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Xu X, Zhu G, Li B, Lin P, Li X, Wang Z. Automated diagnosis of schizophrenia based on spatial-temporal residual graph convolutional network. Biomed Eng Online 2024; 23:55. [PMID: 38886737 PMCID: PMC11181588 DOI: 10.1186/s12938-024-01250-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 05/31/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Schizophrenia (SZ), a psychiatric disorder for which there is no precise diagnosis, has had a serious impact on the quality of human life and social activities for many years. Therefore, an advanced approach for accurate treatment is required. NEW METHOD In this study, we provide a classification approach for SZ patients based on a spatial-temporal residual graph convolutional neural network (STRGCN). The model primarily collects spatial frequency features and temporal frequency features by spatial graph convolution and single-channel temporal convolution, respectively, and blends them both for the classification learning, in contrast to traditional approaches that only evaluate temporal frequency information in EEG and disregard spatial frequency features across brain regions. RESULTS We conducted extensive experiments on the publicly available dataset Zenodo and our own collected dataset. The classification accuracy of the two datasets on our proposed method reached 96.32% and 85.44%, respectively. In the experiment, the dataset using delta has the best classification performance in the sub-bands. COMPARISON WITH EXISTING METHODS Other methods mainly rely on deep learning models dominated by convolutional neural networks and long and short time memory networks, lacking exploration of the functional connections between channels. In contrast, the present method can treat the EEG signal as a graph and integrate and analyze the temporal frequency and spatial frequency features in the EEG signal. CONCLUSION We provide an approach to not only performs better than other classic machine learning and deep learning algorithms on the dataset we used in diagnosing schizophrenia, but also understand the effects of schizophrenia on brain network features.
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Affiliation(s)
- Xinyi Xu
- College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Geng Zhu
- College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Bin Li
- Shanghai Yangpu Mental Health Center, Shanghai, China
| | - Ping Lin
- College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Xiaoou Li
- College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China.
- Shanghai Yangpu Mental Health Center, Shanghai, China.
| | - Zhen Wang
- College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China.
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2
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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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3
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Brookshire G, Kasper J, Blauch NM, Wu YC, Glatt R, Merrill DA, Gerrol S, Yoder KJ, Quirk C, Lucero C. Data leakage in deep learning studies of translational EEG. Front Neurosci 2024; 18:1373515. [PMID: 38765672 PMCID: PMC11099244 DOI: 10.3389/fnins.2024.1373515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 04/04/2024] [Indexed: 05/22/2024] Open
Abstract
A growing number of studies apply deep neural networks (DNNs) to recordings of human electroencephalography (EEG) to identify a range of disorders. In many studies, EEG recordings are split into segments, and each segment is randomly assigned to the training or test set. As a consequence, data from individual subjects appears in both the training and the test set. Could high test-set accuracy reflect data leakage from subject-specific patterns in the data, rather than patterns that identify a disease? We address this question by testing the performance of DNN classifiers using segment-based holdout (in which segments from one subject can appear in both the training and test set), and comparing this to their performance using subject-based holdout (where all segments from one subject appear exclusively in either the training set or the test set). In two datasets (one classifying Alzheimer's disease, and the other classifying epileptic seizures), we find that performance on previously-unseen subjects is strongly overestimated when models are trained using segment-based holdout. Finally, we survey the literature and find that the majority of translational DNN-EEG studies use segment-based holdout. Most published DNN-EEG studies may dramatically overestimate their classification performance on new subjects.
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Affiliation(s)
| | - Jake Kasper
- SPARK Neuro Inc., New York, NY, United States
| | - Nicholas M. Blauch
- SPARK Neuro Inc., New York, NY, United States
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, United States
| | | | - Ryan Glatt
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, United States
| | - David A. Merrill
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, United States
- Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, CA, United States
- Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA, United States
| | | | | | - Colin Quirk
- SPARK Neuro Inc., New York, NY, United States
| | - Ché Lucero
- SPARK Neuro Inc., New York, NY, United States
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4
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Ye H, Chen M, Feng G. Research on Fatigue Driving Detection Technology Based on CA-ACGAN. Brain Sci 2024; 14:436. [PMID: 38790415 PMCID: PMC11118024 DOI: 10.3390/brainsci14050436] [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: 03/27/2024] [Revised: 04/13/2024] [Accepted: 04/20/2024] [Indexed: 05/26/2024] Open
Abstract
Driver fatigue represents a significant peril to global traffic safety, necessitating the advancement of potent fatigue monitoring methodologies to bolster road safety. This research introduces a conditional generative adversarial network with a classification head that integrates convolutional and attention mechanisms (CA-ACGAN) designed for the precise identification of fatigue driving states through the analysis of electroencephalography (EEG) signals. First, this study constructed a 4D feature data model capable of mirroring drivers' fatigue state, meticulously analyzing the EEG signals' frequency, spatial, and temporal dimensions. Following this, we present the CA-ACGAN framework, a novel integration of attention schemes, the bottleneck residual block, and the Transformer element. This integration was designed to refine the processing of EEG signals significantly. In utilizing a conditional generative adversarial network equipped with a classification header, the framework aims to distinguish fatigue states effectively. Moreover, it addresses the scarcity of authentic data through the generation of superior-quality synthetic data. Empirical outcomes illustrate that the CA-ACGAN model surpasses various extant methods in the fatigue detection endeavor on the SEED-VIG public dataset. Moreover, juxtaposed with leading-edge GAN models, our model exhibits an efficacy in in producing high-quality data that is clearly superior. This investigation confirms the CA-ACGAN model's utility in fatigue driving identification and suggests fresh perspectives for deep learning applications in time series data generation and processing.
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Affiliation(s)
| | - Ming Chen
- College of Information, Shanghai Ocean University, No. 999 Huchenghuan Road, Shanghai 201306, China
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5
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Weisenburger RL, Mullarkey MC, Labrada J, Labrousse D, Yang MY, MacPherson AH, Hsu KJ, Ugail H, Shumake J, Beevers CG. Conversational assessment using artificial intelligence is as clinically useful as depression scales and preferred by users. J Affect Disord 2024; 351:489-498. [PMID: 38290584 DOI: 10.1016/j.jad.2024.01.212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 01/15/2024] [Accepted: 01/22/2024] [Indexed: 02/01/2024]
Abstract
BACKGROUND Depression is prevalent, chronic, and burdensome. Due to limited screening access, depression often remains undiagnosed. Artificial intelligence (AI) models based on spoken responses to interview questions may offer an effective, efficient alternative to other screening methods. OBJECTIVE The primary aim was to use a demographically diverse sample to validate an AI model, previously trained on human-administered interviews, on novel bot-administered interviews, and to check for algorithmic biases related to age, sex, race, and ethnicity. METHODS Using the Aiberry app, adults recruited via social media (N = 393) completed a brief bot-administered interview and a depression self-report form. An AI model was used to predict form scores based on interview responses alone. For all meaningful discrepancies between model inference and form score, clinicians performed a masked review to determine which one they preferred. RESULTS There was strong concurrent validity between the model predictions and raw self-report scores (r = 0.73, MAE = 3.3). 90 % of AI predictions either agreed with self-report or with clinical expert opinion when AI contradicted self-report. There was no differential model performance across age, sex, race, or ethnicity. LIMITATIONS Limitations include access restrictions (English-speaking ability and access to smartphone or computer with broadband internet) and potential self-selection of participants more favorably predisposed toward AI technology. CONCLUSION The Aiberry model made accurate predictions of depression severity based on remotely collected spoken responses to a bot-administered interview. This study shows promising results for the use of AI as a mental health screening tool on par with self-report measures.
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Affiliation(s)
- Rachel L Weisenburger
- Department of Psychology and Institute for Mental Health Research, The University of Texas at Austin, United States of America.
| | | | | | - Daniel Labrousse
- Department of Psychiatry, Georgetown University Medical Center, United States of America
| | - Michelle Y Yang
- Department of Psychiatry, Georgetown University Medical Center, United States of America
| | - Allison Huff MacPherson
- Department of Family and Community Medicine, College of Medicine, University of Arizona, United States of America
| | - Kean J Hsu
- Department of Psychiatry, Georgetown University Medical Center, United States of America; Department of Psychology, National University of Singapore, Singapore
| | - Hassan Ugail
- Centre for Visual Computing, University of Bradford, United Kingdom of Great Britain and Northern Ireland
| | | | - Christopher G Beevers
- Department of Psychology and Institute for Mental Health Research, The University of Texas at Austin, United States of America
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6
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Liu W, Jia K, Wang Z. Graph-based EEG approach for depression prediction: integrating time-frequency complexity and spatial topology. Front Neurosci 2024; 18:1367212. [PMID: 38633266 PMCID: PMC11022962 DOI: 10.3389/fnins.2024.1367212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 03/11/2024] [Indexed: 04/19/2024] Open
Abstract
Depression has become the prevailing global mental health concern. The accuracy of traditional depression diagnosis methods faces challenges due to diverse factors, making primary identification a complex task. Thus, the imperative lies in developing a method that fulfills objectivity and effectiveness criteria for depression identification. Current research underscores notable disparities in brain activity between individuals with depression and those without. The Electroencephalogram (EEG), as a biologically reflective and easily accessible signal, is widely used to diagnose depression. This article introduces an innovative depression prediction strategy that merges time-frequency complexity and electrode spatial topology to aid in depression diagnosis. Initially, time-frequency complexity and temporal features of the EEG signal are extracted to generate node features for a graph convolutional network. Subsequently, leveraging channel correlation, the brain network adjacency matrix is employed and calculated. The final depression classification is achieved by training and validating a graph convolutional network with graph node features and a brain network adjacency matrix based on channel correlation. The proposed strategy has been validated using two publicly available EEG datasets, MODMA and PRED+CT, achieving notable accuracy rates of 98.30 and 96.51%, respectively. These outcomes affirm the reliability and utility of our proposed strategy in predicting depression using EEG signals. Additionally, the findings substantiate the effectiveness of EEG time-frequency complexity characteristics as valuable biomarkers for depression prediction.
