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Gradwohl G, Snipes S, Walitza S, Huber R, Gerstenberg M. Timing and cortical region matter: theta power differences between teenagers affected by Major Depression and healthy controls. J Neural Transm (Vienna) 2024; 131:1105-1115. [PMID: 39105815 PMCID: PMC11365826 DOI: 10.1007/s00702-024-02810-1] [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: 01/22/2024] [Accepted: 07/15/2024] [Indexed: 08/07/2024]
Abstract
In adults affected by Major Depressive Disorder (MDD), most findings point to higher electroencephalographic (EEG) theta power during wake compared to healthy controls (HC) as a potential biomarker aiding the diagnostic process or subgrouping for stratified treatment. Besides these group differences, theta power is modulated by time of day, sleep/wake history, and age. Thus, we aimed at assessing if the time of recording alters theta power in teenagers affected by MDD or HC. Standardized wake EEG power was assessed with high-density EEG in 15 children and adolescents with MDD and in 15 age- and sex-matched HC in the evening and morning. Using a two-way ANOVA, group, time, and their interaction were tested. In patients, the current severity of depression was rated using the Children's Depression Rating Scale. Broadband EEG power was lower in the morning after sleep, with a significant interaction (group x time) in central regions in the 4-6 Hz range. In MDD relative to HC, theta power was decreased over occipital areas in the evening and increased over frontal areas in the morning. A higher frontal theta power was correlated with more severe depressive mood in the morning but not in the evening. This was a cross-sectional study design, including patients on antidepressant medication. In conclusion, depending on time of recording, region-specific opposite differences of theta power were found between teenagers with MDD and HC. These findings stress the importance of the time of the recording when investigating theta power's relationship to psychopathology.
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Affiliation(s)
- Gideon Gradwohl
- Lev Academic Center, Department of Computer Sciences, Jerusalem College of Technology, Jerusalem, Israel
| | - Sophia Snipes
- Child Development Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
- Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
- Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Susanne Walitza
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Neuroscicence Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
- Zurich Center for Integrative Human Physiology, University of Zurich, Zurich, Switzerland
| | - Reto Huber
- Child Development Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
- Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Neuroscicence Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
| | - Miriam Gerstenberg
- Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland.
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland.
- Department of Child and Adolescent Psychiatry and Psychotherapy, Outpatient Services Winterthur, Psychiatric University Hospital Zurich, Albanistrasse 24, Winterthur, 8400, Switzerland.
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Xie XM, Sha S, Cai H, Liu X, Jiang I, Zhang L, Wang G. Resting-State Alpha Activity in the Frontal and Occipital Lobes and Assessment of Cognitive Impairment in Depression Patients. Psychol Res Behav Manag 2024; 17:2995-3003. [PMID: 39176258 PMCID: PMC11339342 DOI: 10.2147/prbm.s459954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Accepted: 08/12/2024] [Indexed: 08/24/2024] Open
Abstract
Background Major depressive disorder (MDD) becomes one of the psychiatric disorders characteristic of a combination of cognitive, emotional, and somatic symptoms. Additionally, cognitive impairment has the most significant impact on functional results. However, the evaluation of cognitive level is still based on various subjective questionnaires as there is no objective standard assessment yet. This research focuses on resting-state alpha activity to identify cognition in MDD patients using electroencephalography (EEG) signals. Methods Ninety-two subjects were recruited: 44 patients with MDD and 48 healthy individuals as controls. Functional outcome and cognition were assessed using standardized instruments, and the EEG resting state signal of open and closed eyes was recorded. The comparison and correlation of cognitive levels with alpha power in the bilateral frontal region, bilateral central region, bilateral occipital region, and middle line was evaluated. Results The relative alpha power in MDD group was significantly lower than that in the control group (P < 0.05). Through correlation analysis, it was shown that the bilateral frontal and occipital alpha power of MDD patients in the closed-eyes state was positively correlated with information processing rate, verbal learning, working memory, and attention retention. The alpha power of the bilateral frontal region in the open-eyes state was positively correlated with information processing rate, working memory, and attention retention (P < 0.05). Conclusion The research indicates that the changes in frontal and occipital alpha activities may be a promising neurophysiological indicator of cognitive level to diagnose and treat response prediction.
