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Shahabi MS, Shalbaf A, Rostami R, Kazemi R. A convolutional recurrent neural network with attention for response prediction to repetitive transcranial magnetic stimulation in major depressive disorder. Sci Rep 2023; 13:10147. [PMID: 37349335 PMCID: PMC10287753 DOI: 10.1038/s41598-023-35545-2] [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: 05/30/2022] [Accepted: 05/19/2023] [Indexed: 06/24/2023] Open
Abstract
Prediction of response to Repetitive Transcranial Magnetic Stimulation (rTMS) can build a very effective treatment platform that helps Major Depressive Disorder (MDD) patients to receive timely treatment. We proposed a deep learning model powered up by state-of-the-art methods to classify responders (R) and non-responders (NR) to rTMS treatment. Pre-treatment Electro-Encephalogram (EEG) signal of public TDBRAIN dataset and 46 proprietary MDD subjects were utilized to create time-frequency representations using Continuous Wavelet Transform (CWT) to be fed into the two powerful pre-trained Convolutional Neural Networks (CNN) named VGG16 and EfficientNetB0. Equipping these Transfer Learning (TL) models with Bidirectional Long Short-Term Memory (BLSTM) and attention mechanism for the extraction of most discriminative spatiotemporal features from input images, can lead to superior performance in the prediction of rTMS treatment outcome. Five brain regions named Frontal, Central, Parietal, Temporal, and occipital were assessed and the highest evaluated performance in 46 proprietary MDD subjects was acquired for the Frontal region using the TL-LSTM-Attention model based on EfficientNetB0 with accuracy, sensitivity, specificity, and Area Under the Curve (AUC) of 97.1%, 97.3%, 97.0%, and 0.96 respectively. Additionally, to test the generalizability of the proposed models, these TL-BLSTM-Attention models were evaluated on a public dataset called TDBRAIN and the highest accuracy of 82.3%, the sensitivity of 80.2%, the specificity of 81.9% and the AUC of 0.83 were obtained. Therefore, advanced deep learning methods using a time-frequency representation of EEG signals from the frontal brain region and the convolutional recurrent neural networks equipped with the attention mechanism can construct an accurate platform for the prediction of response to the rTMS treatment.
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Affiliation(s)
- Mohsen Sadat Shahabi
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Reza Rostami
- Department of Psychology, University of Tehran, Tehran, Iran
| | - Reza Kazemi
- Department of Cognitive Psychology, Institute for Cognitive Science Studies, Tehran, Iran
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Chang Y, Wang L, Zhao Y, Liu M, Zhang J. Research on two-class and four-class action recognition based on EEG signals. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:10376-10391. [PMID: 37322937 DOI: 10.3934/mbe.2023455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
BMI has attracted widespread attention in the past decade, which has greatly improved the living conditions of patients with motor disorders. The application of EEG signals in lower limb rehabilitation robots and human exoskeleton has also been gradually applied by researchers. Therefore, the recognition of EEG signals is of great significance. In this paper, a CNN-LSTM neural network model is designed to study the two-class and four-class motion recognition of EEG signals. In this paper, a brain-computer interface experimental scheme is designed. Combining the characteristics of EEG signals, the time-frequency characteristics of EEG signals and event-related potential phenomena are analyzed, and the ERD/ERS characteristics are obtained. Pre-process EEG signals, and propose a CNN-LSTM neural network model to classify the collected binary and four-class EEG signals. The experimental results show that the CNN-LSTM neural network model has a good effect, and its average accuracy and kappa coefficient are higher than the other two classification algorithms, which also shows that the classification algorithm selected in this paper has a good classification effect.
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Affiliation(s)
- Ying Chang
- College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150006, China
- School of Mechanical and Civil Engineering, Jilin Agricultural Science and Technology University, Jilin 132109, China
| | - Lan Wang
- College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150006, China
| | - Yunmin Zhao
- College of Electronic and Information Engineering, Tongji University, Shanghai 200092, China
| | - Ming Liu
- Technology Department YAMAMOTO CO., LTD, Higashine-shi 999-3701, Japan
| | - Jing Zhang
- Respiratory Department, JiLin Central Hospital, Jilin 132109, China
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Bu Y, Zheng J, Jia C. An efficient deep learning based predictor for identifying miRNA-triggered phasiRNA loci in plant. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:6853-6865. [PMID: 37161131 DOI: 10.3934/mbe.2023295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Phasic small interfering RNAs are plant secondary small interference RNAs that typically generated by the convergence of miRNAs and polyadenylated mRNAs. A growing number of studies have shown that miRNA-initiated phasiRNA plays crucial roles in regulating plant growth and stress responses. Experimental verification of miRNA-initiated phasiRNA loci may take considerable time, energy and labor. Therefore, computational methods capable of processing high throughput data have been proposed one by one. In this work, we proposed a predictor (DIGITAL) for identifying miRNA-initiated phasiRNAs in plant, which combined a multi-scale residual network with a bi-directional long-short term memory network. The negative dataset was constructed based on positive data, through replacing 60% of nucleotides randomly in each positive sample. Our predictor achieved the accuracy of 98.48% and 94.02% respectively on two independent test datasets with different sequence length. These independent testing results indicate the effectiveness of our model. Furthermore, DIGITAL is of robustness and generalization ability, and thus can be easily extended and applied for miRNA target recognition of other species. We provide the source code of DIGITAL, which is freely available at https://github.com/yuanyuanbu/DIGITAL.
