoring in clinical applications, inclusion of other PSG recordings, besides EEG recordings, is necessary. In this respect, our report includes methods published in the last decade, underscoring the use of DL models with other PSG recordings, for scoring of sleep stages.
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31
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Yan R, Li F, Zhou DD, Ristaniemi T, Cong F. Automatic sleep scoring: A deep learning architecture for multi-modality time series. J Neurosci Methods 2020; 348:108971. [PMID: 33160019 DOI: 10.1016/j.jneumeth.2020.108971] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Accepted: 10/10/2020] [Indexed: 11/30/2022]
Abstract
BACKGROUND Sleep scoring is an essential but time-consuming process, and therefore automatic sleep scoring is crucial and urgent to help address the growing unmet needs for sleep research. This paper aims to develop a versatile deep-learning architecture to automate sleep scoring using raw polysomnography recordings. METHOD The model adopts a linear function to address different numbers of inputs, thereby extending model applications. Two-dimensional convolution neural networks are used to learn features from multi-modality polysomnographic signals, a "squeeze and excitation" block to recalibrate channel-wise features, together with a long short-term memory module to exploit long-range contextual relation. The learnt features are finally fed to the decision layer to generate predictions for sleep stages. RESULT Model performance is evaluated on three public datasets. For all tasks with different available channels, our model achieves outstanding performance not only on healthy subjects but even on patients with sleep disorders (SHHS: Acc-0.87, K-0.81; ISRUC: Acc-0.86, K-0.82; Sleep-EDF: Acc-0.86, K-0.81). The highest classification accuracy is achieved by a fusion of multiple polysomnographic signals. COMPARISON Compared to state-of-the-art methods that use the same dataset, the proposed model achieves a comparable or better performance, and exhibits low computational cost. CONCLUSIONS The model demonstrates its transferability among different datasets, without changing model architecture or hyper-parameters across tasks. Good model transferability promotes the application of transfer learning on small group studies with mismatched channels. Due to demonstrated availability and versatility, the proposed method can be integrated with diverse polysomnography systems, thereby facilitating sleep monitoring in clinical or routine care.
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Affiliation(s)
- Rui Yan
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, 116024, Dalian, China; Faculty of Information Technology, University of Jyväskylä, 40014, Jyväskylä, Finland
| | - Fan Li
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, 116024, Dalian, China
| | - Dong Dong Zhou
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, 116024, Dalian, China; Faculty of Information Technology, University of Jyväskylä, 40014, Jyväskylä, Finland
| | - Tapani Ristaniemi
- Faculty of Information Technology, University of Jyväskylä, 40014, Jyväskylä, Finland
| | - Fengyu Cong
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, 116024, Dalian, China; Faculty of Information Technology, University of Jyväskylä, 40014, Jyväskylä, Finland; School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, 116024, Dalian, China; Key Laboratory of Integrated Circuit and Biomedical Electronic System, Liaoning Province. Dalian University of Technology, 116024, Dalian, China
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Yang H, Mu W, Wei D, Zhang Y, Duan Y, Gao J, Gong X, Wang H, Wu X, Tao H, Chang J. A Novel Targeted and High-Efficiency Nanosystem for Combinational Therapy for Alzheimer's Disease. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2020; 7:1902906. [PMID: 33042734 PMCID: PMC7539195 DOI: 10.1002/advs.201902906] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 07/23/2020] [Indexed: 05/06/2023]
Abstract
Alzheimer's disease (AD) remains the most prevalent neurodegenerative disease, and no effective treatment is available yet. Metal-ion-triggered aggregates of amyloid-beta (Aβ) peptide and acetylcholine imbalance are reported to be possible factors in AD pathogenesis. Thus, a combination therapy that can not only inhibit and reduce Aβ aggregation but also simultaneously regulate acetylcholine imbalance that can serve as a potential treatment for AD is needed. Here, clioquinol (metal-ion chelating agent) and donepezil (acetylcholinesterase (AChE) inhibitor) co-encapsulated human serum albumin (HSA) nanoparticles (dcHGT NPs) are designed, which are modified with transcriptional activator protein (TAT) and monosialotetrahexosylganglioside (GM1). The GM1 lipid and TAT peptide endow this drug delivery nanosystem with high brain entry efficiency and long-term retention capabilities through intranasal administration. It is found that dcHGT NPs can significantly inhibit and eliminate Aβ aggregation, relieve acetylcholine-related inflammation in microglial cells, and protect primary neurons from Aβ oligomer-induced neurotoxicity in vitro. The alleviation of Aβ-related inflammation and AChE-inhibited effect further synergistically adjust acetylcholine imbalance. It is further demonstrated that dcHGT NPs reduce Aβ deposition, ameliorate neuron morphological changes, rescue memory deficits, and greatly improve acetylcholine regulation ability in vivo. This multifunctional synergetic nanosystem can be a new candidate to achieve highly efficient combination therapy for AD.