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Affiliation(s)
- Xiao-Meng Xie
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, School of Mental Health, Beijing, People’s Republic of China
| | - Sha Sha
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, School of Mental Health, Beijing, People’s Republic of China
| | - Hong Cai
- Unit of Medical Psychology and Behavior Medicine, School of Public Health, Guangxi Medical University, Nanning, Guangxi, People’s Republic of China
| | - Xinyu Liu
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, School of Mental Health, Beijing, People’s Republic of China
| | - Isadora Jiang
- Bellarmine College of Liberal Arts, Loyola Marymount University, Los Angeles, CA, USA
| | - Ling Zhang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, School of Mental Health, Beijing, People’s Republic of China
| | - Gang Wang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, School of Mental Health, Beijing, People’s Republic of China
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Li L, Wang X, Li J, Zhao Y. An EEG-based marker of functional connectivity: detection of major depressive disorder. Cogn Neurodyn 2024; 18:1671-1687. [PMID: 39104678 PMCID: PMC11297863 DOI: 10.1007/s11571-023-10041-5] [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: 04/28/2023] [Revised: 09/15/2023] [Accepted: 11/09/2023] [Indexed: 08/07/2024] Open
Abstract
Major depressive disorder (MDD) is a prevalent psychiatric disorder globally. There are many assays for MDD, but rapid and reliable detection remains a pressing challenge. In this study, we present a fusion feature called P-MSWC, as a novel marker to construct brain functional connectivity matrices and utilize the convolutional neural network (CNN) to identify MDD based on electroencephalogram (EEG) signal. Firstly, we combine synchrosqueezed wavelet transform and coherence theory to get synchrosqueezed wavelet coherence. Then, we obtain the fusion feature by incorporating synchrosqueezed wavelet coherence value and phase-locking value, which outperforms conventional functional connectivity markers by comprehensively capturing the original EEG signal's information and demonstrating notable noise-resistance capabilities. Finally, we propose a lightweight CNN model that effectively utilizes the high-dimensional connectivity matrix of the brain, constructed using our novel marker, to enable more accurate and efficient detection of MDD. The proposed method achieves 99.92% accuracy on a single dataset and 97.86% accuracy on a combined dataset. Moreover, comparison experiments have shown that the performance of the proposed method is superior to traditional machine learning methods. Furthermore, visualization experiments reveal differences in the distribution of brain connectivity between MDD patients and healthy subjects, including decreased connectivity in the T7, O1, F8, and C3 channels of the gamma band. The results of the experiments indicate that the fusion feature can be utilized as a new marker for constructing functional brain connectivity, and the combination of deep learning and functional connectivity matrices can provide more help for the detection of MDD.
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Affiliation(s)
- Ling Li
- College of Communication Engineering, Jilin University, Changchun, Jilin China
| | - Xianshuo Wang
- College of Communication Engineering, Jilin University, Changchun, Jilin China
| | - Jiahui Li
- College of Communication Engineering, Jilin University, Changchun, Jilin China
| | - Yanping Zhao
- College of Communication Engineering, Jilin University, Changchun, Jilin China
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Wang Y, Chen Y, Cui Y, Zhao T, Wang B, Zheng Y, Ren Y, Sha S, Yan Y, Zhao X, Zhang L, Wang G. Alterations in electroencephalographic functional connectivity in individuals with major depressive disorder: a resting-state electroencephalogram study. Front Neurosci 2024; 18:1412591. [PMID: 39055996 PMCID: PMC11270625 DOI: 10.3389/fnins.2024.1412591] [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: 04/05/2024] [Accepted: 06/19/2024] [Indexed: 07/28/2024] Open
Abstract
Background Major depressive disorder (MDD) is the leading cause of disability among all mental illnesses with increasing prevalence. The diagnosis of MDD is susceptible to interference by several factors, which has led to a trend of exploring objective biomarkers. Electroencephalography (EEG) is a non-invasive procedure that is being gradually applied to detect and diagnose MDD through some features such as functional connectivity (FC). Methods In this research, we analyzed the resting-state EEG of patients with MDD and healthy controls (HCs) in both eyes-open (EO) and eyes-closed (EC) conditions. The phase locking value (PLV) method was utilized to explore the connection and synchronization of neuronal activities spatiotemporally between different brain regions. We compared the PLV between participants with MDD and HCs in five frequency bands (theta, 4-8 Hz; alpha, 8-12 Hz; beta1, 12-16 Hz; beta2, 16-24 Hz; and beta3, 24-40 Hz) and further analyzed the correlation between the PLV of connections with significant differences and the severity of depression (via the scores of 17-item Hamilton Depression Rating Scale, HDRS-17). Results During the EO period, lower PLVs were found in the right temporal-left midline occipital cortex (RT-LMOC; theta, alpha, beta1, and beta2) and posterior parietal-right temporal cortex (PP-RT; beta1 and beta2) in the MDD group compared with the HC group, while PLVs were higher in the MDD group in LT-LMOC (beta2). During the EC period, for the MDD group, lower theta and beta (beta1, beta2, and beta3) PLVs were found in PP-RT, as well as lower theta, alpha, and beta (beta1, beta2, and beta3) PLVs in RT-LMOC. Additionally, in the left midline frontal cortex-right temporal cortex (LMFC-RT) and posterior parietal cortex-right temporal cortex (PP-RMOC), higher PLVs were observed in beta2. There were no significant correlations between PLVs and HDRS-17 scores when connections with significantly different PLVs (all p > 0.05) were checked. Conclusion Our study confirmed the presence of differences in FC between patients with MDD and healthy individuals. Lower PLVs in the connection of the right temporal-left occipital cortex were mostly observed, whereas an increase in PLVs was observed in patients with MDD in the connections of the left temporal with occipital lobe (EO), the circuits of the frontal-temporal lobe, and the parietal-occipital lobe. The trends in FC involved in this study were not correlated with the level of depression. Limitations The study was limited due to the lack of further analysis of confounding factors and follow-up data. Future studies with large-sampled and long-term designs are needed to further explore the distinguishable features of EEG FC in individuals with MDD.