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Affiliation(s)
- Yuanyuan Bu
- School of Science, Dalian Maritimr University, Dalian 116026, China
| | - Jia Zheng
- School of Science, Dalian Maritimr University, Dalian 116026, China
| | - Cangzhi Jia
- School of Science, Dalian Maritimr University, Dalian 116026, China
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Xie JY, Li RH, Yuan W, Du J, Zhou DS, Cheng YQ, Xu XM, Liu H, Yuan TF. Advances in neuroimaging studies of alcohol use disorder (AUD). PSYCHORADIOLOGY 2022; 2:146-155. [PMID: 38665276 PMCID: PMC11003430 DOI: 10.1093/psyrad/kkac018] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/08/2022] [Accepted: 11/14/2022] [Indexed: 04/28/2024]
Abstract
Alcohol use disorder (AUD) is a worldwide problem and the most common substance use disorder. Chronic alcohol consumption may have negative effects on the body, the mind, the family, and even society. With the progress of current neuroimaging methods, an increasing number of imaging techniques are being used to objectively detect brain impairment induced by alcoholism and serve a vital role in the diagnosis, prognosis, and treatment assessment of AUD. This article organizes and analyzes the research on alcohol dependence concerning the main noninvasive neuroimaging methods, structural magnetic resonance imaging, functional magnetic resonance imaging, and electroencephalography, as well as the most common noninvasive brain stimulation - transcranial magnetic stimulation, and intersperses the article with joint intra- and intergroup studies, providing an outlook on future research directions.
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Affiliation(s)
- Ji-Yu Xie
- School of Mental Health, Wenzhou Medical University, Wenzho 325000, Zhejiangu, China
| | - Rui-Hua Li
- Shandong Mental Health Center, Shandong University, Jinan 250014, Shandong, China
| | - Wei Yuan
- Shandong Mental Health Center, Shandong University, Jinan 250014, Shandong, China
| | - Jiang Du
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai 200030, China
| | - Dong-Sheng Zhou
- Department of Psychiatry, Ningbo Kangning Hospital, Ningbo 315000, Zhejiang, China
| | - Yu-Qi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming 650000, Yunnan, China
| | - Xue-Ming Xu
- Department of Psychiatry, Taizhou Second People's Hospital, Taizhou 318000, Zhejiang, China
| | - Heng Liu
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi 563000, Guizhou, China
| | - Ti-Fei Yuan
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai 200030, China
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Application of Neuroengineering Based on EEG Features in the Industrial Design of Comfort. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4667689. [PMID: 35720909 PMCID: PMC9205692 DOI: 10.1155/2022/4667689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 04/23/2022] [Accepted: 04/26/2022] [Indexed: 11/17/2022]
Abstract
The smart wheelchair is a service robot that can be used as a means of transportation for the elderly and the disabled. The patients were given an intelligent wheelchair designed by electroencephalogram (EEG), which was used for more than 8 hours and tested continuously for 1 month. By ridit analysis, the difference between the two groups was statistically significant (U = 3.72, P < 0.01). The scores of visual analogue scale (VAS) and joint ground visuality (JGV) in the observation group were significantly better than those in the control group. The modules of physiological function (PF), physical pain (PP), overall health (OH), vitality (VT), social function (SF), emotional function (EF), and mental health (MH) in the SF-36 scores of the two groups were significantly improved (P < 0.05), and the improvement of each module in the observation group was significantly better than that in the control group (P < 0.05). The levels of serum IL-6, IL-10, and superoxide dismutase (SOD) in the two groups were significantly improved (P < 0.05), and the improvement of serum IL-6, IL-10, and SOD in the observation group was significantly better than that in the control group (P < 0.05). It is suggested that neural engineering based on EEG characteristics can be well applied in comfort industrial design.
