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Qin J, Qin Z, Qin Z, Li F, Peng Y, Zhang Y, Yao Y. An automated approach for predicting HAMD-17 scores via divergent selective focused multi-heads self-attention network. Brain Res Bull 2024; 213:110984. [PMID: 38806119 DOI: 10.1016/j.brainresbull.2024.110984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 05/12/2024] [Accepted: 05/24/2024] [Indexed: 05/30/2024]
Abstract
This study introduces the Divergent Selective Focused Multi-heads Self-Attention Network (DSFMANet), an innovative deep learning model devised to automatically predict Hamilton Depression Rating Scale-17 (HAMD-17) scores in patients with depression. This model introduces a multi-branch structure for sub-bands and artificially configures attention focus factors on various branches, resulting in distinct attention distributions for different sub-bands. Experimental results demonstrate that when DSFMANet processes sub-band data, its performance surpasses current benchmarks in key metrics such as mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). This success is particularly evident in terms of MSE and MAE, where the performance of sub-band data is significantly superior, highlighting the model's potential in accurately predicting HAMD-17 scores. Concurrently, the experiment also compared the model's performance with sub-band and full-band data, affirming the superiority of the selective focus attention mechanism in electroencephalography (EEG) signal processing. DSFMANet, when utilizing sub-band data, exhibits higher data processing efficiency and reduced model complexity. The findings of this study underscore the significance of employing deep learning models based on sub-band analysis in depression diagnosis. The DSFMANet model not only effectively enhances the accuracy of depression diagnosis but also offers valuable research directions for similar applications in the future. This deep learning-based automated approach can effectively ascertain the HAMD-17 scores of patients with depression, thus offering more accurate and reliable support for clinical decision-making.
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Affiliation(s)
- Jing Qin
- School of Information and Software Engineering, University of Electronic Science and Technology of China, North Jianshe Road, Chengdu, Sichuan, PR China.
| | - Zhiguang Qin
- School of Information and Software Engineering, University of Electronic Science and Technology of China, North Jianshe Road, Chengdu, Sichuan, PR China
| | - Zhen Qin
- School of Information and Software Engineering, University of Electronic Science and Technology of China, North Jianshe Road, Chengdu, Sichuan, PR China
| | - Fali Li
- School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, High-tech Zone (West District), Chengdu, Sichuan, PR China
| | - Yueheng Peng
- School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, High-tech Zone (West District), Chengdu, Sichuan, PR China
| | - Yue Zhang
- Stanford University, Stanford, CA 94305, United States
| | - Yutong Yao
- Department of Neurology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China
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2
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Guo J, Zhao J, Han P, Wu Y, Zheng K, Huang C, Wang Y, Chen C, Guo Q. Finding the best predictive model for hypertensive depression in older adults based on machine learning and metabolomics research. Front Psychiatry 2024; 15:1370602. [PMID: 38993388 PMCID: PMC11236531 DOI: 10.3389/fpsyt.2024.1370602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 06/10/2024] [Indexed: 07/13/2024] Open
Abstract
Objective Depression is a common comorbidity in hypertensive older adults, yet depression is more difficult to diagnose correctly. Our goal is to find predictive models of depression in hypertensive patients using a combination of various machine learning (ML) methods and metabolomics. Methods Methods We recruited 379 elderly people aged ≥65 years from the Chinese community. Plasma samples were collected and assayed by gas chromatography/liquid chromatography-mass spectrometry (GC/LC-MS). Orthogonal partial least squares discriminant analysis (OPLS-DA), volcano diagrams and thermograms were used to distinguish metabolites. The attribute discriminators CfsSubsetEval combined with search method BestFirst in WEKA software was used to find the best predicted metabolite combinations, and then 24 classification methods with 10-fold cross-validation were used for prediction. Results 34 individuals were considered hypertensive combined with depression according to our criteria, and 34 subjects with hypertension only were matched according to age and sex. 19 metabolites by GC-MS and 65 metabolites by LC-MS contributed significantly to the differentiation between the depressed and non-depressed cohorts, with a VIP value of more than 1 and a P value of less than 0.05. There were multiple metabolic pathway alterations. The metabolite combinations screened with WEKA for optimal diagnostic value included 12 metabolites. The machine learning methods with AUC values greater than 0.9 were bayesNet and random forests, and their other evaluation measures are also better. Conclusion Altered metabolites and metabolic pathways are present in older adults with hypertension combined with depression. Methods using metabolomics and machine learning performed quite well in predicting depression in hypertensive older adults, contributing to further clinical research.
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Affiliation(s)
- Jiangling Guo
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
- Graduate School, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jingwang Zhao
- Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China
| | - Peipei Han
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
| | - Yahui Wu
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Kai Zheng
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Chuanjun Huang
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Yue Wang
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Cheng Chen
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
- School of Health, Fujian Medical University, Fuzhou, Fujian, China
| | - Qi Guo
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
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Wang Y, Peng Y, Han M, Liu X, Niu H, Cheng J, Chang S, Liu T. GCTNet: a graph convolutional transformer network for major depressive disorder detection based on EEG signals. J Neural Eng 2024; 21:036042. [PMID: 38788706 DOI: 10.1088/1741-2552/ad5048] [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: 09/25/2023] [Accepted: 05/24/2024] [Indexed: 05/26/2024]
Abstract
Objective.Identifying major depressive disorder (MDD) using objective physiological signals has become a pressing challenge.Approach.Hence, this paper proposes a graph convolutional transformer network (GCTNet) for accurate and reliable MDD detection using electroencephalogram (EEG) signals. The developed framework integrates a residual graph convolutional network block to capture spatial information and a Transformer block to extract global temporal dynamics. Additionally, we introduce the contrastive cross-entropy (CCE) loss that combines contrastive learning to enhance the stability and discriminability of the extracted features, thereby improving classification performance.Main results. The effectiveness of the GCTNet model and CCE loss was assessed using EEG data from 41 MDD patients and 44 normal controls, in addition to a publicly available dataset. Utilizing a subject-independent data partitioning method and 10-fold cross-validation, the proposed method demonstrated significant performance, achieving an average Area Under the Curve of 0.7693 and 0.9755 across both datasets, respectively. Comparative analyses demonstrated the superiority of the GCTNet framework with CCE loss over state-of-the-art algorithms in MDD detection tasks.Significance. The proposed method offers an objective and effective approach to MDD detection, providing valuable support for clinical-assisted diagnosis.
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Affiliation(s)
- Yuwen Wang
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, People's Republic of China
| | - Yudan Peng
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, People's Republic of China
| | - Mingxiu Han
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, People's Republic of China
| | - Xinyi Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, People's Republic of China
| | - Haijun Niu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, People's Republic of China
| | - Jian Cheng
- School of Computer Science and Engineering, Beihang University, Beijing, People's Republic of China
| | - Suhua Chang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, People's Republic of China
| | - Tao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, People's Republic of China
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4
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Mahmud MS, Fattah SA, Saquib M, Saha O. Emotion recognition with reduced channels using CWT based EEG feature representation and a CNN classifier. Biomed Phys Eng Express 2024; 10:045003. [PMID: 38457844 DOI: 10.1088/2057-1976/ad31f9] [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: 09/21/2023] [Accepted: 03/08/2024] [Indexed: 03/10/2024]
Abstract
Objective.Although emotion recognition has been studied for decades, a more accurate classification method that requires less computing is still needed. At present, in many studies, EEG features are extracted from all channels to recognize emotional states, however, there is a lack of an efficient feature domain that improves classification performance and reduces the number of EEG channels.Approach.In this study, a continuous wavelet transform (CWT)-based feature representation of multi-channel EEG data is proposed for automatic emotion recognition. In the proposed feature, the time-frequency domain information is preserved by using CWT coefficients. For a particular EEG channel, each CWT coefficient is mapped into a strength-to-entropy component ratio to obtain a 2D representation. Finally, a 2D feature matrix, namely CEF2D, is created by concatenating these representations from different channels and fed into a deep convolutional neural network architecture. Based on the CWT domain energy-to-entropy ratio, effective channel and CWT scale selection schemes are also proposed to reduce computational complexity.Main results.Compared with previous studies, the results of this study show that valence and arousal classification accuracy has improved in both 3-class and 2-class cases. For the 2-class problem, the average accuracies obtained for valence and arousal dimensions are 98.83% and 98.95%, respectively, and for the 3-class, the accuracies are 98.25% and 98.68%, respectively.Significance.Our findings show that the entropy-based feature of EEG data in the CWT domain is effective for emotion recognition. Utilizing the proposed feature domain, an effective channel selection method can reduce computational complexity.
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Affiliation(s)
- Md Sultan Mahmud
- Department of Computer Science and Engineering, The Pennsylvania State University, University Park-16802, PA, United States of America
| | - Shaikh Anowarul Fattah
- Department of Electrical and Electronic Engineering (EEE), Bangladesh University of Engineering and Technology, Dhaka-1205, Bangladesh
| | - Mohammad Saquib
- Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson-75080, TX, United States of America
| | - Oishy Saha
- Department of Electrical and Computer Engineering, The University of Maryland-College Park, College Park-20742, MD, United States of America
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5
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Li M, Luo Q, Zhou Y. BGOA-TVG: Binary Grasshopper Optimization Algorithm with Time-Varying Gaussian Transfer Functions for Feature Selection. Biomimetics (Basel) 2024; 9:187. [PMID: 38534872 DOI: 10.3390/biomimetics9030187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 03/09/2024] [Accepted: 03/11/2024] [Indexed: 03/28/2024] Open
Abstract
Feature selection aims to select crucial features to improve classification accuracy in machine learning and data mining. In this paper, a new binary grasshopper optimization algorithm using time-varying Gaussian transfer functions (BGOA-TVG) is proposed for feature selection. Compared with the traditional S-shaped and V-shaped transfer functions, the proposed Gaussian time-varying transfer functions have the characteristics of a fast convergence speed and a strong global search capability to convert a continuous search space to a binary one. The BGOA-TVG is tested and compared to S-shaped and V-shaped binary grasshopper optimization algorithms and five state-of-the-art swarm intelligence algorithms for feature selection. The experimental results show that the BGOA-TVG has better performance in UCI, DEAP, and EPILEPSY datasets for feature selection.