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Affiliation(s)
- Wei Liu
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
- Beijing Laboratory of Advanced Information Networks, Beijing, China
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China
| | - Kebin Jia
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
- Beijing Laboratory of Advanced Information Networks, Beijing, China
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, China
| | - Zhuozheng Wang
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
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7
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He Z, Chen L, Xu J, Lv H, Zhou RN, Hu J, Chen Y, Gao Y. Unified Convolutional Sparse Transformer for Disease Diagnosis, Monitoring, Drug Development, and Therapeutic Effect Prediction from EEG Raw Data. BIOLOGY 2024; 13:203. [PMID: 38666815 PMCID: PMC11048286 DOI: 10.3390/biology13040203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 03/13/2024] [Accepted: 03/19/2024] [Indexed: 04/28/2024]
Abstract
Electroencephalogram (EEG) analysis plays an indispensable role across contemporary medical applications, which encompasses diagnosis, monitoring, drug discovery, and therapeutic assessment. This work puts forth an end-to-end deep learning framework that is uniquely tailored for versatile EEG analysis tasks by directly operating on raw waveform inputs. It aims to address the challenges of manual feature engineering and the neglect of spatial interrelationships in existing methodologies. Specifically, a spatial channel attention module is introduced to emphasize the critical inter-channel dependencies in EEG signals through channel statistics aggregation and multi-layer perceptron operations. Furthermore, a sparse transformer encoder is used to leverage selective sparse attention in order to efficiently process long EEG sequences while reducing computational complexity. Distilling convolutional layers further concatenates the temporal features and retains only the salient patterns. As it was rigorously evaluated on key EEG datasets, our model consistently accomplished a superior performance over the current approaches in detection and classification assignments. By accounting for both spatial and temporal relationships in an end-to-end paradigm, this work facilitates a versatile, automated EEG understanding across diseases, subjects, and objectives through a singular yet customizable architecture. Extensive empirical validation and further architectural refinement may promote broader clinical adoption prospects.
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Affiliation(s)
- Zhengda He
- The Department of Computer Science and Technology, Nanjing University, Nanjing 210023, China;
- Laboratory of Molecular Design and Drug Discovery, China Pharmaceutical University, Nanjing 211198, China; (L.C.); (J.X.)
| | - Linjie Chen
- Laboratory of Molecular Design and Drug Discovery, China Pharmaceutical University, Nanjing 211198, China; (L.C.); (J.X.)
| | - Jiaying Xu
- Laboratory of Molecular Design and Drug Discovery, China Pharmaceutical University, Nanjing 211198, China; (L.C.); (J.X.)
| | - Hao Lv
- Laboratory of Molecular Design and Drug Discovery, China Pharmaceutical University, Nanjing 211198, China; (L.C.); (J.X.)
| | - Rui-ning Zhou
- Laboratory of Molecular Design and Drug Discovery, China Pharmaceutical University, Nanjing 211198, China; (L.C.); (J.X.)
| | - Jianhua Hu
- Laboratory of Molecular Design and Drug Discovery, China Pharmaceutical University, Nanjing 211198, China; (L.C.); (J.X.)
| | - Yadong Chen
- Laboratory of Molecular Design and Drug Discovery, China Pharmaceutical University, Nanjing 211198, China; (L.C.); (J.X.)
| | - Yang Gao
- The Department of Computer Science and Technology, Nanjing University, Nanjing 210023, China;
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8
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Aksoy G, Cattan G, Chakraborty S, Karabatak M. Quantum Machine-Based Decision Support System for the Detection of Schizophrenia from EEG Records. J Med Syst 2024; 48:29. [PMID: 38441727 PMCID: PMC10914922 DOI: 10.1007/s10916-024-02048-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 02/18/2024] [Indexed: 03/07/2024]
Abstract
Schizophrenia is a serious chronic mental disorder that significantly affects daily life. Electroencephalography (EEG), a method used to measure mental activities in the brain, is among the techniques employed in the diagnosis of schizophrenia. The symptoms of the disease typically begin in childhood and become more pronounced as one grows older. However, it can be managed with specific treatments. Computer-aided methods can be used to achieve an early diagnosis of this illness. In this study, various machine learning algorithms and the emerging technology of quantum-based machine learning algorithm were used to detect schizophrenia using EEG signals. The principal component analysis (PCA) method was applied to process the obtained data in quantum systems. The data, which were reduced in dimensionality, were transformed into qubit form using various feature maps and provided as input to the Quantum Support Vector Machine (QSVM) algorithm. Thus, the QSVM algorithm was applied using different qubit numbers and different circuits in addition to classical machine learning algorithms. All analyses were conducted in the simulator environment of the IBM Quantum Platform. In the classification of this EEG dataset, it is evident that the QSVM algorithm demonstrated superior performance with a 100% success rate when using Pauli X and Pauli Z feature maps. This study serves as proof that quantum machine learning algorithms can be effectively utilized in the field of healthcare.
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Affiliation(s)
- Gamzepelin Aksoy
- Department of Software Engineering, Firat University, Elazig, Türkiye.
| | | | - Subrata Chakraborty
- School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW, 2351, Australia
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, 2007, Australia
- Griffith Business School, Griffith University, Brisbane, QLD, 4111, Australia
| | - Murat Karabatak
- Department of Software Engineering, Firat University, Elazig, Türkiye
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9
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Mohammadi Y, Kafraj MS, Graversen C, Moradi MH. Decreased Resting-State Alpha Self-Synchronization in Depressive Disorder. Clin EEG Neurosci 2024; 55:185-191. [PMID: 36945785 DOI: 10.1177/15500594231163958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
Background. Depression disorder has been associated with altered oscillatory brain activity. The common methods to quantify oscillatory activity are Fourier and wavelet transforms. Both methods have difficulties distinguishing synchronized oscillatory activity from nonrhythmic and large-amplitude artifacts. Here we proposed a method called self-synchronization index (SSI) to quantify synchronized oscillatory activities in neural data. The method considers temporal characteristics of neural oscillations, amplitude, and cycles, to estimate the synchronization value for a specific frequency band. Method. The recorded electroencephalography (EEG) data of 45 depressed and 55 healthy individuals were used. The SSI method was applied to each EEG electrode filtered in the alpha frequency band (8-13 Hz). The multiple linear regression model was used to predict depression severity (Beck Depression Inventory-II scores) using alpha SSI values. Results. Patients with severe depression showed a lower alpha SSI than those with moderate depression and healthy controls in all brain regions. Moreover, the alpha SSI values negatively correlated with depression severity in all brain regions. The regression model showed a significant performance of depression severity prediction using alpha SSI. Conclusion. The findings support the SSI measure as a powerful tool for quantifying synchronous oscillatory activity. The data examined in this article support the idea that there is a strong link between the synchronization of alpha oscillatory neural activities and the level of depression. These findings yielded an objective and quantitative depression severity prediction.
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Affiliation(s)
- Yousef Mohammadi
- Integrative Neuroscience, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Mohadeseh Shafiei Kafraj
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Carina Graversen
- Integrative Neuroscience, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
- Department of Health Science and Technology, Integrative Neuroscience Group, Center for Neuroplasticity and Pain (CNAP), Aalborg University, Aalborg, Denmark
| | - Mohammad Hassan Moradi
- Biomedical Engineering Department, Amirkabir University of Technology, Tehran, Islamic Republic of Iran
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10
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Wang Z, Hu C, Liu W, Zhou X, Zhao X. EEG-based high-performance depression state recognition. Front Neurosci 2024; 17:1301214. [PMID: 38371369 PMCID: PMC10871719 DOI: 10.3389/fnins.2023.1301214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 12/14/2023] [Indexed: 02/20/2024] Open
Abstract
Depression is a global disease that is harmful to people. Traditional identification methods based on various scales are not objective and accurate enough. Electroencephalogram (EEG) contains abundant physiological information, which makes it a new research direction to identify depression state. However, most EEG-based algorithms only extract the original EEG features and ignore the complex spatiotemporal information interactions, which will reduce performance. Thus, a more accurate and objective method for depression identification is urgently needed. In this work, we propose a novel depression identification model: W-GCN-GRU. In our proposed method, we censored six sensitive features based on Spearman's rank correlation coefficient and assigned different weight coefficients to each sensitive feature by AUC for the weighted fusion of sensitive features. In particular, we use the GCN and GRU cascade networks based on weighted sensitive features as depression recognition models. For the GCN, we creatively took the brain function network based on the correlation coefficient matrix as the adjacency matrix input and the weighted fused sensitive features were used as the node feature matrix input. Our proposed model performed well on our self-collected dataset and the MODMA datasets with a accuracy of 94.72%, outperforming other methods. Our findings showed that feature dimensionality reduction, weighted fusion, and EEG spatial information all had great effects on depression recognition.
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Affiliation(s)
- Zhuozheng Wang
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Chenyang Hu
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Wei Liu
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Xiaofan Zhou
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Xixi Zhao
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
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11
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Bertl M, Bignoumba N, Ross P, Yahia SB, Draheim D. Evaluation of deep learning-based depression detection using medical claims data. Artif Intell Med 2024; 147:102745. [PMID: 38184352 DOI: 10.1016/j.artmed.2023.102745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 12/01/2023] [Accepted: 12/01/2023] [Indexed: 01/08/2024]
Abstract
Human accuracy in diagnosing psychiatric disorders is still low. Even though digitizing health care leads to more and more data, the successful adoption of AI-based digital decision support (DDSS) is rare. One reason is that AI algorithms are often not evaluated based on large, real-world data. This research shows the potential of using deep learning on the medical claims data of 812,853 people between 2018 and 2022, with 26,973,943 ICD-10-coded diseases, to predict depression (F32 and F33 ICD-10 codes). The dataset used represents almost the entire adult population of Estonia. Based on these data, to show the critical importance of the underlying temporal properties of the data for the detection of depression, we evaluate the performance of non-sequential models (LR, FNN), sequential models (LSTM, CNN-LSTM) and the sequential model with a decay factor (GRU-Δt, GRU-decay). Furthermore, since explainability is necessary for the medical domain, we combine a self-attention model with the GRU decay and evaluate its performance. We named this combination Att-GRU-decay. After extensive empirical experimentation, our model (Att-GRU-decay), with an AUC score of 0.990, an AUPRC score of 0.974, a specificity of 0.999 and a sensitivity of 0.944, proved to be the most accurate. The results of our novel Att-GRU-decay model outperform the current state of the art, demonstrating the potential usefulness of deep learning algorithms for DDSS development. We further expand this by describing a possible application scenario of the proposed algorithm for depression screening in a general practitioner (GP) setting-not only to decrease healthcare costs, but also to improve the quality of care and ultimately decrease people's suffering.