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Affiliation(s)
- Yingtan Wang
- National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yu Chen
- National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yi Cui
- Gnosis Healthineer Co. Ltd, Beijing, China
| | - Tong Zhao
- Gnosis Healthineer Co. Ltd, Beijing, China
| | - Bin Wang
- National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yunxi Zheng
- National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yanping Ren
- National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Sha Sha
- National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | | | - Xixi Zhao
- National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Ling Zhang
- National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Gang Wang
- National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
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Grabowska A, Zabielski J, Senderecka M. Machine learning reveals differential effects of depression and anxiety on reward and punishment processing. Sci Rep 2024; 14:8422. [PMID: 38600089 PMCID: PMC11366008 DOI: 10.1038/s41598-024-58031-9] [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: 09/20/2023] [Accepted: 03/25/2024] [Indexed: 04/12/2024] Open
Abstract
Recent studies suggest that depression and anxiety are associated with unique aspects of EEG responses to reward and punishment, respectively; also, abnormal responses to punishment in depressed individuals are related to anxiety, the symptoms of which are comorbid with depression. In a non-clinical sample, we aimed to investigate the relationships between reward processing and anxiety, between punishment processing and anxiety, between reward processing and depression, and between punishment processing and depression. Towards this aim, we separated feedback-related brain activity into delta and theta bands to isolate activity that indexes functionally distinct processes. Based on the delta/theta frequency and feedback valence, we then used machine learning (ML) to classify individuals with high severity of depressive symptoms and individuals with high severity of anxiety symptoms versus controls. The significant difference between the depression and control groups was driven mainly by delta activity; there were no differences between reward- and punishment-theta activities. The high severity of anxiety symptoms was marginally more strongly associated with the punishment- than the reward-theta feedback processing. The findings provide new insights into the differences in the impacts of anxiety and depression on reward and punishment processing; our study shows the utility of ML in testing brain-behavior hypotheses and emphasizes the joint effect of theta-RewP/FRN and delta frequency on feedback-related brain activity.
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Affiliation(s)
- Anna Grabowska
- Doctoral School in the Social Sciences, Jagiellonian University, Main Square 34, 30-010, Kraków, Poland.
- Institute of Philosophy, Jagiellonian University, Grodzka 52, 31-044, Kraków, Poland.
| | - Jakub Zabielski
- Institute of Philosophy, Jagiellonian University, Grodzka 52, 31-044, Kraków, Poland
| | - Magdalena Senderecka
- Institute of Philosophy, Jagiellonian University, Grodzka 52, 31-044, Kraków, Poland.
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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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7
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Jiang X, Fan J, Zhu Z, Wang Z, Guo Y, Liu X, Jia F, Dai C. Cybersecurity in neural interfaces: Survey and future trends. Comput Biol Med 2023; 167:107604. [PMID: 37883851 DOI: 10.1016/j.compbiomed.2023.107604] [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: 06/01/2023] [Revised: 09/23/2023] [Accepted: 10/17/2023] [Indexed: 10/28/2023]
Abstract
With the joint advancement in areas such as pervasive neural data sensing, neural computing, neuromodulation and artificial intelligence, neural interface has become a promising technology facilitating both the closed-loop neurorehabilitation for neurologically impaired patients and the intelligent man-machine interactions for general application purposes. However, although neural interface has been widely studied, few previous studies focused on the cybersecurity issues in related applications. In this survey, we systematically investigated possible cybersecurity risks in neural interfaces, together with potential solutions to these problems. Importantly, our survey considers interfacing techniques on both central nervous systems (i.e., brain-computer interfaces) and peripheral nervous systems (i.e., general neural interfaces), covering diverse neural modalities such as electroencephalography, electromyography and more. Moreover, our survey is organized on three different levels: (1) the data level, which mainly focuses on the privacy leakage issue via attacking and analyzing neural database of users; (2) the permission level, which mainly focuses on the prospects and risks to directly use real time neural signals as biometrics for continuous and unobtrusive user identity verification; and (3) the model level, which mainly focuses on adversarial attacks and defenses on both the forward neural decoding models (e.g. via machine learning) and the backward feedback implementation models (e.g. via neuromodulation and stimulation). This is the first study to systematically investigate cybersecurity risks and possible solutions in neural interfaces which covers both central and peripheral nervous systems, and considers multiple different levels to provide a complete picture of this issue.
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Affiliation(s)
- Xinyu Jiang
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Jiahao Fan
- The Department of Mechanical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Ziyue Zhu
- The Department of Bioengineering, Imperial College London, SW7 2AZ London, UK
| | - Zihao Wang
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Yao Guo
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Xiangyu Liu
- The College of Communication and Art Design, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Fumin Jia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
| | - Chenyun Dai
- School of Information Science and Technology, Fudan University, Shanghai, China.
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Al-Qaysi ZT, Albahri AS, Ahmed MA, Mohammed SM. Development of hybrid feature learner model integrating FDOSM for golden subject identification in motor imagery. Phys Eng Sci Med 2023; 46:1519-1534. [PMID: 37603133 DOI: 10.1007/s13246-023-01316-6] [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: 08/13/2022] [Accepted: 08/01/2023] [Indexed: 08/22/2023]
Abstract
Brain-computer interfaces (BCIs) based on motor imagery (MI) face challenges due to the complex nature of brain activity, nonstationary and high-dimensional properties, and individual variations in motor behaviour. The identification of a consistent "golden subject" in MI-based BCIs remains an open challenge, complicated by multiple evaluation metrics and conflicting trade-offs, presenting complex Multi-Criteria Decision Making (MCDM) problems. This study proposes a hybrid brain signal decoding model called Hybrid Adaboost Feature Learner (HAFL), which combines feature extraction and classification using VGG-19, STFT, and Adaboost classifier. The model is validated using a pre-recorded MI-EEG dataset from the BCI competition at Graz University. The fuzzy decision-making framework is integrated with HAFL to allocate a golden subject for MI-BCI applications through the Golden Subject Decision Matrix (GSDM) and the Fuzzy Decision by Opinion Score Method (FDOSM). The effectiveness of the HAFL model in addressing inter-subject variability in EEG-based MI-BCI is evaluated using an MI-EEG dataset involving nine subjects. Comparing subject performance fairly is challenging due to complexity variations, but the FDOSM method provides valuable insights. Through FDOSM-based External Group Aggregation (EGA), subject S5 achieves the highest score of 2.900, identified as the most promising golden subject for subject-to-subject transfer learning. The proposed methodology is compared against other benchmark studies from various key perspectives and exhibits significant novelty in several aspects. The findings contribute to the development of more robust and effective BCI systems, paving the way for advancements in subject-to-subject transfer learning for BCI-MI applications.