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Affiliation(s)
- Han Yang
- School of Life SciencesTianjin University92 Weijin Road, Nankai DistrictTianjin300072P. R. China
| | - Weihang Mu
- Department of RehabilitationTianjin Children's Hospital238 Longyan Road, Beichen DistrictTianjin300072P. R. China
| | - Daohe Wei
- School of Life SciencesTianjin University92 Weijin Road, Nankai DistrictTianjin300072P. R. China
| | - Yue Zhang
- School of Life SciencesTianjin University92 Weijin Road, Nankai DistrictTianjin300072P. R. China
| | - Yue Duan
- School of Life SciencesTianjin University92 Weijin Road, Nankai DistrictTianjin300072P. R. China
| | - Jun‐xiao Gao
- School of Life SciencesTianjin University92 Weijin Road, Nankai DistrictTianjin300072P. R. China
| | - Xiao‐qun Gong
- School of Life SciencesTianjin University92 Weijin Road, Nankai DistrictTianjin300072P. R. China
| | - Han‐jie Wang
- School of Life SciencesTianjin University92 Weijin Road, Nankai DistrictTianjin300072P. R. China
| | - Xiao‐li Wu
- School of Life SciencesTianjin University92 Weijin Road, Nankai DistrictTianjin300072P. R. China
| | - Huaying Tao
- Department of NeurologyTianjin Medical University General Hospital154 Anshan Road, Heping DistrictTianjin300072P. R. China
| | - Jin Chang
- School of Life SciencesTianjin University92 Weijin Road, Nankai DistrictTianjin300072P. R. China
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Lun X, Yu Z, Chen T, Wang F, Hou Y. A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals. Front Hum Neurosci 2020; 14:338. [PMID: 33100985 PMCID: PMC7522466 DOI: 10.3389/fnhum.2020.00338] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 07/31/2020] [Indexed: 11/13/2022] Open
Abstract
A brain-computer interface (BCI) based on electroencephalography (EEG) can provide independent information exchange and control channels for the brain and the outside world. However, EEG signals come from multiple electrodes, the data of which can generate multiple features. How to select electrodes and features to improve classification performance has become an urgent problem to be solved. This paper proposes a deep convolutional neural network (CNN) structure with separated temporal and spatial filters, which selects the raw EEG signals of the electrode pairs over the motor cortex region as hybrid samples without any preprocessing or artificial feature extraction operations. In the proposed structure, a 5-layer CNN has been applied to learn EEG features, a 4-layer max pooling has been used to reduce dimensionality, and a fully-connected (FC) layer has been utilized for classification. Dropout and batch normalization are also employed to reduce the risk of overfitting. In the experiment, the 4 s EEG data of 10, 20, 60, and 100 subjects from the Physionet database are used as the data source, and the motor imaginations (MI) tasks are divided into four types: left fist, right fist, both fists, and both feet. The results indicate that the global averaged accuracy on group-level classification can reach 97.28%, the area under the receiver operating characteristic (ROC) curve stands out at 0.997, and the electrode pair with the highest accuracy on 10 subjects dataset is FC3-FC4, with 98.61%. The research results also show that this CNN classification method with minimal (2) electrode can obtain high accuracy, which is an advantage over other methods on the same database. This proposed approach provides a new idea for simplifying the design of BCI systems, and accelerates the process of clinical application.