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Affiliation(s)
- Mengjun Li
- College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
| | - Qifang Luo
- College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
- Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning 530006, China
| | - Yongquan Zhou
- College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
- Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning 530006, China
- Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
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6
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Põld T, Päeske L, Hinrikus H, Lass J, Bachmann M. Temporal stability and correlation of EEG markers and depression questionnaires scores in healthy people. Sci Rep 2023; 13:21996. [PMID: 38081954 PMCID: PMC10713782 DOI: 10.1038/s41598-023-49237-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 12/06/2023] [Indexed: 12/18/2023] Open
Abstract
Mental disorders, especially depression, have become a rising problem in modern society. The development of methods and markers for the early detection of mental disorders is an actual problem. Psychological questionnaires are the only tools for evaluating the symptoms of mental disorders in clinical practice today. The electroencephalography (EEG) based non-invasive and cost-effective method seems feasible for the early detection of depression in occupational and family medicine centers and personal monitoring. The reliability of the EEG markers in the early detection of depression assumes their high temporal stability and correlation with the scores of depression questionnaires. The study was been performed on 17 healthy people over three years. Two hypotheses have been evaluated in the current study: first, the temporal stability of EEG markers is close to the stability of the scores of depression questionnaires, and second, EEG markers and depression questionnaires' scores are not correlated in healthy people. The results of the performed study support both hypotheses: the temporal stability of EEG markers is high and close to the stability of depression questionnaires scores and the correlation between the EEG markers and depression questionnaires scores is not detected in healthy people. The results of the current study contribute to the interpretation of results in depression EEG studies and to the feasibility of EEG markers in the detection of depression.
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Affiliation(s)
- Toomas Põld
- Department of Health Technologies, School of Information Technologies, Tallinn University of Technology, 5 Ehitajate Rd, 19086, Tallinn, Estonia
- Meliva Unimed Qvalitas Medical Centre, Tallinn, Estonia
| | - Laura Päeske
- Department of Health Technologies, School of Information Technologies, Tallinn University of Technology, 5 Ehitajate Rd, 19086, Tallinn, Estonia
| | - Hiie Hinrikus
- Department of Health Technologies, School of Information Technologies, Tallinn University of Technology, 5 Ehitajate Rd, 19086, Tallinn, Estonia.
| | - Jaanus Lass
- Department of Health Technologies, School of Information Technologies, Tallinn University of Technology, 5 Ehitajate Rd, 19086, Tallinn, Estonia
| | - Maie Bachmann
- Department of Health Technologies, School of Information Technologies, Tallinn University of Technology, 5 Ehitajate Rd, 19086, Tallinn, Estonia
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7
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Tian F, Zhu L, Shi Q, Wang R, Zhang L, Dong Q, Qian K, Zhao Q, Hu B. The Three-Lead EEG Sensor: Introducing an EEG-Assisted Depression Diagnosis System Based on Ant Lion Optimization. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2023; 17:1305-1318. [PMID: 37402182 DOI: 10.1109/tbcas.2023.3292237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/06/2023]
Abstract
For depression diagnosis, traditional methods such as interviews and clinical scales have been widely leveraged in the past few decades, but they are subjective, time-consuming, and labor-consuming. With the development of affective computing and Artificial Intelligence (AI) technologies, Electroencephalogram (EEG)-based depression detection methods have emerged. However, previous research has virtually neglected practical application scenarios, as most studies have focused on analyzing and modeling EEG data. Furthermore, EEG data is typically obtained from specialized devices that are large, complex to operate, and poorly ubiquitous. To address these challenges, a wearable three-lead EEG sensor with flexible electrodes was developed to obtain prefrontal-lobe EEG data. Experimental measurements show that the EEG sensor achieves promising performance (background noise of no more than 0.91 μVpp, Signal-to-Noise Ratio (SNR) of 26--48 dB, and electrode-skin contact impedance of less than 1 K Ω). In addition, EEG data from 70 depressed patients and 108 healthy controls were collected using the EEG sensor, and the linear and nonlinear features were extracted. The features were then weighted and selected using the Ant Lion Optimization (ALO) algorithm to improve classification performance. The experimental results show that the k-NN classifier achieves a classification accuracy of 90.70%, specificity of 96.53%, and sensitivity of 81.79%, indicating the promising potential of the three-lead EEG sensor combined with the ALO algorithm and the k-NN classifier for EEG-assisted depression diagnosis.
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8
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Zhang B, Wei D, Yan G, Li X, Su Y, Cai H. Spatial-Temporal EEG Fusion Based on Neural Network for Major Depressive Disorder Detection. Interdiscip Sci 2023; 15:542-559. [PMID: 37140772 PMCID: PMC10158716 DOI: 10.1007/s12539-023-00567-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 04/12/2023] [Accepted: 04/13/2023] [Indexed: 05/05/2023]
Abstract
In view of the major depressive disorder characteristics such as high mortality as well as high recurrence, it is important to explore an objective and effective detection method for major depressive disorder. Considering the advantages complementary of different machine learning algorithms in information mining process, as well as the fusion complementary of different information, in this study, the spatial-temporal electroencephalography fusion framework using neural network is proposed for major depressive disorder detection. Since electroencephalography is a typical time series signal, we introduce recurrent neural network embedded in long short-term memory unit for extract temporal domain features to solve the problem of long-distance information dependence. To reduce the volume conductor effect, the temporal electroencephalography data are mapping into a spatial brain functional network using phase lag index, then the spatial domain features were extracted from brain functional network using 2D convolutional neural networks. Considering the complementarity between different types of features, the spatial-temporal electroencephalography features are fused to achieve data diversity. The experimental results show that spatial-temporal features fusion can improve the detection accuracy of major depressive disorder with a highest of 96.33%. In addition, our research also found that theta, alpha, and full frequency band in brain regions of left frontal, left central, right temporal are closely related to MDD detection, especially theta frequency band in left frontal region. Only using single-dimension EEG data as decision basis, it is difficult to fully explore the valuable information hidden in the data, which affects the overall detection performance of MDD. Meanwhile, different algorithms have their own advantages for different application scenarios. Ideally, different algorithms should use their respective advantages to jointly address complex problems in engineering fields. To this end, we propose a computer-aided MDD detection framework based on spatial-temporal EEG fusion using neural network, as shown in Fig. 1. The simplified process is as follows: (1) Raw EEG data acquisition and preprocessing. (2) The time series EEG data of each channel are input as recurrent neural network (RNN), and RNN is used to process and extract temporal domain (TD) features. (3) The BFN among different EEG channels is constructed, and CNN is used to process and extract the spatial domain (SD) features of the BFN. (4) Based on the theory of information complementarity, the spatial-temporal information is fused to realize efficient MDD detection. Fig. 1 MDD detection framework based on spatial-temporal EEG fusion.
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Affiliation(s)
- Bingtao Zhang
- School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China.
- Key Laboratory of Opto-Technology and Intelligent Control Ministry of Education, Lanzhou Jiaotong University, Lanzhou, 730070, China.
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China.
| | - Dan Wei
- School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China
| | - Guanghui Yan
- School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China
| | - Xiulan Li
- Gansu Province Big Data Center, Lanzhou, 730000, China.
| | - Yun Su
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China
| | - Hanshu Cai
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China
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9
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Maji B, Roy AK, Nasreen S, Guha R, Routray A, Majumdar D. A Novel Technique for Detecting Depressive Disorder: A Speech Database-Based Approach. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082943 DOI: 10.1109/embc40787.2023.10341118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Depression is the second most diagnosed disease in the world and is predicted to be the highest by the year 2030. Depressive disorder impacts both on mentally and physically, thus diagnosing this disorder in early stage is essential. Automatic Depression Detection (ADD) system via speech can greatly facilitate early-stage depression diagnosis. Development of such systems demands a standard balanced database. In this work, we present a novel labeled audio distress interview database. To our knowledge, this is the first depression database in Bengali language that contains audio responses from depressed and non-depressed subjects. Alongside this, we present a set of hand-crafted acoustic features that effectively detect depression mood using speech signals. Finally, we justify the quality of our developed database and the efficacy of the feature set in predicting depression using a baseline machine learning (ML) model. We believe that the annotated database will be a valuable resource for use by treating clinicians.Clinical Relevance-This research reports a new speech database in Bengali language for depression detection. This database can be used in healthcare by developing an automatic prediction model for depression detection.
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10
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Yang L, Wang Y, Zhu X, Yang X, Zheng C. A gated temporal-separable attention network for EEG-based depression recognition. Comput Biol Med 2023; 157:106782. [PMID: 36931203 DOI: 10.1016/j.compbiomed.2023.106782] [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: 08/31/2022] [Revised: 02/14/2023] [Accepted: 03/09/2023] [Indexed: 03/13/2023]
Abstract
Depression, a common mental illness worldwide, needs to be diagnosed and cured at an early stage. To assist clinical diagnosis, an EEG-based deep learning frame, which is named the gated temporal-separable attention network (GTSAN), is proposed in this paper for depression recognition. GTSAN model extracts discriminative information from EEG recordings in two ways. On the one hand, the gated recurrent unit (GRU) is used in the GTSAN model to capture the EEG historical information to form the features. On the other hand, the model digs the multilevel information by using an improved version of temporal convolutional network (TCN), called temporal-separable convolution network (TSCN), which applies causal convolution and dilated convolution to extract features from fine to coarse scales. The TSCN and GRU features can be produced in parallel. Finally, the new model introduces the attention mechanism to give different weights to these features, allowing them to be used to identify depression more effectively. Experiments on two depression datasets have demonstrated that the proposed model can mine potential depression patterns in data and obtain high recognition accuracies. The proposed model provides the possibility of using an EEG-based system to assist for diagnosing depression.