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Affiliation(s)
- Z T Al-Qaysi
- Department of Computer Science, Computer Science and Mathematics College, Tikrit University, Tikrit, Iraq
| | - A S Albahri
- Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq.
| | - M A Ahmed
- Department of Computer Science, Computer Science and Mathematics College, Tikrit University, Tikrit, Iraq
| | - Saleh Mahdi Mohammed
- Department of Computer Technology Engineering, College of Information Technology, Imam Ja'afar Al-Sadiq University, Baghdad, Iraq
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9
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Mao K, Wu Y, Chen J. A systematic review on automated clinical depression diagnosis. NPJ MENTAL HEALTH RESEARCH 2023; 2:20. [PMID: 38609509 PMCID: PMC10955993 DOI: 10.1038/s44184-023-00040-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 09/27/2023] [Indexed: 04/14/2024]
Abstract
Assessing mental health disorders and determining treatment can be difficult for a number of reasons, including access to healthcare providers. Assessments and treatments may not be continuous and can be limited by the unpredictable nature of psychiatric symptoms. Machine-learning models using data collected in a clinical setting can improve diagnosis and treatment. Studies have used speech, text, and facial expression analysis to identify depression. Still, more research is needed to address challenges such as the need for multimodality machine-learning models for clinical use. We conducted a review of studies from the past decade that utilized speech, text, and facial expression analysis to detect depression, as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guideline. We provide information on the number of participants, techniques used to assess clinical outcomes, speech-eliciting tasks, machine-learning algorithms, metrics, and other important discoveries for each study. A total of 544 studies were examined, 264 of which satisfied the inclusion criteria. A database has been created containing the query results and a summary of how different features are used to detect depression. While machine learning shows its potential to enhance mental health disorder evaluations, some obstacles must be overcome, especially the requirement for more transparent machine-learning models for clinical purposes. Considering the variety of datasets, feature extraction techniques, and metrics used in this field, guidelines have been provided to collect data and train machine-learning models to guarantee reproducibility and generalizability across different contexts.
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Affiliation(s)
- Kaining Mao
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G 2R3, Canada
| | - Yuqi Wu
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G 2R3, Canada
| | - Jie Chen
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G 2R3, Canada.
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10
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Shim M, Hwang HJ, Lee SH. Toward practical machine-learning-based diagnosis for drug-naïve women with major depressive disorder using EEG channel reduction approach. J Affect Disord 2023; 338:199-206. [PMID: 37302509 DOI: 10.1016/j.jad.2023.06.007] [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: 07/18/2022] [Revised: 03/30/2023] [Accepted: 06/04/2023] [Indexed: 06/13/2023]
Abstract
BACKGROUND A machine-learning-based computer-aided diagnosis (CAD) system can complement the traditional diagnostic error for major depressive disorder (MDD) using trait-like neurophysiological biomarkers. Previous studies have shown that the CAD system has the potential to differentiate between female MDD patients and healthy controls. The aim of this study was to develop a practically useful resting-state electroencephalography (EEG)-based CAD system to assist in the diagnosis of drug-naïve female MDD patients by considering both the drug and gender effects. In addition, the feasibility of the practical use of the resting-state EEG-based CAD system was evaluated using a channel reduction approach. METHODS Eyes-closed, resting-state EEG data were recorded from 49 drug-naïve female MDD patients and 49 sex-matched healthy controls. Six different EEG feature sets were extracted: power spectrum densities (PSDs), phase-locking values (PLVs), and network indices for both sensor- and source-level, and four different EEG channel montages (62, 30, 19, and 10-channels) were designed to investigate the channel reduction effects in terms of classification performance. RESULTS The classification performances for each feature set were evaluated using a support vector machine with leave-one-out cross-validation. The optimum classification performance was achieved when using sensor-level PLVs (accuracy: 83.67 % and area under curve: 0.92). Moreover, the classification performance was maintained until the number of EEG channels was reduced to 19 (over 80 % accuracy). CONCLUSION We demonstrated the promising potential of sensor-level PLVs as diagnostic features when developing a resting-state EEG-based CAD system for the diagnosis of drug-naïve female MDD patients and verified the feasibility of the practical use of the developed resting-state EEG-based CAD system using the channel reduction approach.
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Affiliation(s)
- Miseon Shim
- Department of Electronics and Information Engineering, Korea University, Sejong, Republic of Korea; Industry Development Institute, Korea University, Sejong, Republic of Korea
| | - Han-Jeong Hwang
- Department of Electronics and Information Engineering, Korea University, Sejong, Republic of Korea; Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology, Korea University, Sejong, Republic of Korea.
| | - Seung-Hwan Lee
- Psychiatry Department, Ilsan Paik Hospital, Inje University, Goyang, Republic of Korea; Clinical Emotion and Cognition Research Laboratory, Goyang, Republic of Korea; BWAVE Inc., Goyang, Republic of Korea.