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Affiliation(s)
- Markus Bertl
- Department of Health Technologies, Tallinn University of Technology, Akadeemia Tee 15A, Tallinn, 12618, Estonia.
| | - Nzamba Bignoumba
- Department of Software Science, Tallinn University of Technology, Akadeemia Tee 15A, Tallinn, 12618, Estonia
| | - Peeter Ross
- Department of Health Technologies, Tallinn University of Technology, Akadeemia Tee 15A, Tallinn, 12618, Estonia; Department of Research, East-Tallinn Central Hospital, Ravi 18, Tallinn, 10138, Estonia
| | - Sadok Ben Yahia
- Department of Software Science, Tallinn University of Technology, Akadeemia Tee 15A, Tallinn, 12618, Estonia; University of Southern Denmark, Alsion 2, Sønderborg, 6400, Denmark
| | - Dirk Draheim
- Information Systems Group, Tallinn University of Technology, Akadeemia Tee 15A, Tallinn, 12618, Estonia
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12
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Wang HG, Meng QH, Jin LC, Hou HR. AMGCN-L: an adaptive multi-time-window graph convolutional network with long-short-term memory for depression detection. J Neural Eng 2023; 20:056038. [PMID: 37844566 DOI: 10.1088/1741-2552/ad038b] [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: 06/02/2023] [Accepted: 10/16/2023] [Indexed: 10/18/2023]
Abstract
Objective.Depression is a common chronic mental disorder characterized by high rates of prevalence, recurrence, suicide, and disability as well as heavy disease burden. An accurate diagnosis of depression is a prerequisite for treatment. However, existing questionnaire-based diagnostic methods are limited by the innate subjectivity of medical practitioners and subjects. In the search for a more objective diagnostic methods for depression, researchers have recently started to use deep learning approaches.Approach.In this work, a deep-learning network, named adaptively multi-time-window graph convolutional network (GCN) with long-short-term memory (LSTM) (i.e. AMGCN-L), is proposed. This network can automatically categorize depressed and non-depressed people by testing for the existence of inherent brain functional connectivity and spatiotemporal features contained in electroencephalogram (EEG) signals. AMGCN-L is mainly composed of two sub-networks: the first sub-network is an adaptive multi-time-window graph generation block with which adjacency matrices that contain brain functional connectivity on different time periods are adaptively designed. The second sub-network consists of GCN and LSTM, which are used to fully extract the innate spatial and temporal features of EEG signals, respectively.Main results.Two public datasets, namely the patient repository for EEG data and computational tools, and the multi-modal open dataset for mental-disorder analysis, were used to test the performance of the proposed network; the depression recognition accuracies achieved in both datasets (using tenfold cross-validation) were 90.38% and 90.57%, respectively.Significance.This work demonstrates that GCN and LSTM have eminent effects on spatial and temporal feature extraction, respectively, suggesting that the exploration of brain connectivity and the exploitation of spatiotemporal features benefit the detection of depression. Moreover, the proposed method provides effective support and supplement for the detection of clinical depression and later treatment procedures.
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Affiliation(s)
- Han-Guang Wang
- Tianjin University, Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information, Tianjin, People's Republic of China
| | - Qing-Hao Meng
- Tianjin University, Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information, Tianjin, People's Republic of China
| | - Li-Cheng Jin
- Tianjin University, Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information, Tianjin, People's Republic of China
| | - Hui-Rang Hou
- Tianjin University, Tianjin Key Laboratory of Process Measurement and Control, Institute of Robotics and Autonomous Systems, School of Electrical and Information, Tianjin, People's Republic of China
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13
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Xu Y, Zhong H, Ying S, Liu W, Chen G, Luo X, Li G. Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:8639. [PMID: 37896732 PMCID: PMC10611358 DOI: 10.3390/s23208639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 10/10/2023] [Accepted: 10/18/2023] [Indexed: 10/29/2023]
Abstract
Depressive disorder (DD) has become one of the most common mental diseases, seriously endangering both the affected person's psychological and physical health. Nowadays, a DD diagnosis mainly relies on the experience of clinical psychiatrists and subjective scales, lacking objective, accurate, practical, and automatic diagnosis technologies. Recently, electroencephalogram (EEG) signals have been widely applied for DD diagnosis, but mainly with high-density EEG, which can severely limit the efficiency of the EEG data acquisition and reduce the practicability of diagnostic techniques. The current study attempts to achieve accurate and practical DD diagnoses based on combining frontal six-channel electroencephalogram (EEG) signals and deep learning models. To this end, 10 min clinical resting-state EEG signals were collected from 41 DD patients and 34 healthy controls (HCs). Two deep learning models, multi-resolution convolutional neural network (MRCNN) combined with long short-term memory (LSTM) (named MRCNN-LSTM) and MRCNN combined with residual squeeze and excitation (RSE) (named MRCNN-RSE), were proposed for DD recognition. The results of this study showed that the higher EEG frequency band obtained the better classification performance for DD diagnosis. The MRCNN-RSE model achieved the highest classification accuracy of 98.48 ± 0.22% with 8-30 Hz EEG signals. These findings indicated that the proposed analytical framework can provide an accurate and practical strategy for DD diagnosis, as well as essential theoretical and technical support for the treatment and efficacy evaluation of DD.
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Affiliation(s)
- Yanting Xu
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China; (Y.X.); (S.Y.)
| | - Hongyang Zhong
- College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China; (H.Z.); (W.L.); (G.C.)
| | - Shangyan Ying
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China; (Y.X.); (S.Y.)
| | - Wei Liu
- College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China; (H.Z.); (W.L.); (G.C.)
| | - Guibin Chen
- College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China; (H.Z.); (W.L.); (G.C.)
| | - Xiaodong Luo
- The Second Hospital of Jinhua, Jinhua 321016, China
| | - Gang Li
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, China
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14
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Parsa M, Rad HY, Vaezi H, Hossein-Zadeh GA, Setarehdan SK, Rostami R, Rostami H, Vahabie AH. EEG-based classification of individuals with neuropsychiatric disorders using deep neural networks: A systematic review of current status and future directions. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107683. [PMID: 37406421 DOI: 10.1016/j.cmpb.2023.107683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 05/23/2023] [Accepted: 06/18/2023] [Indexed: 07/07/2023]
Abstract
The use of deep neural networks for electroencephalogram (EEG) classification has rapidly progressed and gained popularity in recent years, but automatic feature extraction from EEG signals remains a challenging task. The classification of neuropsychiatric disorders demands the extraction of neuro-markers for use in automated EEG classification. Numerous advanced deep learning algorithms can be used for this purpose. In this article, we present a comprehensive review of the main factors and parameters that affect the performance of deep neural networks in classifying different neuropsychiatric disorders using EEG signals. We also analyze the EEG features used for improving classification performance. Our analysis includes 82 scientific journal papers that applied deep neural networks for subject-wise classification based on EEG signals. We extracted information on the EEG dataset and types of disorders, deep neural network structures, performance, and hyperparameters. The results show that most studies have focused on clinical classification, achieving an average accuracy of 91.83 ± 7.34, with convolutional neural networks (CNNs) being the most frequently used network architecture and resting-state EEG signals being the most commonly used data type. Additionally, the review reveals that depression (N = 18), Alzheimer's (N = 11), and schizophrenia (N = 11) were studied more frequently than other types of neuropsychiatric disorders. Our review provides insight into the performance of deep neural networks in EEG classification and highlights the importance of EEG feature extraction in improving classification accuracy. By identifying the main factors and parameters that affect deep neural network performance in EEG classification, our review can guide future research in this area. We hope that our findings will encourage further exploration of deep learning methods for EEG classification and contribute to the development of more accurate and effective methods for diagnosing and monitoring neuropsychiatric disorders using EEG signals.
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Affiliation(s)
- Mohsen Parsa
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, North Kargar St., P.O. Box 14395/515, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences, Artesh Highway, P.O. Box 19568-36484, Tehran, Iran
| | - Habib Yousefi Rad
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, North Kargar St., P.O. Box 14395/515, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences, Artesh Highway, P.O. Box 19568-36484, Tehran, Iran
| | - Hadi Vaezi
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, North Kargar St., P.O. Box 14395/515, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences, Artesh Highway, P.O. Box 19568-36484, Tehran, Iran
| | - Gholam-Ali Hossein-Zadeh
- Control and Intelligent Processing Center of Excellence, Faculty of Electrical and Computer Engineering, University of Tehran, North Kargar St., P.O. Box 14395/515, Tehran, Iran
| | - Seyed Kamaledin Setarehdan
- Control and Intelligent Processing Center of Excellence, Faculty of Electrical and Computer Engineering, University of Tehran, North Kargar St., P.O. Box 14395/515, Tehran, Iran
| | - Reza Rostami
- Faculty of Psychology and Education, University of Tehran, Jalal-Al-e-Ahmed, P.O. Box 14155-6456, Tehran, Iran
| | - Hana Rostami
- ACNC, Atieh Clinical Neuroscience Center, Valiasr St., P.O. Box 19697-13663, Tehran, Iran
| | - Abdol-Hossein Vahabie
- Faculty of Psychology and Education, University of Tehran, Jalal-Al-e-Ahmed, P.O. Box 14155-6456, Tehran, Iran; Cognitive Systems Laboratory, Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, P.O. Box 14395/515, Tehran, Iran; Pasargad Institute for Advanced Innovative Solutions (PIAIS), Tehran, Iran.