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Affiliation(s)
- Lijun Yang
- School of Mathematics and Statistics, Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms, Henan University, Kaifeng 475004, China; Center for Applied Mathematics of Henan Province, Henan University, Zhengzhou, 450046, China.
| | - Yixin Wang
- School of Mathematics and Statistics, Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms, Henan University, Kaifeng 475004, China.
| | - Xiangru Zhu
- Institute of Cognition, Brain, and Health, Henan University, Kaifeng 475004, China.
| | - Xiaohui Yang
- School of Mathematics and Statistics, Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms, Henan University, Kaifeng 475004, China; Center for Applied Mathematics of Henan Province, Henan University, Zhengzhou, 450046, China.
| | - Chen Zheng
- School of Mathematics and Statistics, Henan Engineering Research Center for Artificial Intelligence Theory and Algorithms, Henan University, Kaifeng 475004, China; Center for Applied Mathematics of Henan Province, Henan University, Zhengzhou, 450046, China.
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11
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Zhang J, Xu B, Yin H. Depression screening using hybrid neural network. MULTIMEDIA TOOLS AND APPLICATIONS 2023; 82:1-16. [PMID: 37362740 PMCID: PMC9992920 DOI: 10.1007/s11042-023-14860-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 08/03/2022] [Accepted: 02/06/2023] [Indexed: 06/28/2023]
Abstract
Depression is a common cause of increased suicides worldwide, and studies have shown that the number of patients suffering from major depressive disorder (MDD) increased several-fold during the COVID-19 pandemic, highlighting the importance of disease detection and depression management, while increasing the need for effective diagnostic tools. In recent years, machine learning and deep learning methods based on electroencephalography (EEG) have achieved significant results in the field of automatic depression detection. However, most current studies have focused on a small number of EEG signal channels, and experimental data require special processing by professionals. In this study, 128 channels of EEG signals were simply filtered and 24-fold leave-one-out cross-validation experiments were performed using 2DCNN-LSTM classifier, support vector machine, K-nearest neighbor and decision tree. The current results show that the proposed 2DCNN-LSTM model has an average classification accuracy of 95.1% with an AUC of 0.98 for depression detection of 6-second participant EEG signals, and the model is much better than 72.05%, 79.7% and 79.49% for support vector machine, K nearest neighbor and decision tree. In addition, we found that the model achieved a 100% probability of correctly classifying the EEG signals of 300-second participants.
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Affiliation(s)
- Jiao Zhang
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - Baomin Xu
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - Hongfeng Yin
- School of Computer and Information Technology, Cangzhou Jiaotong College, Cangzhou, Hebei China
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12
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Xiao P, Ma K, Gu L, Huang Y, Zhang J, Duan Z, Wang G, Luo Z, Gan X, Yuan J. Inter-subject prediction of pediatric emergence delirium using feature selection and classification from spontaneous EEG signals. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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13
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Mukherjee R, Kundu A, Mukherjee I, Gupta D, Tiwari P, Khanna A, Shorfuzzaman M. IoT-cloud based healthcare model for COVID-19 detection: an enhanced k-Nearest Neighbour classifier based approach. COMPUTING 2023; 105. [PMCID: PMC8085103 DOI: 10.1007/s00607-021-00951-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
COVID - 19 affected severely worldwide. The pandemic has caused many causalities in a very short span. The IoT-cloud-based healthcare model requirement is utmost in this situation to provide a better decision in the covid-19 pandemic. In this paper, an attempt has been made to perform predictive analytics regarding the disease using a machine learning classifier. This research proposed an enhanced KNN (k NearestNeighbor) algorithm eKNN, which did not randomly choose the value of k. However, it used a mathematical function of the dataset’s sample size while determining the k value. The enhanced KNN algorithm eKNN has experimented on 7 benchmark COVID-19 datasets of different size, which has been gathered from standard data cloud of different countries (Brazil, Mexico, etc.). It appeared that the enhanced KNN classifier performs significantly better than ordinary KNN. The second research question augmented the enhanced KNN algorithm with feature selection using ACO (Ant Colony Optimization). Results indicated that the enhanced KNN classifier along with the feature selection mechanism performed way better than enhanced KNN without feature selection. This paper involves proposing an improved KNN attempting to find an optimal value of k and studying IoT-cloud-based COVID - 19 detection.
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Affiliation(s)
- Rajendrani Mukherjee
- Department of Computer Science and Engineering, University of Engineering and Management, Kolkata, India
| | - Aurghyadip Kundu
- Department of Computer Science and Engineering, Brainware University, Kolkata, India
| | - Indrajit Mukherjee
- Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, India
| | - Deepak Gupta
- Maharaja Agrasen Institute of Technology, Delhi, India
| | - Prayag Tiwari
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Ashish Khanna
- Maharaja Agrasen Institute of Technology, Delhi, India
| | - Mohammad Shorfuzzaman
- Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944 Saudi Arabia
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Tasci G, Loh HW, Barua PD, Baygin M, Tasci B, Dogan S, Tuncer T, Palmer EE, Tan RS, Acharya UR. Automated accurate detection of depression using twin Pascal’s triangles lattice pattern with EEG Signals. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.110190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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15
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Zhang B, Wei D, Yan G, Lei T, Cai H, Yang Z. Feature-level fusion based on spatial-temporal of pervasive EEG for depression recognition. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107113. [PMID: 36103735 DOI: 10.1016/j.cmpb.2022.107113] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 08/23/2022] [Accepted: 09/04/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE In view of the depression characteristics such as high prevalence, high disability rate, high fatality rate, and high recurrence rate, early identification and early intervention are the most effective methods to prevent irreversible damage of brain function over time. The traditional method of depression recognition based on questionnaires and interviews is time-consuming and labor-intensive, and heavily depends on the doctor's subjective experience. Therefore, accurate, convenient and effective recognition of depression has important social value and scientific significance. METHODS This paper proposes a depression recognition framework based on feature-level fusion of spatial-temporal pervasive electroencephalography (EEG). Time series EEG data were collected by portable three-electrode EEG acquisition instrument, and mapped to a spatial complex network called visibility graph (VG). Then temporal EEG features and spatial VG metric features were extracted and selected. Based on the correlation between features and categories, the differences in contribution of individual feature are explored, and different contribution coefficients are assigned to different features as the data basis of feature-level fusion to ensure the diversity of data. A cascade forest model based on three different decision forests is designed to realize the efficient depression recognition using spatial-temporal feature-level fusion data. RESULTS Experimental data were obtained from 26 depressed patients and 29 healthy controls (HC). The results of multiple control experiments show that compared with single type feature, feature-level fusion without contribution coefficient, and independent classifiers, the feature-level method with contribution coefficient of spatial-temporal has a stronger recognition ability of depression, and the highest accuracy is 92.48%. CONCLUSION Feature-level fusion method provides an effective computer-aided tool for rapid clinical diagnosis of depression.
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Affiliation(s)
- Bingtao Zhang
- School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China; School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
| | - Dan Wei
- School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
| | - Guanghui Yan
- School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
| | - Tao Lei
- School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi'an 710021, China
| | - Haishu Cai
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Zhifei Yang
- School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
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Cognitive Computing in Mental Healthcare: a Review of Methods and Technologies for Detection of Mental Disorders. Cognit Comput 2022. [DOI: 10.1007/s12559-022-10042-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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17
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Sanchez-Reolid R, Martinez-Saez MC, Garcia-Martinez B, Fernandez-Aguilar L, Segura LR, Latorre JM, Fernandez-Caballero A. Emotion Classification from EEG with a Low-Cost BCI Versus a High-End Equipment. Int J Neural Syst 2022; 32:2250041. [DOI: 10.1142/s0129065722500411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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18
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Sun S, Yang P, Chen H, Shao X, Ji S, Li X, Li G, Hu B. Electroconvulsive Therapy-Induced Changes in Functional Brain Network of Major Depressive Disorder Patients: A Longitudinal Resting-State Electroencephalography Study. Front Hum Neurosci 2022; 16:852657. [PMID: 35664348 PMCID: PMC9158117 DOI: 10.3389/fnhum.2022.852657] [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: 02/11/2022] [Accepted: 04/05/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectivesSeveral studies have shown abnormal network topology in patients with major depressive disorder (MDD). However, changes in functional brain networks associated with electroconvulsive therapy (ECT) remission based on electroencephalography (EEG) signals have yet to be investigated.MethodsNineteen-channel resting-state eyes-closed EEG signals were collected from 24 MDD patients pre- and post-ECT treatment. Functional brain networks were constructed by using various coupling methods and binarization techniques. Changes in functional connectivity and network metrics after ECT treatment and relationships between network metrics and clinical symptoms were explored.ResultsECT significantly increased global efficiency, edge betweenness centrality, local efficiency, and mean degree of alpha band after ECT treatment, and an increase in these network metrics had significant correlations with decreased depressive symptoms in repeated measures correlation. In addition, ECT regulated the distribution of hubs in frontal and occipital lobes.ConclusionECT modulated the brain’s global and local information-processing patterns. In addition, an ECT-induced increase in network metrics was associated with clinical remission.SignificanceThese findings might present the evidence for us to understand how ECT regulated the topology organization in functional brain networks of clinically remitted depressive patients.