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11
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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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12
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Yang C, Sun Z, Zhang F, Shu H, Li J, Xiang W. TSUnet-CC: Temporal Spectrogram Unet embedding Cross Channel-wise attention mechanism for MDD identification. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083642 DOI: 10.1109/embc40787.2023.10340299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Automatic detection of major depressive disorder (MDD) with multiple-channel electroencephalography (EEG) signals is of great significance for treatment of the mental diseases. In a U-net network, clear EEG signals are fed to obtain temporal feature tensor through encoder and decoder networks with several convolution operations. Moreover, the clear EEG signals can be converted into multi-scale spectrogram to obtain the rich saliency information and then the spectrogram feature tensor can be extracted by another symmetrical U-net. The temporal and spectrogram feature tensors can provide more comprehensive information, but may also contain redundant information, which may affect the detection of MDD. To deal with such issue, this paper proposed a novel Temporal Spectrogram Unet (TSUnet-CC), which embeds the cross channel-wise attention mechanism for multiple-channel EEGbased MDD identification. We make three novel contributions: 1) multi-scale saliency-encoded spectrogram using Fourierbased approach to capture rich saliency information under different scales, 2) TSUnet network using a symmetrical twostream U-net architecture that learns multiple temporal and spectrogram feature tensors in time and frequency domains, and 3) cross channel-wise block enabling the larger weights of key feature channels that contain MDD information. The leaveone-subject-out experiments show that our proposed TSUnetCC gains high performance with a classification accuracy up to 98.55% and 99.22% in eyes closed and eyes open datasets, which outperformed some state-of-the-art methods and revealed its clinical potential.
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13
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Chumachenko SY, McVoy M. A narrative review and discussion of concepts and ongoing data regarding quantitative EEG as a childhood mood disorder biomarker. Biomark Neuropsychiatry 2023. [DOI: 10.1016/j.bionps.2022.100060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
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14
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Guhan Seshadri N, Agrawal S, Kumar Singh B, Geethanjali B, Mahesh V, Pachori RB. EEG based classification of children with learning disabilities using shallow and deep neural network. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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15
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Yang L, Wei X, Liu F, Zhu X, Zhou F. Automatic feature learning model combining functional connectivity network and graph regularization for depression detection. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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16
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KAYA Ş, TASCİ B. Electroencephalogram-Based Major Depressive Disorder Classification Using Convolutional Neural Network and Transfer Learning. TURKISH JOURNAL OF SCIENCE AND TECHNOLOGY 2023; 18:207-214. [DOI: 10.55525/tjst.1242881] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2024]
Abstract
Major Depressive Disorder (MDD) is a worldwide common disease with a high risk of becoming chronic, suicidal, and recurrence, with serious consequences such as loss of workforce. Objective tests such as EEG, EKG, brain MRI, and Doppler USG are used to aid diagnosis in MDD detection. With advances in artificial intelligence and sample data from objective testing for depression, an early depression detection system can be developed as a way to reduce the number of individuals affected by MDD. In this study, MDD was tried to be diagnosed automatically with a deep learning-based approach using EEG signals. In the study, 3-channel modma dataset was used as a dataset. Modma dataset consists of EEG signals of 29 controls and 26 MDD patients. ResNet18 convolutional neural network was used for feature extraction. The ReliefF algorithm is used for feature selection. In the classification phase, kNN was preferred. The accuracy was yielded 95.65% for Channel 1, 87.00% for Channel 2, and 86.94% for Channel 3.
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Affiliation(s)
- Şuheda KAYA
- Elazığ Ruh Sağlığı ve Hastalıkları Hastanesi
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17
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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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18
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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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19
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The Resting State of Taiwan EEG Normative Database: Z-Scores of Patients with Major Depressive Disorder as the Cross-Validation. Brain Sci 2023; 13:brainsci13020351. [PMID: 36831893 PMCID: PMC9954681 DOI: 10.3390/brainsci13020351] [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: 01/19/2023] [Revised: 02/10/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023] Open
Abstract
This study referred to the standard of electroencephalography (EEG) collection of normative databases and collected the Taiwan normative database to examine the reliability and validation of the Taiwan EEG normative database. We included 260 healthy participants and divided them into five groups in 10-year age-group segments and calculated the EEG means, standard deviation, and z-scores. Internal consistency reliability was verified at different frequencies between the three electrode locations in the Taiwan normative database. We recruited 221 major depressive disorder (MDD) patients for cross-validation between the Taiwan and NeuroGuide normative databases. There were high internal consistency reliabilities for delta, theta, alpha, beta, and high-beta at C3, Cz, and C4 in the HC group. There were high correlations between the two z-scores of the Taiwan and NeuroGuide normative databases in the frontal, central, parietal, temporal, and occipital lobes from MDD patients. The beta z-scores in the frontal lobe and central area, and the high-beta z-scores in the frontal, central, parietal, temporal, and occipital lobes were greater than one for MDD patients; in addition, the beta and high-beta absolute value z-scores in the whole brain were greater than the ones of MDD patients. The Taiwan EEG normative database has good psychometric characteristics of internal consistency reliability and cross-validation.