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15
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Shahabi MS, Shalbaf A, Rostami R. Prediction of response to repetitive transcranial magnetic stimulation for major depressive disorder using hybrid Convolutional recurrent neural networks and raw Electroencephalogram Signal. Cogn Neurodyn 2023; 17:909-920. [PMID: 37522037 PMCID: PMC10374518 DOI: 10.1007/s11571-022-09881-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 08/03/2022] [Accepted: 08/28/2022] [Indexed: 11/30/2022] Open
Abstract
Major Depressive Disorder (MDD) is a high prevalence disease that needs an effective and timely treatment to prevent its progress and additional costs. Repetitive Transcranial Magnetic Stimulation (rTMS) is an effective treatment option for MDD patients which uses strong magnetic pulses to stimulate specific regions of the brain. However, some patients do not respond to this treatment which causes the waste of multiple weeks as treatment time and clinical resources. Therefore developing an effective way for the prediction of response to the rTMS treatment of depression is necessary. In this work, we proposed a hybrid model created by pre-trained Convolutional Neural Networks (CNN) models and Bidirectional Long Short-Term Memory (BLSTM) cells to predict response to rTMS treatment from raw EEG signal. Three pre-trained CNN models named VGG16, InceptionResNetV2, and EffecientNetB0 were utilized as Transfer Learning (TL) models to construct hybrid TL-BLSTM models. Then an ensemble of these models was created using weighted majority voting which the weights were optimized by Differential Evolution (DE) optimization algorithm. Evaluation of these models shows the superior performance of the ensemble model by the accuracy of 98.51%, sensitivity of 98.64%, specificity of 98.36%, F1-score of 98.6%, and AUC of 98.5%. Therefore, the ensemble of the proposed hybrid convolutional recurrent networks can efficiently predict the treatment outcome of rTMS using raw EEG data.
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Affiliation(s)
- Mohsen Sadat Shahabi
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Reza Rostami
- Department of Psychology, University of Tehran, Tehran, Iran
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16
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Soni S, Seal A, Mohanty SK, Sakurai K. Electroencephalography signals-based sparse networks integration using a fuzzy ensemble technique for depression detection. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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17
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Khadidos AO, Alyoubi KH, Mahato S, Khadidos AO, Nandan Mohanty S. Machine Learning and Electroencephalogram Signal based Diagnosis of Dipression. Neurosci Lett 2023; 809:137313. [PMID: 37257682 DOI: 10.1016/j.neulet.2023.137313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 05/12/2023] [Accepted: 05/22/2023] [Indexed: 06/02/2023]
Abstract
Depression is a psychological condition which hampers day to day activity (Thinking, Feeling or Action). The early detection of this illness will help to save many lives because it is now recognized as a global problem which could even lead to suicide. Electroencephalogram (EEG) signals can be used to diagnose depression using machine learning techniques. The dataset studied is public dataset which consists of 30 healthy people and 34 depression patients. The methods used for detection of depression are Decision Tree, Random Forest, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional Long-Short Term Memory (Bi-LSTM), Gradient Boosting, Extreme Gradient Boosting (XGBoost) along with band power. Among Deep Learning techniques, CNN model got the highest accuracy with 98.13%, specificity of 99%, and sensitivity of 97% using band power features.
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Affiliation(s)
- Adil O Khadidos
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
| | - Khaled H Alyoubi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia; Department of Computer Science, College of Computers and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia.
| | - Shalini Mahato
- Department of Computer Science and Engineering, Indian Institute of Information Technology (IIIT), Ranchi, Jharkhand, India.
| | - Alaa O Khadidos
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
| | - Sachi Nandan Mohanty
- Department of Computer Science & Engineering, Vardhaman College of Engineering(Autonomous), Hyderabad, India.
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18
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Qayyum A, Razzak I, Tanveer M, Mazher M, Alhaqbani B. High-Density Electroencephalography and Speech Signal Based Deep Framework for Clinical Depression Diagnosis. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2587-2597. [PMID: 37028339 DOI: 10.1109/tcbb.2023.3257175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Depression is a mental disorder characterized by persistent depressed mood or loss of interest in performing activities, causing significant impairment in daily routine. Possible causes include psychological, biological, and social sources of distress. Clinical depression is the more-severe form of depression, also known as major depression or major depressive disorder. Recently, electroencephalography and speech signals have been used for early diagnosis of depression; however, they focus on moderate or severe depression. We have combined audio spectrogram and multiple frequencies of EEG signals to improve diagnostic performance. To do so, we have fused different levels of speech and EEG features to generate descriptive features and applied vision transformers and various pre-trained networks on the speech and EEG spectrum. We have conducted extensive experiments on Multimodal Open Dataset for Mental-disorder Analysis (MODMA) dataset, which showed significant improvement in performance in depression diagnosis (0.972, 0.973 and 0.973 precision, recall and F1 score respectively) for patients at the mild stage. Besides, we provided a web-based framework using Flask and provided the source code publicly.1.
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19
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Shahabi MS, Shalbaf A, Rostami R, Kazemi R. A convolutional recurrent neural network with attention for response prediction to repetitive transcranial magnetic stimulation in major depressive disorder. Sci Rep 2023; 13:10147. [PMID: 37349335 PMCID: PMC10287753 DOI: 10.1038/s41598-023-35545-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 05/19/2023] [Indexed: 06/24/2023] Open
Abstract
Prediction of response to Repetitive Transcranial Magnetic Stimulation (rTMS) can build a very effective treatment platform that helps Major Depressive Disorder (MDD) patients to receive timely treatment. We proposed a deep learning model powered up by state-of-the-art methods to classify responders (R) and non-responders (NR) to rTMS treatment. Pre-treatment Electro-Encephalogram (EEG) signal of public TDBRAIN dataset and 46 proprietary MDD subjects were utilized to create time-frequency representations using Continuous Wavelet Transform (CWT) to be fed into the two powerful pre-trained Convolutional Neural Networks (CNN) named VGG16 and EfficientNetB0. Equipping these Transfer Learning (TL) models with Bidirectional Long Short-Term Memory (BLSTM) and attention mechanism for the extraction of most discriminative spatiotemporal features from input images, can lead to superior performance in the prediction of rTMS treatment outcome. Five brain regions named Frontal, Central, Parietal, Temporal, and occipital were assessed and the highest evaluated performance in 46 proprietary MDD subjects was acquired for the Frontal region using the TL-LSTM-Attention model based on EfficientNetB0 with accuracy, sensitivity, specificity, and Area Under the Curve (AUC) of 97.1%, 97.3%, 97.0%, and 0.96 respectively. Additionally, to test the generalizability of the proposed models, these TL-BLSTM-Attention models were evaluated on a public dataset called TDBRAIN and the highest accuracy of 82.3%, the sensitivity of 80.2%, the specificity of 81.9% and the AUC of 0.83 were obtained. Therefore, advanced deep learning methods using a time-frequency representation of EEG signals from the frontal brain region and the convolutional recurrent neural networks equipped with the attention mechanism can construct an accurate platform for the prediction of response to the rTMS treatment.
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Affiliation(s)
- Mohsen Sadat Shahabi
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Reza Rostami
- Department of Psychology, University of Tehran, Tehran, Iran
| | - Reza Kazemi
- Department of Cognitive Psychology, Institute for Cognitive Science Studies, Tehran, Iran
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20
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Sakib N, Islam MK, Faruk T. Machine Learning Model for Computer-Aided Depression Screening among Young Adults Using Wireless EEG Headset. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:1701429. [PMID: 37293375 PMCID: PMC10247322 DOI: 10.1155/2023/1701429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Revised: 04/09/2023] [Accepted: 04/17/2023] [Indexed: 06/10/2023]
Abstract
Depression is a disorder that if not treated can hamper the quality of life. EEG has shown great promise in detecting depressed individuals from depression control individuals. It overcomes the limitations of traditional questionnaire-based methods. In this study, a machine learning-based method for detecting depression among young adults using EEG data recorded by the wireless headset is proposed. For this reason, EEG data has been recorded using an Emotiv Epoc+ headset. A total of 32 young adults participated and the PHQ9 screening tool was used to identify depressed participants. Features such as skewness, kurtosis, variance, Hjorth parameters, Shannon entropy, and Log energy entropy from 1 to 5 sec data filtered at different band frequencies were applied to KNN and SVM classifiers with different kernels. At AB band (8-30 Hz) frequency, 98.43 ± 0.15% accuracy was achieved by extracting Hjorth parameters, Shannon entropy, and Log energy entropy from 5 sec samples with a 5-fold CV using a KNN classifier. And with the same features and classifier overall accuracy = 98.10 ± 0.11, NPV = 0.977, precision = 0.984, sensitivity = 0.984, specificity = 0.976, and F1 score = 0.984 was achieved after splitting the data to 70/30 ratio for training and testing with 5-fold CV. From the findings, it can be concluded that EEG data from an Emotiv headset can be used to detect depression with the proposed method.
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Affiliation(s)
- Nazmus Sakib
- Department of Electrical and Electronic Engineering, Independent University Bangladesh (IUB), Dhaka, Bangladesh
- Biomedical Instrumentation and Signal Processing Lab (BISPL), Independent University Bangladesh (IUB), Dhaka, Bangladesh
| | - Md Kafiul Islam
- Department of Electrical and Electronic Engineering, Independent University Bangladesh (IUB), Dhaka, Bangladesh
- Biomedical Instrumentation and Signal Processing Lab (BISPL), Independent University Bangladesh (IUB), Dhaka, Bangladesh
| | - Tasnuva Faruk
- Biomedical Instrumentation and Signal Processing Lab (BISPL), Independent University Bangladesh (IUB), Dhaka, Bangladesh
- Department of Public Health, Independent University Bangladesh (IUB), Dhaka, Bangladesh
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21
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Ksibi A, Zakariah M, Menzli LJ, Saidani O, Almuqren L, Hanafieh RAM. Electroencephalography-Based Depression Detection Using Multiple Machine Learning Techniques. Diagnostics (Basel) 2023; 13:diagnostics13101779. [PMID: 37238263 DOI: 10.3390/diagnostics13101779] [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: 04/09/2023] [Revised: 04/28/2023] [Accepted: 05/08/2023] [Indexed: 05/28/2023] Open
Abstract
The growth of biomedical engineering has made depression diagnosis via electroencephalography (EEG) a trendy issue. The two significant challenges to this application are EEG signals' complexity and non-stationarity. Additionally, the effects caused by individual variances may hamper the generalization of detection systems. Given the association between EEG signals and particular demographics, such as gender and age, and the influences of these demographic characteristics on the incidence of depression, it would be preferable to include demographic factors during EEG modeling and depression detection. The main objective of this work is to develop an algorithm that can recognize depression patterns by studying EEG data. Following a multiband analysis of such signals, machine learning and deep learning techniques were used to detect depression patients automatically. EEG signal data are collected from the multi-modal open dataset MODMA and employed in studying mental diseases. The EEG dataset contains information from a traditional 128-electrode elastic cap and a cutting-edge wearable 3-electrode EEG collector for widespread applications. In this project, resting EEG readings of 128 channels are considered. According to CNN, training with 25 epoch iterations had a 97% accuracy rate. The patient's status has to be divided into two basic categories: major depressive disorder (MDD) and healthy control. Additional MDD include the following six classes: obsessive-compulsive disorders, addiction disorders, conditions brought on by trauma and stress, mood disorders, schizophrenia, and the anxiety disorders discussed in this paper are a few examples of mental illnesses. According to the study, a natural combination of EEG signals and demographic data is promising for the diagnosis of depression.