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Al-Hadeethi H, Abdulla S, Diykh M, Deo RC, Green JH. An Eigenvalues-Based Covariance Matrix Bootstrap Model Integrated With Support Vector Machines for Multichannel EEG Signals Analysis. Front Neuroinform 2022; 15:808339. [PMID: 35185506 PMCID: PMC8851395 DOI: 10.3389/fninf.2021.808339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 12/20/2021] [Indexed: 11/30/2022] Open
Abstract
Identification of alcoholism is clinically important because of the way it affects the operation of the brain. Alcoholics are more vulnerable to health issues, such as immune disorders, high blood pressure, brain anomalies, and heart problems. These health issues are also a significant cost to national health systems. To help health professionals to diagnose the disease with a high rate of accuracy, there is an urgent need to create accurate and automated diagnosis systems capable of classifying human bio-signals. In this study, an automatic system, denoted as (CT-BS- Cov-Eig based FOA-F-SVM), has been proposed to detect the prevalence and health effects of alcoholism from multichannel electroencephalogram (EEG) signals. The EEG signals are segmented into small intervals, with each segment passed to a clustering technique-based bootstrap (CT-BS) for the selection of modeling samples. A covariance matrix method with its eigenvalues (Cov-Eig) is integrated with the CT-BS system and applied for useful feature extraction related to alcoholism. To select the most relevant features, a nonparametric approach is adopted, and to classify the extracted features, a radius-margin-based support vector machine (F-SVM) with a fruit fly optimization algorithm (FOA), (i.e., FOA-F-SVM) is utilized. To assess the performance of the proposed CT-BS model, different types of evaluation methods are employed, and the proposed model is compared with the state-of-the-art models to benchmark the overall effectiveness of the newly designed system for EEG signals. The results in this study show that the proposed CT-BS model is more effective than the other commonly used methods and yields a high accuracy rate of 99%. In comparison with the state-of-the-art algorithms tested on identical databases describing the capability of the newly proposed FOA-F-SVM method, the study ascertains the proposed model as a promising medical diagnostic tool with potential implementation in automated alcoholism detection systems used by clinicians and other health practitioners. The proposed model, adopted as an expert system where EEG data could be classified through advanced pattern recognition techniques, can assist neurologists and other health professionals in the accurate and reliable diagnosis and treatment decisions related to alcoholism.
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Affiliation(s)
- Hanan Al-Hadeethi
- School of Mathematics Physics and Computing, University of Southern Queensland, Toowoomba, QLD, Australia
| | - Shahab Abdulla
- USQ College, University of Southern Queensland, Toowoomba, QLD, Australia
- Information and Communication Technology Research Group, Scientific Research Centre, Al-Ayen University, Nasiriyah, Iraq
| | - Mohammed Diykh
- School of Sciences, University of Southern Queensland, Toowoomba, QLD, Australia
- College of Education for Pure Science, University of Thi-Qar, Nasiriyah, Iraq
- *Correspondence: Mohammed Diykh, ;
| | - Ravinesh C. Deo
- School of Sciences, University of Southern Queensland, Toowoomba, QLD, Australia
| | - Jonathan H. Green
- USQ College, University of Southern Queensland, Toowoomba, QLD, Australia
- Faculty of the Humanities, University of the Free State, Bloemfontein, South Africa
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Cui F, Li S, Zhang Z, Sui M, Cao C, El-Latif Hesham A, Zou Q. DeepMC-iNABP: Deep learning for multiclass identification and classification of nucleic acid-binding proteins. Comput Struct Biotechnol J 2022; 20:2020-2028. [PMID: 35521556 PMCID: PMC9065708 DOI: 10.1016/j.csbj.2022.04.029] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 04/06/2022] [Accepted: 04/20/2022] [Indexed: 11/29/2022] Open
Abstract
Nucleic acid-binding proteins (NABPs), including DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs), play vital roles in gene expression. Accurate identification of these proteins is crucial. However, there are two existing challenges: one is the problem of ignoring DNA- and RNA-binding proteins (DRBPs), and the other is a cross-predicting problem referring to DBP predictors predicting DBPs as RBPs, and vice versa. In this study, we proposed a computational predictor, called DeepMC-iNABP, with the goal of solving these difficulties by utilizing a multiclass classification strategy and deep learning approaches. DBPs, RBPs, DRBPs and non-NABPs as separate classes of data were used for training the DeepMC-iNABP model. The results on test data collected in this study and two independent test datasets showed that DeepMC-iNABP has a strong advantage in identifying the DRBPs and has the ability to alleviate the cross-prediction problem to a certain extent. The web-server of DeepMC-iNABP is freely available at http://www.deepmc-inabp.net/. The datasets used in this research can also be downloaded from the website.
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Affiliation(s)
- Feifei Cui
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China
| | - Shuang Li
- Beidahuang Industry Group General Hospital, Harbin 150001, China
| | - Zilong Zhang
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China
| | - Miaomiao Sui
- Graduate School Agricultural and Life Science, The University of Tokyo, Tokyo 1138657, Japan
| | - Chen Cao
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Abd El-Latif Hesham
- Genetics Department, Faculty of Agriculture, Beni-Suef University, Beni-Suef 62511, Egypt
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China
- Corresponding author at: Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China.
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