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Affiliation(s)
- Xiangmin Lun
- College of Mechanical and Electric Engineering, Changchun University of Science and Technology, Changchun, China.,School of Automation Engineering, Northeast Electric Power University, Jilin, China
| | - Zhenglin Yu
- College of Mechanical and Electric Engineering, Changchun University of Science and Technology, Changchun, China
| | - Tao Chen
- School of Automation Engineering, Northeast Electric Power University, Jilin, China
| | - Fang Wang
- School of Automation Engineering, Northeast Electric Power University, Jilin, China
| | - Yimin Hou
- School of Automation Engineering, Northeast Electric Power University, Jilin, China
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Shen H, Ran F, Xu M, Guez A, Li A, Guo A. An Automatic Sleep Stage Classification Algorithm Using Improved Model Based Essence Features. SENSORS 2020; 20:s20174677. [PMID: 32825024 PMCID: PMC7506989 DOI: 10.3390/s20174677] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 08/12/2020] [Accepted: 08/13/2020] [Indexed: 11/16/2022]
Abstract
The automatic sleep stage classification technique can facilitate the diagnosis of sleep disorders and release the medical expert from labor-consumption work. In this paper, novel improved model based essence features (IMBEFs) were proposed combining locality energy (LE) and dual state space models (DSSMs) for automatic sleep stage detection on single-channel electroencephalograph (EEG) signals. Firstly, each EEG epoch is decomposed into low-level sub-bands (LSBs) and high-level sub-bands (HSBs) by wavelet packet decomposition (WPD), separately. Then, the DSSMs are estimated by the LSBs and the LE calculation is carried out on HSBs. Thirdly, the IMBEFs extracted from the DSSM and LE are fed into the appropriate classifier for sleep stage classification. The performance of the proposed method was evaluated on three public sleep databases. The experimental results show that under the Rechtschaffen's and Kale's (R&K) standard, the sleep stage classification accuracies of six classes on the Sleep EDF database and the Dreams Subjects database are 92.04% and 78.92%, respectively. Under the American Academy of Sleep Medicine (AASM) standard, the classification accuracies of five classes in the Dreams Subjects database and the ISRUC database reached 79.90% and 81.65%. The proposed method can be used for reliable sleep stage classification with high accuracy compared with state-of-the-art methods.
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Affiliation(s)
- Huaming Shen
- School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, China; (F.R.); (M.X.); (A.L.); (A.G.)
- Correspondence:
| | - Feng Ran
- School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, China; (F.R.); (M.X.); (A.L.); (A.G.)
| | - Meihua Xu
- School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, China; (F.R.); (M.X.); (A.L.); (A.G.)
| | - Allon Guez
- Faculty of Biomedical Engineering, Drexel University, Philadelphia, PA 19104, USA;
| | - Ang Li
- School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, China; (F.R.); (M.X.); (A.L.); (A.G.)
| | - Aiying Guo
- School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, China; (F.R.); (M.X.); (A.L.); (A.G.)
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Rim B, Sung NJ, Min S, Hong M. Deep Learning in Physiological Signal Data: A Survey. SENSORS (BASEL, SWITZERLAND) 2020; 20:E969. [PMID: 32054042 PMCID: PMC7071412 DOI: 10.3390/s20040969] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 01/31/2020] [Accepted: 02/09/2020] [Indexed: 12/11/2022]
Abstract
Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially exploited from this novel approach to fulfil the desired medical tasks. Therefore, in this paper we survey the latest scientific research on deep learning in physiological signal data such as electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), and electrooculogram (EOG). We found 147 papers published between January 2018 and October 2019 inclusive from various journals and publishers. The objective of this paper is to conduct a detailed study to comprehend, categorize, and compare the key parameters of the deep-learning approaches that have been used in physiological signal analysis for various medical applications. The key parameters of deep-learning approach that we review are the input data type, deep-learning task, deep-learning model, training architecture, and dataset sources. Those are the main key parameters that affect system performance. We taxonomize the research works using deep-learning method in physiological signal analysis based on: (1) physiological signal data perspective, such as data modality and medical application; and (2) deep-learning concept perspective such as training architecture and dataset sources.
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Affiliation(s)
- Beanbonyka Rim
- Department of Computer Science, Soonchunhyang University, Asan 31538, Korea
| | - Nak-Jun Sung
- Department of Computer Science, Soonchunhyang University, Asan 31538, Korea
| | - Sedong Min
- Department of Medical IT Engineering, Soonchunhyang University, Asan 31538, Korea
| | - Min Hong
- Department of Computer Software Engineering, Soonchunhyang University, Asan 31538, Korea
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