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20
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Feature extraction and selection from electroencephalogram signals for epileptic seizure diagnosis. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08350-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
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21
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Emre İE, Erol Ç, Taş C, Tarhan N. Multi-class classification model for psychiatric disorder discrimination. Int J Med Inform 2023; 170:104926. [PMID: 36442444 DOI: 10.1016/j.ijmedinf.2022.104926] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/24/2022] [Accepted: 11/07/2022] [Indexed: 11/13/2022]
Abstract
BACKGROUND Physicians follow-up a symptom-based approach in the diagnosis of psychiatric diseases. According to this approach, a process based on internationally valid diagnostic tools such as The Diagnostic and Statistical Manual of Mental Disorders (DSM) or International Classification of Diseases (ICD), patient reports and the observation and experience of the physician is monitored. As in other fields of medicine, the search for biomarkers that can be used in processes related to diseases continues in psychiatry and various researches are carried out in this field. OBJECTIVES Within the scope of this study, a dataset containing electroencephalogram (EEG) measurements of individuals diagnosed with different psychiatric diseases were analyzed by machine learning methods and the diseases were differentiated/classified with the models obtained. Thus, it was investigated whether EEG data could be a biomarker for psychiatric diseases. MATERIALS AND METHODS In the dataset analyzed within the scope of the study, for 550 patients (81 bipolar disorder, 95 attention deficit and hyperactivity disorder - ADHD, 67 depression, 34 obsessive compulsive disorder - OCD, 75 opioid, 146 posttraumatic stress disorder - PTSD, 52 schizophrenia) and 84 healthy individuals, there are 634 samples (rows), 77 variables (columns) in total. 76 of the variables consist of absolute power values belonging to 4 frequency bands (alpha, beta, delta, theta) collected from 19 different electrodes. 80 % of the dataset was used for training the models and 20 % of the data was used for testing the performance of the models. The 5-fold cross validation (CV) method, which repeats 3 times in the training dataset, was used and with this method, the hyperparameters used in the models were also optimized. Different models have been established with the selected hyperparameters and the performance of these models has been tested with the test dataset. C5.0, random forest (RF), support vector machine (SVM) and artificial neural networks (ANN) were used to build the models. RESULTS Within the scope of the study, the absolute power values obtained from EEG measurements performed using 19 electrodes were analyzed by machine learning methods. It was concluded that classification between disease groups was feasible with a high accuracy (C5.0-0.841, SVM_radial - 0.841, RF - 0.762). It was observed that ADHD, depression and schizophrenia diseases can be differentiated better (F-score = 1, balanced accuracy = 1) once the results were evaluated on a class category basis according to the F- measure and balanced accuracy values. DISCUSSION AND CONCLUSION Through the medium of the analyzes made within the scope of this study, it was investigated whether EEG data could be used as a biomarker for the detection and diagnosis of psychiatric diseases. The findings obtained from this study revealed that by using EEG data as a biomarker, it can be highly predicted whether a person has a psychiatric disease or not. Once evaluated with broad strokes, it is feasible to assert that it is possible to analyze whether the person who consults a physician with a complaint is ranked among the psychiatric disease class with EEG measurement. When trying to differentiate between numerous and diverse disease categories, it may be claimed that some diseases (ADHD, depression, schizophrenia) can be distinguished better by coming to the fore on a model basis. Considering the findings, it is anticipated that the analyzes obtained as a result of this study will contribute to the studies to be conducted using machine learning in the field of psychiatry.
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Affiliation(s)
- İlkim Ecem Emre
- Marmara University, Faculty of Business Administration, Department of Management Information Systems, İstanbul, Turkey; İstanbul University, Institute of Graduate Studies in Sciences, İstanbul, Turkey.
| | - Çiğdem Erol
- İstanbul University, Department of Informatics and Science Faculty Biology Department, Turkey.
| | - Cumhur Taş
- Üsküdar University, Faculty of Humanities and Social Sciences, Department of Psychology, Turkey.
| | - Nevzat Tarhan
- Üsküdar University, Faculty of Humanities and Social Sciences, Department of Psychology, Turkey.
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22
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Akbari H, Sadiq MT, Siuly S, Li Y, Wen P. Identification of normal and depression EEG signals in variational mode decomposition domain. Health Inf Sci Syst 2022; 10:24. [PMID: 36061530 PMCID: PMC9437202 DOI: 10.1007/s13755-022-00187-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 06/29/2022] [Indexed: 10/14/2022] Open
Abstract
Early detection of depression is critical in assisting patients in receiving the best therapy possible to avoid negative repercussions. Depression detection using electroencephalogram (EEG) signals is a simple, low-cost, convenient, and accurate approach. This paper proposes a six-stage novel method for detecting depression using EEG signals. First, EEG signals are recorded from 44 subjects, with 22 subjects being normal and 22 subjects being depressed. Second, a simple notch filter with EEG signals differencing approach is employed for effective preprocessing. Third, the variational mode decomposition (VMD) approach is implemented for nonlinear and non-stationary EEG signals analysis, resulting in many modes. Fourth, mutual information-based novel modes selection criterion is proposed to select the most informative modes. In the fifth step, a combination of linear and nonlinear features are extracted from selected modes and at last, classification is performed with neural networks. In this study, a novel single feature is also proposed, which is made using Log energy, norm entropies and fluctuation index, which delivers 100% classification accuracy, sensitivity and specificity. By using these features, a novel depression diagnostic index is also proposed. This integrated index would assist in quicker and more objective identification of normal and depression EEG signals. The proposed computerized framework and the DDI can help health workers, large enterprises, and product developers build a real-time system.