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Affiliation(s)
- Shuting Sun
- Brain Health Engineering Laboratory, School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Peng Yang
- Shandong Daizhuang Hospital, Jining, China
| | - Huayu Chen
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Xuexiao Shao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Shanling Ji
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Xiaowei Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
- Shandong Academy of Intelligent Computing Technology, Jinan, China
- *Correspondence: Xiaowei Li,
| | - Gongying Li
- Department of Psychiatry, Huai’an Third People’s Hospital, Huai’an, China
- Gongying Li,
| | - Bin Hu
- Brain Health Engineering Laboratory, School of Medical Technology, Beijing Institute of Technology, Beijing, China
- Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
- Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University and Institute of Semiconductors, Chinese Academy of Sciences, Lanzhou, China
- Open Source Software and Real-Time System, Lanzhou University, Ministry of Education, Lanzhou, China
- Bin Hu,
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LSDD-EEGNet: An efficient end-to-end framework for EEG-based depression detection. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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20
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Zhang B, Cai H, Song Y, Tao L, Li Y. Computer-aided Recognition Based on Decision-level Multimodal Fusion for Depression. IEEE J Biomed Health Inform 2022; 26:3466-3477. [PMID: 35389872 DOI: 10.1109/jbhi.2022.3165640] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Aiming at the problem of depression recognition, this paper proposes a computer-aided recognition framework based on decision-level multimodal fusion. In Song Dynasty of China, the idea of multimodal fusion was contained in "one gets different impressions of a mountain when viewing it from the front or sideways, at a close range or from afar" poetry. Objective and comprehensive analysis of depression can more accurately restore its essence, and multimodal can represent more information about depression compared to single modal. Linear electroencephalography (EEG) features based on adaptive auto regression (AR) model and typical nonlinear EEG features are extracted. EEG features related to depression and graph metric features in depression related brain regions are selected as the data basis of multimodal fusion to ensure data diversity. Based on the theory of multi-agent cooperation, the computer-aided depression recognition model of decision-level is realized. The experimental data comes from 24 depressed patients and 29 healthy controls (HC). The results of multi-group controlled trials show that compared with single modal or independent classifiers, the decision-level multimodal fusion method has a stronger ability to recognize depression, and the highest accuracy rate 92.13% was obtained. In addition, our results suggest that improving the brain region associated with information processing can help alleviate and treat depression. In the field of classification and recognition, our results clarify that there is no universal classifier suitable for any condition.
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Zhao H, Liu J, Shen Z, Yan J. SCC-MPGCN: Self-Attention Coherence Clustering Based on Multi-Pooling Graph Convolutional Network for EEG Emotion Recognition. J Neural Eng 2022; 19. [PMID: 35354132 DOI: 10.1088/1741-2552/ac6294] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 03/29/2022] [Indexed: 11/12/2022]
Abstract
The emotion recognition with electroencephalography (EEG) has been widely studied using the deep learning methods, but the topology of EEG channels is rarely exploited completely. In this paper, we propose a self-attention coherence clustering based on multi-pooling graph convolutional network (SCC-MPGCN) model for EEG emotion recognition. locking value (PLV)The adjacency matrix is constructed based on phase- to describe the intrinsic relationship between different EEG electrodes as graph signals. The Laplacian matrix of a graph is obtained from the adjacency matrix and then is fed into the graph convolutional layers to learn the generalized features. Moreover, we propose a novel graph coarsening method called self-attention coherence clustering (SCC), using the coherence to cluster the nodes. The benefits are that the global information can be achieved from the raw data and the dimensionality of input can be reduced. Meanwhile, a multi-pooling graph convolutional network (MPGCN) block is introduced to learn the generalized emotional states features and tackle the problem of imbalanced dimensionality. The fully-connected layer and a softmax layer are adopted to drive the final prediction. We carry out the extensive experiments on DEAP dataset and the experimental results show that the proposed method has better classification results than the state-of-the-art methods with the 10-fold cross-validation. And the model achieves the emotion recognition performance with a mean accuracy of 96.37%, 97.02%, 96.72% on valence, arousal, and dominance dimension, respectively.
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Affiliation(s)
- Huijuan Zhao
- Tiangong University, No. 399, Binshui West Road, Xiqing District, Tianjin, Tianjin, Tianjin, 300387, CHINA
| | - Jingjin Liu
- Shantou University, No.243, Daxue Road, Jinping District, Shantou City, Guangdong Province, Shantou, Guangdong, 515063, CHINA
| | - Zhenqian Shen
- Tiangong University, No. 399, Binshui West Road, Xiqing District, Tianjin, Tianjin, Tianjin, 300387, CHINA
| | - Jingwen Yan
- Shantou University, No.243, Daxue Road, Jinping District, Shantou City, Guangdong Province, Shantou, 515063, CHINA
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Maithri M, Raghavendra U, Gudigar A, Samanth J, Murugappan M, Chakole Y, Acharya UR. Automated emotion recognition: Current trends and future perspectives. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 215:106646. [PMID: 35093645 DOI: 10.1016/j.cmpb.2022.106646] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 12/25/2021] [Accepted: 01/16/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Human emotions greatly affect the actions of a person. The automated emotion recognition has applications in multiple domains such as health care, e-learning, surveillance, etc. The development of computer-aided diagnosis (CAD) tools has led to the automated recognition of human emotions. OBJECTIVE This review paper provides an insight into various methods employed using electroencephalogram (EEG), facial, and speech signals coupled with multi-modal emotion recognition techniques. In this work, we have reviewed most of the state-of-the-art papers published on this topic. METHOD This study was carried out by considering the various emotion recognition (ER) models proposed between 2016 and 2021. The papers were analysed based on methods employed, classifier used and performance obtained. RESULTS There is a significant rise in the application of deep learning techniques for ER. They have been widely applied for EEG, speech, facial expression, and multimodal features to develop an accurate ER model. CONCLUSION Our study reveals that most of the proposed machine and deep learning-based systems have yielded good performances for automated ER in a controlled environment. However, there is a need to obtain high performance for ER even in an uncontrolled environment.
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Affiliation(s)
- M Maithri
- Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
| | - U Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
| | - Anjan Gudigar
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India.
| | - Jyothi Samanth
- Department of Cardiovascular Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India
| | - Murugappan Murugappan
- Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, 13133, Kuwait
| | - Yashas Chakole
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
| | - U Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Clementi 599489, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore
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Yin D, Chen D, Tang Y, Dong H, Li X. Adaptive feature selection with shapley and hypothetical testing: Case study of EEG feature engineering. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2021.11.063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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24
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Ye C, Yin Z, Zhao M, Tian Y, Sun Z. Identification of mental fatigue levels in a language understanding task based on multi-domain EEG features and an ensemble convolutional neural network. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103360] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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25
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Enhancing EEG-Based Mental Stress State Recognition Using an Improved Hybrid Feature Selection Algorithm. SENSORS 2021; 21:s21248370. [PMID: 34960469 PMCID: PMC8703860 DOI: 10.3390/s21248370] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 12/06/2021] [Accepted: 12/10/2021] [Indexed: 01/15/2023]
Abstract
In real-life applications, electroencephalogram (EEG) signals for mental stress recognition require a conventional wearable device. This, in turn, requires an efficient number of EEG channels and an optimal feature set. This study aims to identify an optimal feature subset that can discriminate mental stress states while enhancing the overall classification performance. We extracted multi-domain features within the time domain, frequency domain, time-frequency domain, and network connectivity features to form a prominent feature vector space for stress. We then proposed a hybrid feature selection (FS) method using minimum redundancy maximum relevance with particle swarm optimization and support vector machines (mRMR-PSO-SVM) to select the optimal feature subset. The performance of the proposed method is evaluated and verified using four datasets, namely EDMSS, DEAP, SEED, and EDPMSC. To further consolidate, the effectiveness of the proposed method is compared with that of the state-of-the-art metaheuristic methods. The proposed model significantly reduced the features vector space by an average of 70% compared with the state-of-the-art methods while significantly increasing overall detection performance.
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26
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Ghiasi S, Dell'Acqua C, Benvenuti SM, Scilingo EP, Gentili C, Valenza G, Greco A. Classifying subclinical depression using EEG spectral and connectivity measures. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2050-2053. [PMID: 34891691 DOI: 10.1109/embc46164.2021.9630044] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Detecting depression on its early stages helps preventing the onset of severe depressive episodes. In this study, we propose an automatic classification pipeline to detect subclinical depression (i.e., dysphoria) through the electroencephalography (EEG) signal. To this aim, we recorded the EEG signals in resting condition from 26 female participants with dysphoria and 38 female controls. The EEG signals were processed to extract several spectral and functional connectivity features to feed a nonlinear Support Vector Machine (SVM) classifier embedded with a Recursive Feature Elimination (RFE) algorithm. Our recognition pipeline obtained a maximum classification accuracy of 83.91% in recognizing dysphoria patients with a combination of connectivity and spectral measures. Moreover, an accuracy of 76.11% was achieved with only the 4 most informative functional connections, suggesting a central role of cortical connectivity in the theta band for early depression recognition. The present study can facilitate the diagnosis of subclinical conditions of depression and may provide reliable indicators of depression for the clinical community.