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Affiliation(s)
- Amel Ksibi
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
| | - Mohammed Zakariah
- Department of Computer Science, College of Computer and Information Sciences, Riyadh 11442, Saudi Arabia
| | - Leila Jamel Menzli
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
| | - Oumaima Saidani
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
| | - Latifah Almuqren
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
| | - Rosy Awny Mohamed Hanafieh
- Department of Computer Science, College of Computing in Al-Qunfudah, Umm Al-Qura University, Makkah 24382, Saudi Arabia
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22
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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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23
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Takemoto A, Aispuriete I, Niedra L, Dreimane LF. Differentiating depression using facial expressions in a virtual avatar communication system. Front Digit Health 2023; 5:1080023. [PMID: 36969955 PMCID: PMC10036904 DOI: 10.3389/fdgth.2023.1080023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 01/05/2023] [Indexed: 03/12/2023] Open
Abstract
Depression has a major effect on the quality of life. Thus, identifying an effective way to detect depression is important in the field of human-machine interaction. To examine whether a combination of a virtual avatar communication system and facial expression monitoring potentially classifies people as being with or without depression, this study consists of three research aims; 1) to understand the effect of different types of interviewers such as human and virtual avatars, on people with depression symptoms, 2) to clarify the effect of neutral conversation topics on facial expressions and emotions in people with depression symptoms, and 3) to compare verbal and non-verbal information between people with or without depression. In this study, twenty-seven participants—fifteen in the control group and twelve in the depression symptoms group—were recruited. They were asked to talk to a virtual avatar and human interviewers on both neutral and negative conversation topics and to score PANAS; meanwhile, facial expressions were recorded by a web camera. Facial expressions were analyzed by both manual and automatic analyses. In the manual analysis, three annotators counted gaze directions and reacting behaviors. On the other hand, automatic facial expression detection was conducted using OpenFace. The results of PANAS suggested that there was no significance between different interviewers’ types. Furthermore, in the control group, the frequency of look-downward was larger in negative conversation topics than in neutral conversation topics. The intensity of Dimpler was larger in the control group than in the depression symptoms group. Moreover, the intensity of Chin Raiser was larger in neutral conversation topics than in negative conversation topics in the depression symptoms group. However, in the control groups, there was no significance in the types of conversation topics. In conclusion, 1) there was no significance between human and virtual avatar interviewers in emotions, facial expressions, and eye gaze patterns, 2) neutral conversation topics induced less negative emotion in both the control and depression symptoms group, and 3) different facial expressions’ patterns between people with, or without depression, were observed in the virtual avatar communication system.
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Affiliation(s)
- Ayumi Takemoto
- Faculty of Computing, University of Latvia, Riga, Latvia
- Bioinformatics Laboratory, Riga Stradins University, Riga, Latvia
- Correspondence: Ayumi Takemoto
| | - Inese Aispuriete
- Faculty of Pedagogy, Psychology and the Arts, University of Latvia, Riga, Latvia
| | - Laima Niedra
- Faculty of Pedagogy, Psychology and the Arts, University of Latvia, Riga, Latvia
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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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25
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Yasin S, Othmani A, Raza I, Hussain SA. Machine learning based approaches for clinical and non-clinical depression recognition and depression relapse prediction using audiovisual and EEG modalities: A comprehensive review. Comput Biol Med 2023; 159:106741. [PMID: 37105109 DOI: 10.1016/j.compbiomed.2023.106741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 02/25/2023] [Accepted: 03/02/2023] [Indexed: 03/07/2023]
Abstract
Mental disorders are rapidly increasing each year and have become a major challenge affecting the social and financial well-being of individuals. There is a need for phenotypic characterization of psychiatric disorders with biomarkers to provide a rich signature for Major Depressive Disorder, improving the understanding of the pathophysiological mechanisms underlying these mental disorders. This comprehensive review focuses on depression and relapse detection modalities such as self-questionnaires, audiovisuals, and EEG, highlighting noteworthy publications in the last ten years. The article concentrates on the literature that adopts machine learning by audiovisual and EEG signals. It also outlines preprocessing, feature extraction, and public datasets for depression detection. The review concludes with recommendations that will help improve the reliability of developed models and the determinism of computational intelligence-based systems in psychiatry. To the best of our knowledge, this survey is the first comprehensive review on depression and relapse prediction by self-questionnaires, audiovisual, and EEG-based approaches. The findings of this review will serve as a useful and structured starting point for researchers studying clinical and non-clinical depression recognition and relapse through machine learning-based approaches.
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Affiliation(s)
- Sana Yasin
- Department of Computer Science, COMSATS University Islamabad, Lahore Campus Lahore, Pakistan; Department of Computer Science, University of Okara, Okara, Pakistan.
| | - Alice Othmani
- Université Paris-Est Créteil (UPEC), LISSI, Vitry sur Seine, 94400, France.
| | - Imran Raza
- Department of Computer Science, COMSATS University Islamabad, Lahore Campus Lahore, Pakistan.
| | - Syed Asad Hussain
- Department of Computer Science, COMSATS University Islamabad, Lahore Campus Lahore, Pakistan.
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26
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Exploring user experience and performance of a tedious task through human-agent relationship. Sci Rep 2023; 13:2995. [PMID: 36810767 PMCID: PMC9944929 DOI: 10.1038/s41598-023-29874-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 02/11/2023] [Indexed: 02/23/2023] Open
Abstract
Positive human-agent relationships can effectively improve human experience and performance in human-machine systems or environments. The characteristics of agents that enhance this relationship have garnered attention in human-agent or human-robot interactions. In this study, based on the rule of the persona effect, we study the effect of an agent's social cues on human-agent relationships and human performance. We constructed a tedious task in an immersive virtual environment, designing virtual partners with varying levels of human likeness and responsiveness. Human likeness encompassed appearance, sound, and behavior, while responsiveness referred to the way agents responded to humans. Based on the constructed environment, we present two studies to explore the effects of an agent's human likeness and responsiveness to agents on participants' performance and perception of human-agent relationships during the task. The results indicate that when participants work with an agent, its responsiveness attracts attention and induces positive feelings. Agents with responsiveness and appropriate social response strategies have a significant positive effect on human-agent relationships. These results shed some light on how to design virtual agents to improve user experience and performance in human-agent interactions.
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27
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Shusharina N, Yukhnenko D, Botman S, Sapunov V, Savinov V, Kamyshov G, Sayapin D, Voznyuk I. Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression. Diagnostics (Basel) 2023; 13:diagnostics13030573. [PMID: 36766678 PMCID: PMC9914271 DOI: 10.3390/diagnostics13030573] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 01/27/2023] [Accepted: 01/29/2023] [Indexed: 02/09/2023] Open
Abstract
This paper discusses the promising areas of research into machine learning applications for the prevention and correction of neurodegenerative and depressive disorders. These two groups of disorders are among the leading causes of decline in the quality of life in the world when estimated using disability-adjusted years. Despite decades of research, the development of new approaches for the assessment (especially pre-clinical) and correction of neurodegenerative diseases and depressive disorders remains among the priority areas of research in neurophysiology, psychology, genetics, and interdisciplinary medicine. Contemporary machine learning technologies and medical data infrastructure create new research opportunities. However, reaching a consensus on the application of new machine learning methods and their integration with the existing standards of care and assessment is still a challenge to overcome before the innovations could be widely introduced to clinics. The research on the development of clinical predictions and classification algorithms contributes towards creating a unified approach to the use of growing clinical data. This unified approach should integrate the requirements of medical professionals, researchers, and governmental regulators. In the current paper, the current state of research into neurodegenerative and depressive disorders is presented.
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Affiliation(s)
- Natalia Shusharina
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
- Correspondence:
| | - Denis Yukhnenko
- Department of Social Security and Humanitarian Technologies, N. I. Lobachevsky State University of Nizhny Novgorod, 603022 Nizhny Novgorod, Russia
| | - Stepan Botman
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
| | - Viktor Sapunov
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
| | - Vladimir Savinov
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
| | - Gleb Kamyshov
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
| | - Dmitry Sayapin
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
| | - Igor Voznyuk
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
- Department of Neurology, Pavlov First Saint Petersburg State Medical University, 197022 Saint Petersburg, Russia
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28
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Lin S, Wu Y, He L, Fang Y. Prediction of depressive symptoms onset and long-term trajectories in home-based older adults using machine learning techniques. Aging Ment Health 2023; 27:8-17. [PMID: 35118924 DOI: 10.1080/13607863.2022.2031868] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
OBJECTIVES Our aim was to explore the possibility of using machine learning (ML) in predicting the onset and trajectories of depressive symptom in home-based older adults over a 7-year period. METHODS Depressive symptom data (collected in the year 2011, 2013, 2015 and 2018) of home-based older Chinese (n = 2650) recruited in the China Health and Retirement Longitudinal Study (CHARLS) were included in the current analysis. The latent class growth modeling (LCGM) and growth mixture modeling (GMM) were used to classify different trajectory classes. Based on the identified trajectory patterns, three ML classification algorithms (i.e. gradient boosting decision tree, support vector machine and random forest) were evaluated with a 10-fold cross-validation procedure and a metric of the area under the receiver operating characteristic curve (AUC). RESULTS Four trajectories were identified for the depressive symptoms: no symptoms (63.9%), depressive symptoms onset {incident increasing symptoms [new-onset increasing (16.8%)], chronic symptoms [slowly decreasing (12.5%), persistent high (6.8%)]}. Among the analyzed baseline variables, the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10) score, cognition, sleep time, self-reported memory were the top five important predictors across all trajectories. The mean AUCs of the three predictive models had a range from 0.661 to 0.892. CONCLUSIONS ML techniques can be robust in predicting depressive symptom onset and trajectories over a 7-year period with easily accessible sociodemographic and health information. UNLABELLED Supplemental data for this article is available online at http://dx.doi.org/10.1080/13607863.2022.2031868.