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Affiliation(s)
- Hesam Akbari
- Department of Biomedical Engineering, Islamic Azad University, Tehran, 1584715414 Iran
| | - Muhammad Tariq Sadiq
- School of Architecture, Technology and Engineering, University of Brighton, Brighton, BN2 4AT UK
| | - Siuly Siuly
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, 3011 Australia
| | - Yan Li
- School of Mathematics Physics and Computing, University of Southern Queensland, Toowoomba Campus, 4350 Australia
| | - Paul Wen
- School of Engineering, Victoria University, Melbourne, University of Southern Queensland, Toowoomba Campus, 4350 Australia
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23
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Wu X, Yang J. The superiority verification of morphological features in the EEG-based assessment of depression. J Neurosci Methods 2022; 381:109690. [PMID: 36007848 DOI: 10.1016/j.jneumeth.2022.109690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 08/16/2022] [Accepted: 08/19/2022] [Indexed: 12/14/2022]
Affiliation(s)
- Xiaolong Wu
- School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China; Shunde Graduate School, University of Science and Technology Beijing, Guangdong 528399, China.
| | - Jianhong Yang
- School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China; Shunde Graduate School, University of Science and Technology Beijing, Guangdong 528399, China; Technical Support Center for Prevention and Control of Disastrous Accidents in Metal Smelting, University of Science and Technology Beijing, Beijing 100083, China.
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24
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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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25
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Li Y, Shen Y, Fan X, Huang X, Yu H, Zhao G, Ma W. A novel EEG-based major depressive disorder detection framework with two-stage feature selection. BMC Med Inform Decis Mak 2022; 22:209. [PMID: 35933348 PMCID: PMC9357341 DOI: 10.1186/s12911-022-01956-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 07/29/2022] [Indexed: 11/16/2022] Open
Abstract
Background Major depressive disorder (MDD) is a common mental illness, characterized by persistent depression, sadness, despair, etc., troubling people’s daily life and work seriously. Methods In this work, we present a novel automatic MDD detection framework based on EEG signals. First of all, we derive highly MDD-correlated features, calculating the ratio of extracted features from EEG signals at frequency bands between \documentclass[12pt]{minimal}
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\begin{document}$$\alpha$$\end{document}α. Then, a two-stage feature selection method named PAR is presented with the sequential combination of Pearson correlation coefficient (PCC) and recursive feature elimination (RFE), where the advantages lie in minimizing the feature searching space. Finally, we employ widely used machine learning methods of support vector machine (SVM), logistic regression (LR), and linear regression (LNR) for MDD detection with the merit of feature interpretability. Results Experiment results show that our proposed MDD detection framework achieves competitive results. The accuracy and \documentclass[12pt]{minimal}
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\begin{document}$$F_{1}$$\end{document}F1 score are up to 0.9895 and 0.9846, respectively. Meanwhile, the regression determination coefficient \documentclass[12pt]{minimal}
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\begin{document}$$R^2$$\end{document}R2 for MDD severity assessment is up to 0.9479. Compared with existing MDD detection methods with the best accuracy of 0.9840 and \documentclass[12pt]{minimal}
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\begin{document}$$F_1$$\end{document}F1 score of 0.97, our proposed framework achieves the state-of-the-art MDD detection performance. Conclusions Development of this MDD detection framework can be potentially deployed into a medical system to aid physicians to screen out MDD patients.
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Affiliation(s)
- Yujie Li
- School of Computer Science, South China Normal University, Guangzhou, China
| | - Yingshan Shen
- School of Computer Science, South China Normal University, Guangzhou, China
| | - Xiaomao Fan
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China.
| | - Xingxian Huang
- Department of Acupuncture and Moxibustion, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China
| | - Haibo Yu
- Department of Acupuncture and Moxibustion, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China
| | - Gansen Zhao
- School of Computer Science, South China Normal University, Guangzhou, China
| | - Wenjun Ma
- School of Computer Science, South China Normal University, Guangzhou, China
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26
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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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27
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Visual electrophysiology and neuropsychology in bipolar disorders: a review on current state and perspectives. Neurosci Biobehav Rev 2022; 140:104764. [DOI: 10.1016/j.neubiorev.2022.104764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/21/2022] [Accepted: 07/01/2022] [Indexed: 11/21/2022]
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28
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Wang Z, Ma Z, Liu W, An Z, Huang F. A Depression Diagnosis Method Based on the Hybrid Neural Network and Attention Mechanism. Brain Sci 2022; 12:834. [PMID: 35884641 PMCID: PMC9313113 DOI: 10.3390/brainsci12070834] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [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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Affiliation(s)
- Zhuozheng Wang
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; (Z.M.); (W.L.)
| | - Zhuo Ma
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; (Z.M.); (W.L.)
| | - Wei Liu
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; (Z.M.); (W.L.)