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Emotion Recognition Based on EEG Using Generative Adversarial Nets and Convolutional Neural Network. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:2520394. [PMID: 34671415 PMCID: PMC8523271 DOI: 10.1155/2021/2520394] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 08/28/2021] [Accepted: 09/25/2021] [Indexed: 11/22/2022]
Abstract
Emotion recognition plays an important role in the field of human-computer interaction (HCI). Automatic emotion recognition based on EEG is an important topic in brain-computer interface (BCI) applications. Currently, deep learning has been widely used in the field of EEG emotion recognition and has achieved remarkable results. However, due to the cost of data collection, most EEG datasets have only a small amount of EEG data, and the sample categories are unbalanced in these datasets. These problems will make it difficult for the deep learning model to predict the emotional state. In this paper, we propose a new sample generation method using generative adversarial networks to solve the problem of EEG sample shortage and sample category imbalance. In experiments, we explore the performance of emotion recognition with the frequency band correlation and frequency band separation computational models before and after data augmentation on standard EEG-based emotion datasets. Our experimental results show that the method of generative adversarial networks for data augmentation can effectively improve the performance of emotion recognition based on the deep learning model. And we find that the frequency band correlation deep learning model is more conducive to emotion recognition.
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28
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EEG Mental Stress Assessment Using Hybrid Multi-Domain Feature Sets of Functional Connectivity Network and Time-Frequency Features. SENSORS 2021; 21:s21186300. [PMID: 34577505 PMCID: PMC8473213 DOI: 10.3390/s21186300] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 09/07/2021] [Accepted: 09/16/2021] [Indexed: 12/28/2022]
Abstract
Exposure to mental stress for long period leads to serious accidents and health problems. To avoid negative consequences on health and safety, it is very important to detect mental stress at its early stages, i.e., when it is still limited to acute or episodic stress. In this study, we developed an experimental protocol to induce two different levels of stress by utilizing a mental arithmetic task with time pressure and negative feedback as the stressors. We assessed the levels of stress on 22 healthy subjects using frontal electroencephalogram (EEG) signals, salivary alpha-amylase level (AAL), and multiple machine learning (ML) classifiers. The EEG signals were analyzed using a fusion of functional connectivity networks estimated by the Phase Locking Value (PLV) and temporal and spectral domain features. A total of 210 different features were extracted from all domains. Only the optimum multi-domain features were used for classification. We then quantified stress levels using statistical analysis and seven ML classifiers. Our result showed that the AAL level was significantly increased (p < 0.01) under stress condition in all subjects. Likewise, the functional connectivity network demonstrated a significant decrease under stress, p < 0.05. Moreover, we achieved the highest stress classification accuracy of 93.2% using the Support Vector Machine (SVM) classifier. Other classifiers produced relatively similar results.
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Saeedi A, Saeedi M, Maghsoudi A, Shalbaf A. Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach. Cogn Neurodyn 2021; 15:239-252. [PMID: 33854642 PMCID: PMC7969675 DOI: 10.1007/s11571-020-09619-0] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 07/13/2020] [Accepted: 07/16/2020] [Indexed: 02/07/2023] Open
Abstract
Deep learning techniques have recently made considerable advances in the field of artificial intelligence. These methodologies can assist psychologists in early diagnosis of mental disorders and preventing severe trauma. Major Depression Disorder (MDD) is a common and serious medical condition whose exact manifestations are not fully understood. So, early discovery of MDD patients helps to cure or limit the adverse effects. Electroencephalogram (EEG) is prominently used to study brain diseases such as MDD due to having high temporal resolution information, and being a noninvasive, inexpensive and portable method. This paper has proposed an EEG-based deep learning framework that automatically discriminates MDD patients from healthy controls. First, the relationships among EEG channels in the form of effective brain connectivity analysis are extracted by Generalized Partial Directed Coherence (GPDC) and Direct directed transfer function (dDTF) methods. A novel combination of sixteen connectivity methods (GPDC and dDTF in eight frequency bands) was used to construct an image for each individual. Finally, the constructed images of EEG signals are applied to the five different deep learning architectures. The first and second algorithms were based on one and two-dimensional convolutional neural network (1DCNN-2DCNN). The third method is based on long short-term memory (LSTM) model, while the fourth and fifth algorithms utilized a combination of CNN with LSTM model namely, 1DCNN-LSTM and 2DCNN-LSTM. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals. The efficiency of the proposed algorithms is evaluated on resting state EEG data obtained from 30 healthy subjects and 34 MDD patients. The experiments show that the 1DCNN-LSTM applied on constructed image of effective connectivity achieves best results with accuracy of 99.24% due to specific architecture which captures the presence of spatial and temporal relations in the brain connectivity. The proposed method as a diagnostic tool is able to help clinicians for diagnosing the MDD patients for early diagnosis and treatment.
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Affiliation(s)
- Abdolkarim Saeedi
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Maryam Saeedi
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Arash Maghsoudi
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Emotion recognition from EEG signals using empirical mode decomposition and second-order difference plot. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102389] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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31
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Jiang C, Li Y, Tang Y, Guan C. Enhancing EEG-Based Classification of Depression Patients Using Spatial Information. IEEE Trans Neural Syst Rehabil Eng 2021; 29:566-575. [PMID: 33587703 DOI: 10.1109/tnsre.2021.3059429] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND Depression has become a leading mental disorder worldwide. Evidence has shown that subjects with depression exhibit different spatial responses in neurophysiological signals from the healthy controls when they are exposed to positive and negative stimuli. METHODS We proposed an effective electroencephalogram-based detection method for depression classification using spatial information. A face-in-the-crowd task, including positive and negative emotional facial expressions, was presented to 30 participants, including 16 depression patients and 14 healthy controls. Differential entropy and the genetic algorithm were used for feature extraction and selection, and a support vector machine was used for classification. A task-related common spatial pattern (TCSP) was proposed to enhance the spatial differences before the feature extraction. RESULTS AND DISCUSSION We achieved a leave-one-subject-out cross-validation classification result of 84% and 85.7% for positive and negative stimuli, respectively, using TCSP, which is statistically significantly higher than 81.7% and 83.2%, respectively, acquired without the TCSP (p < 0.05). We also evaluated the classification performance using individual frequency bands and found that the contribution of the gamma band was predominant. In addition, we evaluated different classifiers, including k-nearest neighbor and logistic regression, which showed similar trends in the improvement of classification by employing TCSP. CONCLUSION The results show that our proposed method, employing spatial information, significantly improves the accuracy of classifying depression patients.
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Amidfar M, Kim YK. EEG Correlates of Cognitive Functions and Neuropsychiatric Disorders: A Review of Oscillatory Activity and Neural Synchrony Abnormalities. CURRENT PSYCHIATRY RESEARCH AND REVIEWS 2021. [DOI: 10.2174/2666082216999201209130117] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
A large body of evidence suggested that disruption of neural rhythms and
synchronization of brain oscillations are correlated with a variety of cognitive and perceptual processes.
Cognitive deficits are common features of psychiatric disorders that complicate treatment of
the motivational, affective and emotional symptoms.
Objective:
Electrophysiological correlates of cognitive functions will contribute to understanding of
neural circuits controlling cognition, the causes of their perturbation in psychiatric disorders and
developing novel targets for the treatment of cognitive impairments.
Methods:
This review includes a description of brain oscillations in Alzheimer’s disease, bipolar
disorder, attention-deficit/hyperactivity disorder, major depression, obsessive compulsive disorders,
anxiety disorders, schizophrenia and autism.
Results:
The review clearly shows that the reviewed neuropsychiatric diseases are associated with
fundamental changes in both spectral power and coherence of EEG oscillations.
Conclusion:
In this article, we examined the nature of brain oscillations, the association of brain
rhythms with cognitive functions and the relationship between EEG oscillations and neuropsychiatric
diseases. Accordingly, EEG oscillations can most likely be used as biomarkers in psychiatric
disorders.
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Affiliation(s)
- Meysam Amidfar
- Department of Neuroscience, Tehran University of Medical Sciences, Tehran, Iran
| | - Yong-Ku Kim
- Department of Psychiatry, College of Medicine, Korea University, Seoul, South Korea
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Uyulan C, Ergüzel TT, Unubol H, Cebi M, Sayar GH, Nezhad Asad M, Tarhan N. Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach. Clin EEG Neurosci 2021; 52:38-51. [PMID: 32491928 DOI: 10.1177/1550059420916634] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The human brain is characterized by complex structural, functional connections that integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation of both structural and functional connections of the brain and the effects in the diagnosis and treatment of neurodegenerative diseases. Currently, there is no clinically specific diagnostic biomarker capable of confirming the diagnosis of major depressive disorder (MDD). Therefore, exploring translational biomarkers of mood disorders based on deep learning (DL) has valuable potential with its recently underlined promising outcomes. In this article, an electroencephalography (EEG)-based diagnosis model for MDD is built through advanced computational neuroscience methodology coupled with a deep convolutional neural network (CNN) approach. EEG recordings are analyzed by modeling 3 different deep CNN structure, namely, ResNet-50, MobileNet, Inception-v3, in order to dichotomize MDD patients and healthy controls. EEG data are collected for 4 main frequency bands (Δ, θ, α, and β, accompanying spatial resolution with location information by collecting data from 19 electrodes. Following the pre-processing step, different DL architectures were employed to underline discrimination performance by comparing classification accuracies. The classification performance of models based on location data, MobileNet architecture generated 89.33% and 92.66% classification accuracy. As to the frequency bands, delta frequency band outperformed compared to other bands with 90.22% predictive accuracy and area under curve (AUC) value of 0.9 for ResNet-50 architecture. The main contribution of the study is the delineation of distinctive spatial and temporal features using various DL architectures to dichotomize 46 MDD subjects from 46 healthy subjects. Exploring translational biomarkers of mood disorders based on DL perspective is the main focus of this study and, though it is challenging, with its promising potential to improve our understanding of the psychiatric disorders, computational methods are highly worthy for the diagnosis process and valuable in terms of both speed and accuracy compared with classical approaches.