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Affiliation(s)
- Shaowu Lin
- The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China.,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.,Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
| | - Yafei Wu
- The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China.,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.,Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
| | - Lingxiao He
- The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China.,Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
| | - Ya Fang
- The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China.,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.,Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, China
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29
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Lin S, Wu Y, Fang Y. A hybrid machine learning model of depression estimation in home-based older adults: a 7-year follow-up study. BMC Psychiatry 2022; 22:816. [PMID: 36544119 PMCID: PMC9768728 DOI: 10.1186/s12888-022-04439-4] [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: 07/19/2022] [Accepted: 11/30/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Our aim was to explore whether a two-step hybrid machine learning model has the potential to discover the onset of depression in home-based older adults. METHODS Depression data (collected in the year 2011, 2013, 2015 and 2018) of home-based older Chinese (n = 2,548) recruited in the China Health and Retirement Longitudinal Study were included in the current analysis. The long short-term memory network (LSTM) was applied to identify the risk factors of participants in 2015 utilizing the first 2 waves of data. Based on the identified predictors, three ML classification algorithms (i.e., gradient boosting decision tree, support vector machine and random forest) were evaluated with a 10-fold cross-validation procedure and a metric of the area under the receiver operating characteristic curve (AUROC) to estimate the depressive outcome. RESULTS Time-varying predictors of the depression were successfully identified by LSTM (mean squared error =0.8). The mean AUCs of the three predictive models had a range from 0.703 to 0.749. Among the prediction variables, self-reported health status, cognition, sleep time, self-reported memory and ADL (activities of daily living) disorder were the top five important variables. CONCLUSIONS A two-step hybrid model based on "LSTM+ML" framework can be robust in predicting depression over a 5-year period with easily accessible sociodemographic and health information.
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Affiliation(s)
- Shaowu Lin
- grid.12955.3a0000 0001 2264 7233The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102 China ,grid.12955.3a0000 0001 2264 7233National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361102 China ,grid.12955.3a0000 0001 2264 7233Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, 361102 China
| | - Yafei Wu
- grid.12955.3a0000 0001 2264 7233The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102 China ,grid.12955.3a0000 0001 2264 7233National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361102 China ,grid.12955.3a0000 0001 2264 7233Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, 361102 China
| | - Ya Fang
- The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, China. .,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361102, China. .,Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, 361102, China.
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30
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Nadeem A, Naveed M, Islam Satti M, Afzal H, Ahmad T, Kim KI. Depression Detection Based on Hybrid Deep Learning SSCL Framework Using Self-Attention Mechanism: An Application to Social Networking Data. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22249775. [PMID: 36560144 PMCID: PMC9782829 DOI: 10.3390/s22249775] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 11/30/2022] [Accepted: 12/08/2022] [Indexed: 06/01/2023]
Abstract
In today's world, mental health diseases have become highly prevalent, and depression is one of the mental health problems that has become widespread. According to WHO reports, depression is the second-leading cause of the global burden of diseases. In the proliferation of such issues, social media has proven to be a great platform for people to express themselves. Thus, a user's social media can speak a great deal about his/her emotional state and mental health. Considering the high pervasiveness of the disease, this paper presents a novel framework for depression detection from textual data, employing Natural Language Processing and deep learning techniques. For this purpose, a dataset consisting of tweets was created, which were then manually annotated by the domain experts to capture the implicit and explicit depression context. Two variations of the dataset were created, on having binary and one ternary labels, respectively. Ultimately, a deep-learning-based hybrid Sequence, Semantic, Context Learning (SSCL) classification framework with a self-attention mechanism is proposed that utilizes GloVe (pre-trained word embeddings) for feature extraction; LSTM and CNN were used to capture the sequence and semantics of tweets; finally, the GRUs and self-attention mechanism were used, which focus on contextual and implicit information in the tweets. The framework outperformed the existing techniques in detecting the explicit and implicit context, with an accuracy of 97.4 for binary labeled data and 82.9 for ternary labeled data. We further tested our proposed SSCL framework on unseen data (random tweets), for which an F1-score of 94.4 was achieved. Furthermore, in order to showcase the strengths of the proposed framework, we validated it on the "News Headline Data set" for sarcasm detection, considering a dataset from a different domain. It also outmatched the performance of existing techniques in cross-domain validation.
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Affiliation(s)
- Aleena Nadeem
- Department of Computer Software Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan
| | - Muhammad Naveed
- Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad 44000, Pakistan
| | - Muhammad Islam Satti
- Department of Computer Science, Faculty of Computing, Riphah International University, Islamabad 46000, Pakistan
| | - Hammad Afzal
- Department of Computer Software Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan
| | - Tanveer Ahmad
- Innovation Education and Research Center for On-Device AI Software (Bk21), Department of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Republic of Korea
| | - Ki-Il Kim
- Department of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Republic of Korea
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31
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Zitouni MS, Lih Oh S, Vicnesh J, Khandoker A, Acharya UR. Automated recognition of major depressive disorder from cardiovascular and respiratory physiological signals. Front Psychiatry 2022; 13:970993. [PMID: 36569627 PMCID: PMC9780587 DOI: 10.3389/fpsyt.2022.970993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 11/21/2022] [Indexed: 12/14/2022] Open
Abstract
Major Depressive Disorder (MDD) is a neurohormonal disorder that causes persistent negative thoughts, mood and feelings, often accompanied with suicidal ideation (SI). Current clinical diagnostic approaches are solely based on psychiatric interview questionnaires. Thus, a computational intelligence tool for the automated detection of MDD with and without suicidal ideation is presented in this study. Since MDD is proven to affect cardiovascular and respiratory systems, the aim of the study is to automatically identify the disorder severity in MDD patients using corresponding multi-modal physiological signals, including electrocardiogram (ECG), finger photoplethysmography (PPG) and respiratory signals (RSP). Data from 88 subjects were used in this study, out of which 25 were MDD patients without SI (MDDSI-), 18 MDD patients with SI (MDDSI+), and 45 normal subjects. Multi-modal physiological signals were acquired from each subject, including ECG, RSP, and PPG signals, and then pre-processed. Discrete wavelet transform (DWT) was applied to the signals, which were decomposed up to six levels, and then eleven nonlinear features were extracted. The features were ranked according to the analysis of variance test and Marginal Fisher Analysis was employed to reduce the feature set, after which the reduced features were ranked again to select the most discriminatory features. Support vector machine with polynomial radial basis function (SVM-RBF) as well as k-nearest neighbor (KNN) classifiers were used to classify the significant features. The performance of the classifiers was evaluated in a 10-fold cross validation scheme. The best performance achieved for the classification of MDDSI+ patients was up to 85.2%, by using selected features from the obtained multi-modal signals with SVM-RBF, while it was up to 96.6% for the detection of MDD patients against healthy subjects. This work is a step toward the utilization of automated tools in diagnostics and monitoring of MDD patients in a personalized and wearable healthcare system.
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Affiliation(s)
- M. Sami Zitouni
- College of Engineering & IT, University of Dubai, Dubai, United Arab Emirates
- Health Engineering Innovation Center, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Shu Lih Oh
- School of Engineering, Ngee Ann Polytechnic, Singapore, Singapore
| | - Jahmunah Vicnesh
- School of Engineering, Ngee Ann Polytechnic, Singapore, Singapore
| | - Ahsan Khandoker
- Health Engineering Innovation Center, Khalifa University, Abu Dhabi, United Arab Emirates
- Department of Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
| | - U. Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Singapore, Singapore
- Department Bioinformatics and Medical Engineering, Asia University, Taichung City, Taiwan
- International Research Organization for Advanced Science and Technology, Kumamoto University, Kumamoto, Japan
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore
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32
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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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Wu W, Ma L, Lian B, Cai W, Zhao X. Few-Electrode EEG from the Wearable Devices Using Domain Adaptation for Depression Detection. BIOSENSORS 2022; 12:1087. [PMID: 36551054 PMCID: PMC9775005 DOI: 10.3390/bios12121087] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 11/16/2022] [Accepted: 11/23/2022] [Indexed: 06/17/2023]
Abstract
Nowadays, major depressive disorder (MDD) has become a crucial mental disease that endangers human health. Good results have been achieved by electroencephalogram (EEG) signals in the detection of depression. However, EEG signals are time-varying, and the distributions of the different subjects' data are non-uniform, which poses a bad influence on depression detection. In this paper, the deep learning method with domain adaptation is applied to detect depression based on EEG signals. Firstly, the EEG signals are preprocessed and then transformed into pictures by two methods: the first one is to present the three channels of EEG separately in the same image, and the second one is the RGB synthesis of the three channels of EEG. Finally, the training and prediction are performed in the domain adaptation model. The results indicate that the domain adaptation model can effectively extract EEG features and obtain an average accuracy of 77.0 ± 9.7%. This paper proves that the domain adaptation method can effectively weaken the inherent differences of EEG signals, making the diagnosis of different users more accurate.