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Affiliation(s)
- Caglar Uyulan
- Department of Mechatronics, Faculty of Engineering, Bulent Ecevit University, Zonguldak, Turkey
| | - Türker Tekin Ergüzel
- Department of Software Engineering, Faculty of Engineering and Natural Sciences, Uskudar University, Istanbul, Turkey
| | - Huseyin Unubol
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| | - Merve Cebi
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| | - Gokben Hizli Sayar
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| | | | - Nevzat Tarhan
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
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34
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Ontology-Based Decision Support Tool for Automatic Sleep Staging Using Dual-Channel EEG Data. Symmetry (Basel) 2020. [DOI: 10.3390/sym12111921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Sleep staging has attracted significant attention as a critical step in auxiliary diagnosis of sleep disease. To avoid subjectivity of doctor’s manual sleep staging, and to realize scientific management of massive physiological data, an ontology-based decision support tool is proposed. The tool implements an automated procedure for sleep staging using dual-channel electroencephalogram (EEG) signals. First of all, it encodes EEG features, sleep-related concepts and other contextual information to “EEG-Sleep ontology”. Secondly, a rule-set is constructed based on a data mining technique. Finally, the first two steps are processed in a reasoning engine which is automatically assign each 30 s epoch (segment) sleep stage to one of five possible sleep stages: WA, NREM1, NREM2, SWS and REM. The rule set is obtained using EEG data taken from the Sleep-EDF database [EXPANDED] according to the random forest algorithm (RF), we prove that the performance of the proposed method with 89.12% accuracy, and 0.81 Kappa statistics is superior to other algorithms such as Bayesian network, C4.5, support vector machine, and multilayer perceptron. Additionally, our proposed approach improved performance when compared to other studies using a small subset of the Sleep-EDF database [EXPANDED].
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35
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Liu J, Wu G, Luo Y, Qiu S, Yang S, Li W, Bi Y. EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder. Front Syst Neurosci 2020; 14:43. [PMID: 32982703 PMCID: PMC7492909 DOI: 10.3389/fnsys.2020.00043] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Accepted: 06/12/2020] [Indexed: 11/13/2022] Open
Abstract
Emotion classification based on brain-computer interface (BCI) systems is an appealing research topic. Recently, deep learning has been employed for the emotion classifications of BCI systems and compared to traditional classification methods improved results have been obtained. In this paper, a novel deep neural network is proposed for emotion classification using EEG systems, which combines the Convolutional Neural Network (CNN), Sparse Autoencoder (SAE), and Deep Neural Network (DNN) together. In the proposed network, the features extracted by the CNN are first sent to SAE for encoding and decoding. Then the data with reduced redundancy are used as the input features of a DNN for classification task. The public datasets of DEAP and SEED are used for testing. Experimental results show that the proposed network is more effective than conventional CNN methods on the emotion recognitions. For the DEAP dataset, the highest recognition accuracies of 89.49% and 92.86% are achieved for valence and arousal, respectively. For the SEED dataset, however, the best recognition accuracy reaches 96.77%. By combining the CNN, SAE, and DNN and training them separately, the proposed network is shown as an efficient method with a faster convergence than the conventional CNN.
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Affiliation(s)
- Junxiu Liu
- School of Electronic Engineering, Guangxi Normal University, Guilin, China
- Guangxi Key Lab of Multi-Source Information Mining & Security, Guangxi Normal University, Guilin, China
| | - Guopei Wu
- School of Electronic Engineering, Guangxi Normal University, Guilin, China
- Guangxi Key Lab of Multi-Source Information Mining & Security, Guangxi Normal University, Guilin, China
| | - Yuling Luo
- School of Electronic Engineering, Guangxi Normal University, Guilin, China
- Guangxi Key Lab of Multi-Source Information Mining & Security, Guangxi Normal University, Guilin, China
| | - Senhui Qiu
- School of Electronic Engineering, Guangxi Normal University, Guilin, China
- Guangxi Key Lab of Multi-Source Information Mining & Security, Guangxi Normal University, Guilin, China
- Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing, Guilin, China
| | - Su Yang
- Department of Computer Science and Software Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Wei Li
- Academy for Engineering & Technology, Fudan University, Shanghai, China
- Department of Electronic Engineering, The University of York, York, United Kingdom
| | - Yifei Bi
- College of Foreign Languages, University of Shanghai for Science and Technology, Shanghai, China
- Department of Psychology, The University of York, York, United Kingdom
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36
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Leite JAA, Dos Santos MAC, da Silva RMC, Andrade ADO, da Silva GM, Bazan R, de Souza LAPS, Luvizutto GJ. Alpha and beta cortical activity during guitar playing: task complexity and audiovisual stimulus analysis. Somatosens Mot Res 2020; 37:245-251. [PMID: 32597273 DOI: 10.1080/08990220.2020.1784130] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
PURPOSE Some studies have explored the relationship between music and cortical activities; however, there are just few studies investigating guitar performance associated with different sensory stimuli. Our aim was to evaluate alpha and beta activity during guitar playing. MATERIALS AND METHOD Twenty healthy right-handed people participated in this study. Cortical activity was measured by electroencephalogram (EEG) during rest and 4 tasks (1: easy music with an auditory stimulus; 2: easy music with an audiovisual stimulus; 3: complex music with an auditory stimulus; 4: complex music with an audiovisual stimulus). The peak frequency (PF), median frequency (MF) and root mean square (RMS) of alpha and beta EEG signals were assessed. RESULTS A higher alpha PF at the T3-P3 was observed, and this difference was higher between rest and task 3, rest and task 4, tasks 1 and 3, and tasks 1 and 4. For beta waves, a higher PF was observed at C4-P4 and a higher RMS at C3-C4 and O1-O2. At C4-P4, differences between rest and tasks 2 and 4 were observed. The RMS of beta waves at C3-C4 presented differences between rest and task 3 and at O1-O2 between rest and task 2 and 4. CONCLUSION The action observation of audiovisual stimuli while playing guitar can increase beta wave activity in the somatosensory and motor cortexes; and increase in the alpha activity in the somatosensory and auditory cortexes and increase in the beta activity in the bilateral visual cortexes during complex music execution, regardless of the stimulus type received. Abbreviations: bpm: beats per minute; C: central; EEG: electroencephalogram; F: frontal; Hz: hertz; LABCOM: Laboratory of Motor Control and Biomechanics; MD: mean difference; MF: median frequency; O: occipital; P: parietal; PF: peak frequency; R: rest; RMS: root mean square; T: temporal; T1: task 1; T2: task 2; T3: task 3; T4: task 4; UFTM: Federal University of Triângulo Mineiro.
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Affiliation(s)
- José Artur Aragão Leite
- Department of Physical Therapy, Federal University of Triângulo Mineiro, Uberaba, MG, Brazil
| | | | | | - Adriano de Oliveira Andrade
- Centre for Innovation and Technology Assessment in Health, Federal University of Uberlândia, Uberlândia, MG, Brazil
| | - Gustavo Moreira da Silva
- Centre for Innovation and Technology Assessment in Health, Federal University of Uberlândia, Uberlândia, MG, Brazil
| | - Rodrigo Bazan
- Botucatu Medical School, São Paulo State University, Botucatu, SP, Brazil
| | | | - Gustavo José Luvizutto
- Department of Physical Therapy, Federal University of Triângulo Mineiro, Uberaba, MG, Brazil
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37
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Zhu J, Wang Z, Gong T, Zeng S, Li X, Hu B, Li J, Sun S, Zhang L. An Improved Classification Model for Depression Detection Using EEG and Eye Tracking Data. IEEE Trans Nanobioscience 2020; 19:527-537. [PMID: 32340958 DOI: 10.1109/tnb.2020.2990690] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
At present, depression has become a main health burden in the world. However, there are many problems with the diagnosis of depression, such as low patient cooperation, subjective bias and low accuracy. Therefore, reliable and objective evaluation method is needed to achieve effective depression detection. Electroencephalogram (EEG) and eye movements (EMs) data have been widely used for depression detection due to their advantages of easy recording and non-invasion. This research proposes a content based ensemble method (CBEM) to promote the depression detection accuracy, both static and dynamic CBEM were discussed. In the proposed model, EEG or EMs dataset was divided into subsets by the context of the experiments, and then a majority vote strategy was used to determine the subjects' label. The validation of the method is testified on two datasets which included free viewing eye tracking and resting-state EEG, and these two datasets have 36,34 subjects respectively. For these two datasets, CBEM achieves accuracies of 82.5% and 92.65% respectively. The results show that CBEM outperforms traditional classification methods. Our findings provide an effective solution for promoting the accuracy of depression identification, and provide an effective method for identificationof depression, which in the future could be used for the auxiliary diagnosis of depression.
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38
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Li X, La R, Wang Y, Hu B, Zhang X. A Deep Learning Approach for Mild Depression Recognition Based on Functional Connectivity Using Electroencephalography. Front Neurosci 2020; 14:192. [PMID: 32300286 PMCID: PMC7142271 DOI: 10.3389/fnins.2020.00192] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 02/24/2020] [Indexed: 12/15/2022] Open
Abstract
Early detection remains a significant challenge for the treatment of depression. In our work, we proposed a novel approach to mild depression recognition using electroencephalography (EEG). First, we explored abnormal organization in the functional connectivity network of mild depression using graph theory. Second, we proposed a novel classification model for recognizing mild depression. Considering the powerful ability of CNN to process two-dimensional data, we applied CNN separately to the two-dimensional data form of the functional connectivity matrices from five EEG bands (delta, theta, alpha, beta, and gamma). In addition, inspired by recent breakthroughs in the ability of deep recurrent CNNs to classify mental load, we merged the functional connectivity matrices from the three EEG bands that performed the best into a three-channel image to classify mild depression-related and normal EEG signals using the CNN. The results of the graph theory analysis showed that the brain functional network of the mild depression group had a larger characteristic path length and a lower clustering coefficient than the healthy control group, showing deviation from the small-world network. The proposed classification model obtained a classification accuracy of 80.74% for recognizing mild depression. The current study suggests that the combination of a CNN and functional connectivity matrix may provide a promising objective approach for diagnosing mild depression. Deep learning approaches such as this might have the potential to inform clinical practice and aid in research on psychiatric disorders.
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Affiliation(s)
- Xiaowei Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Rong La
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Ying Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
- Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
| | - Xuemin Zhang
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education, Faculty of Psychology, Beijing Normal University, Beijing, China
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China
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39
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A deep learning framework for automatic diagnosis of unipolar depression. Int J Med Inform 2019; 132:103983. [DOI: 10.1016/j.ijmedinf.2019.103983] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2019] [Revised: 08/20/2019] [Accepted: 09/27/2019] [Indexed: 01/02/2023]
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40
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Zhang X, Shen J, Din ZU, Liu J, Wang G, Hu B. Multimodal Depression Detection: Fusion of Electroencephalography and Paralinguistic Behaviors Using a Novel Strategy for Classifier Ensemble. IEEE J Biomed Health Inform 2019; 23:2265-2275. [PMID: 31478879 DOI: 10.1109/jbhi.2019.2938247] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Currently, depression has become a common mental disorder and one of the main causes of disability worldwide. Due to the difference in depressive symptoms evoked by individual differences, how to design comprehensive and effective depression detection methods has become an urgent demand. This study explored from physiological and behavioral perspectives simultaneously and fused pervasive electroencephalography (EEG) and vocal signals to make the detection of depression more objective, effective and convenient. After extraction of several effective features for these two types of signals, we trained six representational classifiers on each modality, then denoted diversity and correlation of decisions from different classifiers using co-decision tensor and combined these decisions into the ultimate classification result with multi-agent strategy. Experimental results on 170 (81 depressed patients and 89 normal controls) subjects showed that the proposed multi-modal depression detection strategy is superior to the single-modal classifiers or other typical late fusion strategies in accuracy, f1-score and sensitivity. This work indicates that late fusion of pervasive physiological and behavioral signals is promising for depression detection and the multi-agent strategy can take advantage of diversity and correlation of different classifiers effectively to gain a better final decision.
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41
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Li X, Zhang X, Zhu J, Mao W, Sun S, Wang Z, Xia C, Hu B. Depression recognition using machine learning methods with different feature generation strategies. Artif Intell Med 2019; 99:101696. [DOI: 10.1016/j.artmed.2019.07.004] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Revised: 07/11/2019] [Accepted: 07/16/2019] [Indexed: 10/26/2022]
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42
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Hou Y, Chen S. Distinguishing Different Emotions Evoked by Music via Electroencephalographic Signals. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2019; 2019:3191903. [PMID: 30956655 PMCID: PMC6431402 DOI: 10.1155/2019/3191903] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 12/25/2018] [Accepted: 01/28/2019] [Indexed: 11/18/2022]
Abstract
Music can evoke a variety of emotions, which may be manifested by distinct signals on the electroencephalogram (EEG). Many previous studies have examined the associations between specific aspects of music, including the subjective emotions aroused, and EEG signal features. However, no study has comprehensively examined music-related EEG features and selected those with the strongest potential for discriminating emotions. So, this paper conducted a series of experiments to identify the most influential EEG features induced by music evoking different emotions (calm, joy, sad, and angry). We extracted 27-dimensional features from each of 12 electrode positions then used correlation-based feature selection method to identify the feature set most strongly related to the original features but with lowest redundancy. Several classifiers, including Support Vector Machine (SVM), C4.5, LDA, and BPNN, were then used to test the recognition accuracy of the original and selected feature sets. Finally, results are analyzed in detail and the relationships between selected feature set and human emotions are shown clearly. Through the classification results of 10 random examinations, it could be concluded that the selected feature sets of Pz are more effective than other features when using as the key feature set to classify human emotion statues.
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Affiliation(s)
- Yimin Hou
- School of Automation Engineering, Northeast Electric Power University, Jilin, China
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43
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EEG-based mild depression recognition using convolutional neural network. Med Biol Eng Comput 2019; 57:1341-1352. [DOI: 10.1007/s11517-019-01959-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2018] [Accepted: 01/28/2019] [Indexed: 10/27/2022]
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44
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Pérez-Benito FJ, Villacampa-Fernández P, Conejero JA, García-Gómez JM, Navarro-Pardo E. A happiness degree predictor using the conceptual data structure for deep learning architectures. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 168:59-68. [PMID: 29183649 DOI: 10.1016/j.cmpb.2017.11.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Revised: 08/27/2017] [Accepted: 11/06/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Happiness is a universal fundamental human goal. Since the emergence of Positive Psychology, a major focus in psychological research has been to study the role of certain factors in the prediction of happiness. The conventional methodologies are based on linear relationships, such as the commonly used Multivariate Linear Regression (MLR), which may suffer from the lack of representative capacity to the varied psychological features. Using Deep Neural Networks (DNN), we define a Happiness Degree Predictor (H-DP) based on the answers to five psychometric standardized questionnaires. METHODS A Data-Structure driven architecture for DNNs (D-SDNN) is proposed for defining a HDP in which the network architecture enables the conceptual interpretation of psychological factors associated to happiness. Four different neural network configurations have been tested, varying the number of neurons and the presence or absence of bias in the hidden layers. Two metrics for evaluating the influence of conceptual dimensions have been defined and computed: one quantifies the influence weight of the conceptual dimension in absolute terms and the other one pinpoints the direction (positive or negative) of the influence. MATERIALS A cross-sectional survey targeting non-institutionalized adult population residing in Spain was completed by 823 cases. The total of 111 elements of the survey are grouped by socio-demographic data and by five psychometric scales (Brief COPE Inventory, EPQR-A, GHQ-28, MOS-SSS and SDHS) measuring several psychological factors acting one as the outcome (SDHS) and the four others as predictors. RESULTS Our D-SDNN approach provided a better outcome (MSE: 1.46·10-2) than MLR (MSE: 2.30·10-2), hence improving by 37% the predictive accuracy, and allowing to simulate the conceptual structure. CONCLUSIONS We observe a better performance of Deep Neural Networks (DNN) with respect to traditional methodologies. This demonstrates its capability to capture the conceptual structure for predicting happiness degree through psychological variables assessed by standardized questionnaires. It also permits to estimate the influence of each factor on the outcome without assuming a linear relationship.
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Affiliation(s)
- Francisco Javier Pérez-Benito
- Biomedical Data Science Lab. Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politécnica de Valéncia, Camino de Vera s/n, Valencia 46022, Spain.
| | - Patricia Villacampa-Fernández
- Instituto Universitario de Matemática Pura y Aplicada, Universitat Politécnica de Valéncia, Camino de Vera s/n, Valencia 46022, Spain; Departamento de Psicología Evolutiva y de la Educación, Universitat de Valéncia, Avenida Blasco Ibáñez, 21, Valencia 46010, Spain.
| | - J Alberto Conejero
- Instituto Universitario de Matemática Pura y Aplicada, Universitat Politécnica de Valéncia, Camino de Vera s/n, Valencia 46022, Spain.
| | - Juan M García-Gómez
- Biomedical Data Science Lab. Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politécnica de Valéncia, Camino de Vera s/n, Valencia 46022, Spain.
| | - Esperanza Navarro-Pardo
- Departamento de Psicología Evolutiva y de la Educación, Universitat de Valéncia, Avenida Blasco Ibáñez, 21, Valencia 46010, Spain.
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45
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Wan Z, Zhang H, Chen J, Zhou H, Yang J, Zhong N. WaaS architecture-driven depressive mood status quantitative analysis based on forehead EEG and self-rating tool. Brain Inform 2018; 5:15. [PMID: 30515600 PMCID: PMC6429167 DOI: 10.1186/s40708-018-0093-y] [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: 06/02/2018] [Accepted: 11/08/2018] [Indexed: 11/25/2022] Open
Abstract
Background Although the objective depression evaluation is a hot topic in recent years, less is known concerning developing a pervasive and objective approach for quantitatively evaluating depression. Driven by the Wisdom as a Service architecture, a quantitative analysis method for rating depressive mood status based on forehead electroencephalograph (EEG) and an electronic diary log application named quantitative log for mental state (Q-Log) is proposed. A regression method based on random forest algorithm is adopted to train the quantitative model, where independent variables are forehead EEG features and the dependent variables are the first principal component (FPC) values of the Q-Log. Results The Leave-One-Participant-Out Cross-Validation is adopted to estimate the performance of the quantitative model, and the result shows that the model outcomes have a moderate uphill relationship (the average coefficient equals 0.6556 and the P value less than 0.01) with the FPC values of the Q-Log. Furthermore, an exemplary application of knowledge sharing, which is developed by using ontology technology and Jena inference subsystem, is given to illustrate the preliminary work for annotating data and facilitating clinical users to understand the meaning of the quantitative analysis results. Conclusions This method combining physiological sensor data with psychological self-rating data could provide new insights into the pervasive and objective depression evaluation processes in daily life.