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Affiliation(s)
- Wei Wu
- School of Information Science and Engineering, NingboTech University, Ningbo 315100, China
- School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
| | - Longhua Ma
- School of Information Science and Engineering, NingboTech University, Ningbo 315100, China
| | - Bin Lian
- School of Information Science and Engineering, NingboTech University, Ningbo 315100, China
| | - Weiming Cai
- School of Information Science and Engineering, NingboTech University, Ningbo 315100, China
| | - Xianghong Zhao
- School of Information Science and Engineering, NingboTech University, Ningbo 315100, China
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Barua PD, Vicnesh J, Lih OS, Palmer EE, Yamakawa T, Kobayashi M, Acharya UR. Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review. Cogn Neurodyn 2022:1-22. [PMID: 36467993 PMCID: PMC9684805 DOI: 10.1007/s11571-022-09904-0] [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: 05/09/2022] [Revised: 09/26/2022] [Accepted: 10/17/2022] [Indexed: 11/24/2022] Open
Abstract
Epidemiological studies report high levels of anxiety and depression amongst adolescents. These psychiatric conditions and complex interplays of biological, social and environmental factors are important risk factors for suicidal behaviours and suicide, which show a peak in late adolescence and early adulthood. Although deaths by suicide have fallen globally in recent years, suicide deaths are increasing in some countries, such as the US. Suicide prevention is a challenging global public health problem. Currently, there aren't any validated clinical biomarkers for suicidal diagnosis, and traditional methods exhibit limitations. Artificial intelligence (AI) is budding in many fields, including in the diagnosis of medical conditions. This review paper summarizes recent studies (past 8 years) that employed AI tools for the automated detection of depression and/or anxiety disorder and discusses the limitations and effects of some modalities. The studies assert that AI tools produce promising results and could overcome the limitations of traditional diagnostic methods. Although using AI tools for suicidal ideation exhibits limitations, these are outweighed by the advantages. Thus, this review article also proposes extracting a fusion of features such as facial images, speech signals, and visual and clinical history features from deep models for the automated detection of depression and/or anxiety disorder in individuals, for future work. This may pave the way for the identification of individuals with suicidal thoughts.
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Affiliation(s)
- Prabal Datta Barua
- School of Management and Enterprise, University of Southern Queensland, Springfield, Australia
| | - Jahmunah Vicnesh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
| | - Oh Shu Lih
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
| | - Elizabeth Emma Palmer
- Discipline of Pediatric and Child Health, School of Clinical Medicine, University of New South Wales, Kensington, Australia
- Sydney Children’s Hospitals Network, Sydney, Australia
| | - Toshitaka Yamakawa
- Department of Computer Science and Electrical Engineering, Kumamoto University, Kumamoto, Japan
| | - Makiko Kobayashi
- Department of Computer Science and Electrical Engineering, Kumamoto University, Kumamoto, Japan
| | - Udyavara Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
- School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore
- Department of Bioinformatics and Medical Engineering, Asia University, Taizhong, Taiwan
- International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan
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35
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Anders C, Arnrich B. Wearable electroencephalography and multi-modal mental state classification: A systematic literature review. Comput Biol Med 2022; 150:106088. [PMID: 36137314 DOI: 10.1016/j.compbiomed.2022.106088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/10/2022] [Accepted: 09/03/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND Wearable multi-modal time-series classification applications outperform their best uni-modal counterparts and hold great promise. A modality that directly measures electrical correlates from the brain is electroencephalography. Due to varying noise sources, different key brain regions, key frequency bands, and signal characteristics like non-stationarity, techniques for data pre-processing and classification algorithms are task-dependent. METHOD Here, a systematic literature review on mental state classification for wearable electroencephalography is presented. Four search terms in different combinations were used for an in-title search. The search was executed on the 29th of June 2022, across Google Scholar, PubMed, IEEEXplore, and ScienceDirect. 76 most relevant publications were set into context as the current state-of-the-art in mental state time-series classification. RESULTS Pre-processing techniques, features, and time-series classification models were analyzed. Across publications, a window length of one second was mainly chosen for classification and spectral features were utilized the most. The achieved performance per time-series classification model is analyzed, finding linear discriminant analysis, decision trees, and k-nearest neighbors models outperform support-vector machines by a factor of up to 1.5. A historical analysis depicts future trends while under-reported aspects relevant to practical applications are discussed. CONCLUSIONS Five main conclusions are given, covering utilization of available area for electrode placement on the head, most often or scarcely utilized features and time-series classification model architectures, baseline reporting practices, as well as explainability and interpretability of Deep Learning. The importance of a 'test battery' assessing the influence of data pre-processing and multi-modality on time-series classification performance is emphasized.
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Affiliation(s)
- Christoph Anders
- Hasso Plattner Institute, University of Potsdam, Potsdam, 14482, Brandenburg, Germany.
| | - Bert Arnrich
- Hasso Plattner Institute, University of Potsdam, Potsdam, 14482, Brandenburg, Germany.
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36
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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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37
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Malhotra A, Jindal R. Deep learning techniques for suicide and depression detection from online social media: A scoping review. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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38
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Wang B, Kang Y, Huo D, Feng G, Zhang J, Li J. EEG diagnosis of depression based on multi-channel data fusion and clipping augmentation and convolutional neural network. Front Physiol 2022; 13:1029298. [PMID: 36338469 PMCID: PMC9632488 DOI: 10.3389/fphys.2022.1029298] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 09/23/2022] [Indexed: 09/29/2023] Open
Abstract
Depression is an undetectable mental disease. Most of the patients with depressive symptoms do not know that they are suffering from depression. Since the novel Coronavirus pandemic 2019, the number of patients with depression has increased rapidly. There are two kinds of traditional depression diagnosis. One is that professional psychiatrists make diagnosis results for patients, but it is not conducive to large-scale depression detection. Another is to use electroencephalography (EEG) to record neuronal activity. Then, the features of the EEG are extracted using manual or traditional machine learning methods to diagnose the state and type of depression. Although this method achieves good results, it does not fully utilize the multi-channel information of EEG. Aiming at this problem, an EEG diagnosis method for depression based on multi-channel data fusion cropping enhancement and convolutional neural network is proposed. First, the multi-channel EEG data are transformed into 2D images after multi-channel fusion (MCF) and multi-scale clipping (MSC) augmentation. Second, it is trained by a multi-channel convolutional neural network (MCNN). Finally, the trained model is loaded into the detection device to classify the input EEG signals. The experimental results show that the combination of MCF and MSC can make full use of the information contained in the single sensor records, and significantly improve the classification accuracy and clustering effect of depression diagnosis. The method has the advantages of low complexity and good robustness in signal processing and feature extraction, which is beneficial to the wide application of detection systems.
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Affiliation(s)
- Baiyang Wang
- School of Information Science and Engineering, Linyi University, Linyi, China
| | - Yuyun Kang
- School of Logistics, Linyi University, Linyi, China
| | - Dongyue Huo
- School of Information Science and Engineering, Linyi University, Linyi, China
| | - Guifang Feng
- School of Life Science, Linyi University, Linyi, China
- International College, Philippine Christian University, Manila, Philippines
| | - Jiawei Zhang
- Linyi Trade Logistics Science and Technology Industry Research Institute, Linyi, China
| | - Jiadong Li
- School of Logistics, Linyi University, Linyi, China
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39
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Zhu M, Men Q, Ho ESL, Leung H, Shum HPH. A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders Prediction. J Med Syst 2022; 46:76. [PMID: 36201114 PMCID: PMC9537228 DOI: 10.1007/s10916-022-01857-5] [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: 02/22/2022] [Accepted: 08/17/2022] [Indexed: 11/24/2022]
Abstract
Musculoskeletal and neurological disorders are the most common causes of walking problems among older people, and they often lead to diminished quality of life. Analyzing walking motion data manually requires trained professionals and the evaluations may not always be objective. To facilitate early diagnosis, recent deep learning-based methods have shown promising results for automated analysis, which can discover patterns that have not been found in traditional machine learning methods. We observe that existing work mostly applies deep learning on individual joint features such as the time series of joint positions. Due to the challenge of discovering inter-joint features such as the distance between feet (i.e. the stride width) from generally smaller-scale medical datasets, these methods usually perform sub-optimally. As a result, we propose a solution that explicitly takes both individual joint features and inter-joint features as input, relieving the system from the need of discovering more complicated features from small data. Due to the distinctive nature of the two types of features, we introduce a two-stream framework, with one stream learning from the time series of joint position and the other from the time series of relative joint displacement. We further develop a mid-layer fusion module to combine the discovered patterns in these two streams for diagnosis, which results in a complementary representation of the data for better prediction performance. We validate our system with a benchmark dataset of 3D skeleton motion that involves 45 patients with musculoskeletal and neurological disorders, and achieve a prediction accuracy of 95.56%, outperforming state-of-the-art methods.
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Affiliation(s)
- Manli Zhu
- Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Qianhui Men
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Edmond S L Ho
- School of Computing Science, University of Glasgow, Glasgow, UK
| | - Howard Leung
- Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong
| | - Hubert P H Shum
- Department of Computer Science, Durham University, Durham, UK.
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40
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Benchmarks for machine learning in depression discrimination using electroencephalography signals. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04159-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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41
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Hammad M, Alkinani MH, Gupta BB, Abd El-Latif AA. Myocardial infarction detection based on deep neural network on imbalanced data. MULTIMEDIA SYSTEMS 2022; 28:1373-1385. [DOI: 10.1007/s00530-020-00728-8] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Accepted: 12/01/2020] [Indexed: 09/02/2023]
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42
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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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43
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Huang CC, Low I, Kao CH, Yu CY, Su TP, Hsieh JC, Chen YS, Chen LF. MEG-based Classification and Grad-CAM Visualization for Major Depressive and Bipolar Disorders with Semi-CNN. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1823-1826. [PMID: 36086021 DOI: 10.1109/embc48229.2022.9871238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Major depressive disorder (MDD) and bipolar disorder (BD) are two major mood disorders with partly overlapped symptoms but different treatments. However, their misdiagnosis and mistreatment are common based on the DSM-V criteria, lacking objective and quantitative indicators. This study aimed to develop a novel approach that accurately classifies MDD and BD based on their resting-state magnetoencephalography (MEG) signals during euthymic phases. A revisited 3D CNN model, Semi-CNN, that could automatically detect brainwave patterns in spatial, temporal, and frequency domains was implemented to classify wavelet-transformed MEG signals of normal controls and MDD and BD patients. The model achieved a test accuracy of 96.05% and an average of 95.71% accuracy for 5-fold cross-validation. Furthermore, saliency maps of the model were estimated using Grad-CAM++ to visualize the proposed classification model and highlight disease-specific brain regions and frequencies. Clinical Relevance - Our model provides a stable pipeline that accurately classifies MDD, BD, and healthy individuals based on resting-state MEG signals during the euthymic phases, opening the potential for quantitative and accurate brain-based diagnosis for the highly misdiagnosed MDD/BD patients.