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Affiliation(s)
- Zhijiang Wan
- Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, 3710864, Japan
| | - Hao Zhang
- College of Economics and Management, Nanjing Forestry University, Nanjing, 210037, China
| | - Jianhui Chen
- College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, 100124, China.,International WIC Institute, Beijing University of Technology, Beijing, 100088, China
| | - Haiyan Zhou
- College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, 100124, China.,International WIC Institute, Beijing University of Technology, Beijing, 100088, China
| | - Jie Yang
- Beijing Anding Hospital of Capital Medical University, Beijing, 100088, China
| | - Ning Zhong
- Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, 3710864, Japan. .,College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, 100124, China. .,International WIC Institute, Beijing University of Technology, Beijing, 100088, China.
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46
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Electroencephalography based fatigue detection using a novel feature fusion and extreme learning machine. COGN SYST RES 2018. [DOI: 10.1016/j.cogsys.2018.08.018] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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47
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Li X, Li J, Hu B, Zhu J, Zhang X, Wei L, Zhong N, Li M, Ding Z, Yang J, Zhang L. Attentional bias in MDD: ERP components analysis and classification using a dot-probe task. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 164:169-179. [PMID: 30195425 DOI: 10.1016/j.cmpb.2018.07.003] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Revised: 06/27/2018] [Accepted: 07/16/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Strands of evidence have supported existence of negative attentional bias in patients with depression. This study aimed to assess the behavioral and electrophysiological signatures of attentional bias in major depressive disorder (MDD) and explore whether ERP components contain valuable information for discriminating between MDD patients and healthy controls (HCs). METHODS Electroencephalography data were collected from 17 patients with MDD and 17 HCs in a dot-probe task, with emotional-neutral pairs as experimental materials. Fourteen features related to ERP waveform shape were generated. Then, Correlated Feature Selection (CFS), ReliefF and GainRatio (GR) were applied for feature selection. For discriminating between MDDs and HCs, k-nearest neighbor (KNN), C4.5, Sequential Minimal Optimization (SMO) and Logistic Regression (LR) were used. RESULTS Behaviorally, MDD patients showed significantly shorter reaction time (RT) to valid than invalid sad trials, with significantly higher bias score for sad-neutral pairs. Analysis of split-half reliability in RT indices indicated a strong reliability in RT, while coefficients of RT bias scores neared zero. These behavioral effects were supported by ERP results. MDD patients had higher P300 amplitude with the probe replacing a sad face than a neutral face, indicating difficult attention disengagement from negative emotional faces. Meanwhile, data mining analysis based on ERP components suggested that CFS was the best feature selection algorithm. Especially for the P300 induced by valid sad trials, the classification accuracy of CFS combination with any classifier was above 85%, and the KNN (k = 3) classifier achieved the highest accuracy (94%). CONCLUSIONS MDD patients show difficulty in attention disengagement from negative stimuli, reflected by P300. The CFS over other methods leads to a good overall performance in most cases, especially when KNN classifier is used for P300 component classification, illustrating that ERP component may be applied as a tool for auxiliary diagnosis of depression.
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Affiliation(s)
- Xiaowei Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China.
| | - Jianxiu Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China.
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, China; Beijing Institute for Brain Disorders, Capital Medical University, China.
| | - Jing Zhu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China.
| | - Xuemin Zhang
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, China; State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China.
| | - Liuqing Wei
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, China
| | - Ning Zhong
- International WIC Institute, Beijing University of Technology, Beijing, China.
| | - Mi Li
- International WIC Institute, Beijing University of Technology, Beijing, China.
| | - Zhijie Ding
- The Third People's Hospital of Tianshui City, Tianshui, China.
| | - Jing Yang
- Department of Child Psychology, Lanzhou University Second Hospital, Lanzhou, China
| | - Lan Zhang
- Department of Child Psychology, Lanzhou University Second Hospital, Lanzhou, China
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EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2018; 2018:6534041. [PMID: 30254690 PMCID: PMC6142786 DOI: 10.1155/2018/6534041] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Revised: 06/26/2018] [Accepted: 07/02/2018] [Indexed: 01/08/2023]
Abstract
Sleep staging is considered as an effective indicator for auxiliary diagnosis of sleep diseases and related psychiatric diseases, so it attracts a lot of attention from sleep researchers. Nevertheless, sleep staging based on visual inspection of tradition is subjective, time-consuming, and error-prone due to the large bulk of data which have to be processed. Therefore, automatic sleep staging is essential in order to solve these problems. In this article, an electroencephalogram- (EEG-) based scheme that is able to automatically classify sleep stages is proposed. Firstly, EEG data are preprocessed to remove artifacts, extract features, and normalization. Secondly, the normalized features and other context information are stored using an ontology-based model (OBM). Thirdly, an improved method of self-adaptive correlation analysis is designed to select the most effective EEG features. Based on these EEG features and weighting features analysis, the improved random forest (RF) is considered as the classifier to achieve the classification of sleep stages. To investigate the classification ability of the proposed method, several sets of experiments are designed and conducted to classify the sleep stages into two, three, four, and five states. The accuracy of five-state classification is 89.37%, which is improved compared to the accuracy using unimproved RF (84.37%) or previously reported classifiers. In addition, a set of controlled experiments is executed to verify the effect of the number of sleep segments (epochs) on the classification, and the results demonstrate that the proposed scheme is less affected by the sleep segments.
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49
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Chen J, Wang H, Hua C. Assessment of driver drowsiness using electroencephalogram signals based on multiple functional brain networks. Int J Psychophysiol 2018; 133:120-130. [PMID: 30081067 DOI: 10.1016/j.ijpsycho.2018.07.476] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 07/09/2018] [Accepted: 07/31/2018] [Indexed: 12/22/2022]
Abstract
This paper proposes a comprehensive approach to explore whether functional brain network (FBN) changes from the alert state to the drowsy state and to find out ideal neurophysiology indicators able to detect driver drowsiness in terms of FBN. A driving simulation experiment consisting of two driving tasks is designed and conducted using fifteen participant drivers. Collected EEG signals are then decomposed into multiple frequency bands by wavelet packet transform (WPT). Based on this, two novel FBN approaches, synchronization likelihood (SL) and minimum spanning tree (MST) are combined and applied to feature recognition and classification system. Unlike other methods, our approaches focus on the interaction and correlation between different brain regions. Statistical analysis of network features indicates that the difference between alert state and drowsy state are significant and further confirmed that brain network configuration should be related to drowsiness. For classification, these brain network features are selected and then fed into four classifiers considered namely Support Vector Machines (SVM), K Nearest Neighbors classifier (KNN), Logistic Regression (LR) and Decision Trees (DT). It is found that combining MST method and SL method is actually increasing the classification accuracy with all classifiers considered in this work especially the KNN classifier from 95.4% to 98.6%. Moreover, KNN classifier also gives the highest precision of 98.3%, sensitivity of 98.8% and specificity of 98.9%. Thus this kind of methodology might be a useful tool for further understanding the neurophysiology mechanisms of driver drowsiness, and as a reference work for future studies or future 'systems'.
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Affiliation(s)
- Jichi Chen
- Department of Mechanical Engineering and Automation, Northeastern University, 110819 Shenyang, Liaoning, China
| | - Hong Wang
- Department of Mechanical Engineering and Automation, Northeastern University, 110819 Shenyang, Liaoning, China.
| | - Chengcheng Hua
- Department of Mechanical Engineering and Automation, Northeastern University, 110819 Shenyang, Liaoning, China
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50
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Alonso SG, de la Torre-Díez I, Hamrioui S, López-Coronado M, Barreno DC, Nozaleda LM, Franco M. Data Mining Algorithms and Techniques in Mental Health: A Systematic Review. J Med Syst 2018; 42:161. [PMID: 30030644 DOI: 10.1007/s10916-018-1018-2] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Accepted: 07/16/2018] [Indexed: 12/12/2022]
Abstract
Data Mining in medicine is an emerging field of great importance to provide a prognosis and deeper understanding of disease classification, specifically in Mental Health areas. The main objective of this paper is to present a review of the existing research works in the literature, referring to the techniques and algorithms of Data Mining in Mental Health, specifically in the most prevalent diseases such as: Dementia, Alzheimer, Schizophrenia and Depression. Academic databases that were used to perform the searches are Google Scholar, IEEE Xplore, PubMed, Science Direct, Scopus and Web of Science, taking into account as date of publication the last 10 years, from 2008 to the present. Several search criteria were established such as 'techniques' AND 'Data Mining' AND 'Mental Health', 'algorithms' AND 'Data Mining' AND 'dementia' AND 'schizophrenia' AND 'depression', etc. selecting the papers of greatest interest. A total of 211 articles were found related to techniques and algorithms of Data Mining applied to the main Mental Health diseases. 72 articles have been identified as relevant works of which 32% are Alzheimer's, 22% dementia, 24% depression, 14% schizophrenia and 8% bipolar disorders. Many of the papers show the prediction of risk factors in these diseases. From the review of the research articles analyzed, it can be said that use of Data Mining techniques applied to diseases such as dementia, schizophrenia, depression, etc. can be of great help to the clinical decision, diagnosis prediction and improve the patient's quality of life.
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Affiliation(s)
- Susel Góngora Alonso
- Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011, Valladolid, Spain
| | - Isabel de la Torre-Díez
- Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011, Valladolid, Spain.
| | - Sofiane Hamrioui
- Bretagne Loire and Nantes Universities, UMR 6164, IETR Polytech Nantes, Nantes, France
| | - Miguel López-Coronado
- Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011, Valladolid, Spain
| | - Diego Calvo Barreno
- Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011, Valladolid, Spain
| | - Lola Morón Nozaleda
- Nozaleda and Lafora Mental Health Clinic, C/ José Ortega Y Gasset, 44, 28006, Madrid, Spain
| | - Manuel Franco
- Psiquiatry Service, Hospital Zamora, Hernán Cortés, Zamora, Spain
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