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44
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A Depression Diagnosis Method Based on the Hybrid Neural Network and Attention Mechanism. Brain Sci 2022; 12:brainsci12070834. [PMID: 35884641 PMCID: PMC9313113 DOI: 10.3390/brainsci12070834] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/23/2022] [Accepted: 06/24/2022] [Indexed: 02/01/2023] Open
Abstract
Depression is a common but easily misdiagnosed disease when using a self-assessment scale. Electroencephalograms (EEGs) provide an important reference and objective basis for the identification and diagnosis of depression. In order to improve the accuracy of the diagnosis of depression by using mainstream algorithms, a high-performance hybrid neural network depression detection method is proposed in this paper combined with deep learning technology. Firstly, a concatenating one-dimensional convolutional neural network (1D-CNN) and gated recurrent unit (GRU) are employed to extract the local features and to determine the global features of the EEG signal. Secondly, the attention mechanism is introduced to form the hybrid neural network. The attention mechanism assigns different weights to the multi-dimensional features extracted by the network, so as to screen out more representative features, which can reduce the computational complexity of the network and save the training time of the model while ensuring high precision. Moreover, dropout is applied to accelerate network training and address the over-fitting problem. Experiments reveal that the 1D-CNN-GRU-ATTN model has more effectiveness and a better generalization ability compared with traditional algorithms. The accuracy of the proposed method in this paper reaches 99.33% in a public dataset and 97.98% in a private dataset, respectively.
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45
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A major depressive disorder diagnosis approach based on EEG signals using dictionary learning and functional connectivity features. Phys Eng Sci Med 2022; 45:705-719. [DOI: 10.1007/s13246-022-01135-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 05/03/2022] [Indexed: 10/18/2022]
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46
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Liu W, Jia K, Wang Z, Ma Z. A Depression Prediction Algorithm Based on Spatiotemporal Feature of EEG Signal. Brain Sci 2022; 12:brainsci12050630. [PMID: 35625016 PMCID: PMC9139403 DOI: 10.3390/brainsci12050630] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 04/29/2022] [Accepted: 05/09/2022] [Indexed: 11/16/2022] Open
Abstract
Depression has gradually become the most common mental disorder in the world. The accuracy of its diagnosis may be affected by many factors, while the primary diagnosis seems to be difficult to define. Finding a way to identify depression by satisfying both objective and effective conditions is an urgent issue. In this paper, a strategy for predicting depression based on spatiotemporal features is proposed, and is expected to be used in the auxiliary diagnosis of depression. Firstly, electroencephalogram (EEG) signals were denoised through the filter to obtain the power spectra of the three corresponding frequency ranges, Theta, Alpha and Beta. Using orthogonal projection, the spatial positions of the electrodes were mapped to the brainpower spectrum, thereby obtaining three brain maps with spatial information. Then, the three brain maps were superimposed on a new brain map with frequency domain and spatial characteristics. A Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) were applied to extract the sequential feature. The proposed strategy was validated with a public EEG dataset, achieving an accuracy of 89.63% and an accuracy of 88.56% with the private dataset. The network had less complexity with only six layers. The results show that our strategy is credible, less complex and useful in predicting depression using EEG signals.
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Affiliation(s)
- Wei Liu
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; (W.L.); (Z.W.); (Z.M.)
- Beijing Laboratory of Advanced Information Networks, Beijing 100124, China
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China
| | - Kebin Jia
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; (W.L.); (Z.W.); (Z.M.)
- Beijing Laboratory of Advanced Information Networks, Beijing 100124, China
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China
- Correspondence:
| | - Zhuozheng Wang
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; (W.L.); (Z.W.); (Z.M.)
| | - Zhuo Ma
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; (W.L.); (Z.W.); (Z.M.)
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47
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Graphical representation learning-based approach for automatic classification of electroencephalogram signals in depression. Comput Biol Med 2022; 145:105420. [PMID: 35390744 DOI: 10.1016/j.compbiomed.2022.105420] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 02/01/2022] [Accepted: 02/19/2022] [Indexed: 11/20/2022]
Abstract
Depression is a major depressive disorder characterized by persistent sadness and a sense of worthlessness, as well as a loss of interest in pleasurable activities, which leads to a variety of physical and emotional problems. It is a worldwide illness that affects millions of people and should be detected at an early stage to prevent negative effects on an individual's life. Electroencephalogram (EEG) is a non-invasive technique for detecting depression that analyses brain signals to determine the current mental state of depressed subjects. In this study, we propose a method for automatic feature extraction to detect depression by first constructing a graph from the dataset where the nodes represent the subjects in the dataset and where the edge weights obtained using the Euclidean distance reflect the relationship between them. The Node2vec algorithmic framework is then used to compute feature representations for nodes in a graph in the form of node embeddings ensuring that similar nodes in the graph remain near in the embedding. These node embeddings act as useful features which can be directly used by classification algorithms to determine whether a subject is depressed thus reducing the effort required for manual handcrafted feature extraction. To combine the features collected from the multiple channels of the EEG data, the method proposes three types of fusion methods: graph-level fusion, feature-level fusion, and decision-level fusion. The proposed method is tested on three publicly available datasets with 3, 20, and 128 channels, respectively, and compared to five state-of-the-art methods. The results show that the proposed method detects depression effectively with a peak accuracy of 0.933 in decision-level fusion, which is the highest among the state-of-the-art methods.
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48
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Ray A, Bhardwaj A, Malik YK, Singh S, Gupta R. Artificial intelligence and Psychiatry: An overview. Asian J Psychiatr 2022; 70:103021. [PMID: 35219978 PMCID: PMC9760544 DOI: 10.1016/j.ajp.2022.103021] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 01/06/2022] [Accepted: 02/11/2022] [Indexed: 12/14/2022]
Abstract
The burden of mental illness both in world and India is increasing at an alarming rate. Adding to it, there has been an increase in mental health challenges during covid-19 pandemic with a rise in suicide, loneliness and substance use. Artificial intelligence can act as a potential solution to address this shortage. The use of artificial intelligence is increasingly being employed in various fields of mental health like affective disorders, psychosis, and geriatric psychiatry. The benefits are various like lower costs, wider reach but at the same time it comes with its own disadvantages. This article reviews the current understanding of artificial intelligence, the types of Artificial intelligence, its current use in various mental health disorders, current status in India, advantages, disadvantages and future potentials. With the passage of time and digitalization of the modern age, there will be an increase in the use of artificial intelligence in psychiatry hence a detailed understanding will be thoughtful. For this, we searched PubMed, Google Scholar, and Science Direct, China national Knowledge Infrastructure (CNKI), Globus Index Medicus search engines by using keywords. Initial searches involved the use of each individual keyword while the later searches involved the use of more than one word in different permutation combinations.
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Affiliation(s)
- Adwitiya Ray
- Department of Psychiatry, Institute of Mental Health, Pandit Bhagwat Dayal Sharma Post Graduate Institute of Medical Sciences, Rohtak, Haryana, India
| | - Akansha Bhardwaj
- Department of Psychiatry, Institute of Mental Health, Pandit Bhagwat Dayal Sharma Post Graduate Institute of Medical Sciences, Rohtak, Haryana, India
| | - Yogender Kumar Malik
- Department of Psychiatry, Institute of Mental Health, Pandit Bhagwat Dayal Sharma Post Graduate Institute of Medical Sciences, Rohtak, Haryana, India.
| | - Shipra Singh
- Department of Psychiatry, Institute of Mental Health, Pandit Bhagwat Dayal Sharma Post Graduate Institute of Medical Sciences, Rohtak, Haryana, India
| | - Rajiv Gupta
- Department of Psychiatry, Institute of Mental Health, Pandit Bhagwat Dayal Sharma Post Graduate Institute of Medical Sciences, Rohtak, Haryana, India
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49
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Lei Y, Belkacem AN, Wang X, Sha S, Wang C, Chen C. A convolutional neural network-based diagnostic method using resting-state electroencephalograph signals for major depressive and bipolar disorders. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103370] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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50
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Hashempour S, Boostani R, Mohammadi M, Sanei S. Continuous Scoring of Depression from EEG Signals via a Hybrid of Convolutional Neural Networks. IEEE Trans Neural Syst Rehabil Eng 2022; 30:176-183. [PMID: 35030081 DOI: 10.1109/tnsre.2022.3143162] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Depression score is traditionally determined by taking the Beck depression inventory (BDI) test, which is a qualitative questionnaire. Quantitative scoring of depression has also been achieved by analyzing and classifying pre-recorded electroencephalography (EEG) signals. Here, we go one step further and apply raw EEG signals to a proposed hybrid convolutional and temporal-convolutional neural network (CNN-TCN) to continuously estimate the BDI score. In this research, the EEG signals of 119 individuals are captured by 64 scalp electrodes through successive eyes-closed and eyes-open intervals. Moreover, all the subjects take the BDI test and their scores are determined. The proposed CNN-TCN provides mean squared error (MSE) of 5.64±1.6 and mean absolute error (MAE) of 1.73±0.27 for eyes-open state and also provides MSE of 9.53±2.94 and MAE of 2.32±0.35 for the eyes-closed state, which significantly surpasses state-of-the-art deep network methods. In another approach, conventional EEG features are elicited from the EEG signals in successive frames and apply them to the proposed CNN-TCN in conjunction with known statistical regression methods. Our method provides MSE of 10.81±5.14 and MAE of 2.41±0.59 that statistically outperform the statistical regression methods. Moreover, the results with raw EEG are significantly better than those with EEG features.
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