| | - Zhefeng An
- Advising Center for Student Development, Beijing University of Technology, Beijing 100124, China;
| | - Fubiao Huang
- Department of Occupational Therapy, China Rehabilitation Research Center, Beijing 100068, China;
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29
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Zhang Y, Wang K, Yu W, Guo X, Wen J, Luo Y. Minimal EEG channel selection for depression detection with connectivity features during sleep. Comput Biol Med 2022; 147:105690. [DOI: 10.1016/j.compbiomed.2022.105690] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/29/2022] [Accepted: 05/31/2022] [Indexed: 11/30/2022]
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30
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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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31
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Sharma V, Prakash NR, Kalra P. Depression status identification using autoencoder neural network. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103568] [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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32
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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: 10] [Impact Index Per Article: 5.0] [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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Ketu S, Mishra PK. Hybrid classification model for eye state detection using electroencephalogram signals. Cogn Neurodyn 2022; 16:73-90. [PMID: 35126771 PMCID: PMC8807771 DOI: 10.1007/s11571-021-09678-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 03/16/2021] [Accepted: 04/05/2021] [Indexed: 02/03/2023] Open
Abstract
The electroencephalography (EEG) signal is an essential source of Brain-Computer Interface (BCI) technology implementation. The BCI is nothing but a non-muscle communication medium among the external devices and the brain. The basic concept of BCI is to enable the interaction among the neurological ill patients to others with the help of brain signals. EEG signal classification is an essential requirement for various applications such as motor imagery classification, drug effects diagnosis, emotion classification, seizure prediction/detection, eye state prediction/detection, and so on. Thus, there is a need for an efficient classification model that can deal with the EEG datasets more adequately with better classification accuracy, which will further help in developing the automatic solution for the medical domain. In this paper, we have introduced a hybrid classification model for eye state detection using electroencephalogram (EEG) signals. This hybrid classification model has been evaluated with the other traditional machine learning models, eight classification models (Prepossessed + Hypertuned) and six state-of-the-art methods to assess its appropriateness and correctness. This proposed classification model establishes a machine learning-based hybrid model for the classification of eye state using EEG signals with greater exactness. It is also capable of solving the issue of outlier detection and removal to address the class imbalance problem, which will offer the solution toward building the robotic or smart machine-based solution for social well-being.
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Affiliation(s)
- Shwet Ketu
- Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi, India
| | - Pramod Kumar Mishra
- Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi, India
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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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35
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Yu Y, Ling J, Yu L, Liu P, Jiang M. Closed-Loop Transcutaneous Auricular Vagal Nerve Stimulation: Current Situation and Future Possibilities. Front Hum Neurosci 2022; 15:785620. [PMID: 35058766 PMCID: PMC8763674 DOI: 10.3389/fnhum.2021.785620] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 11/11/2021] [Indexed: 11/13/2022] Open
Abstract
Closed-loop (CL) transcutaneous auricular vagal nerve stimulation (taVNS) was officially proposed in 2020. This work firstly reviewed two existing CL-taVNS forms: motor-activated auricular vagus nerve stimulation (MAAVNS) and respiratory-gated auricular vagal afferent nerve stimulation (RAVANS), and then proposed three future CL-taVNS systems: electroencephalography (EEG)-gated CL-taVNS, electrocardiography (ECG)-gated CL-taVNS, and subcutaneous humoral signals (SHS)-gated CL-taVNS. We also highlighted the mechanisms, targets, technical issues, and patterns of CL-taVNS. By reviewing, proposing, and highlighting, this work might draw a preliminary blueprint for the development of CL-taVNS.
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Affiliation(s)
- Yutian Yu
- Acupuncture Department, Beijing Shijitan Hospital, Capital Medical University, Beijing, China
- Ninth School of Clinical Medicine, Peking University, Beijing, China
- *Correspondence: Yutian Yu Min Jiang
| | - Jing Ling
- Department of Gynecology, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China
| | - Lingling Yu
- Department of Chinese and Western Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Pengfei Liu
- Ninth School of Clinical Medicine, Peking University, Beijing, China
- Department of Anesthesiology, Beijing Shijitan Hospital, Capital Medical University, Beijing, China
| | - Min Jiang
- Acupuncture Department, Beijing Shijitan Hospital, Capital Medical University, Beijing, China
- Ninth School of Clinical Medicine, Peking University, Beijing, China
- *Correspondence: Yutian Yu Min Jiang
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Uyulan C, de la Salle S, Erguzel TT, Lynn E, Blier P, Knott V, Adamson MM, Zelka M, Tarhan N. Depression Diagnosis Modeling With Advanced Computational Methods: Frequency-Domain eMVAR and Deep Learning. Clin EEG Neurosci 2022; 53:24-36. [PMID: 34080925 DOI: 10.1177/15500594211018545] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Electroencephalogram (EEG)-based automated depression diagnosis systems have been suggested for early and accurate detection of mood disorders. EEG signals are highly irregular, nonlinear, and nonstationary in nature and are traditionally studied from a linear viewpoint by means of statistical and frequency features. Since, linear metrics present certain limitations and nonlinear methods have proven to be an efficient tool in understanding the complexities of the brain in the identification of underlying behavior of biological signals, such as electrocardiogram, EEG and magnetoencephalogram and thus, can be applied to all nonstationary signals. Various nonlinear algorithms can be used in the analysis of EEG signals. In this research paper, we aim to develop a novel methodology for EEG-based depression diagnosis utilizing 2 advanced computational techniques: frequency-domain extended multivariate autoregressive (eMVAR) and deep learning (DL). We proposed a hybrid method comprising a pretrained ResNet-50 and long-short term memory (LSTM) to capture depression-specific information and compared with a strong conventional machine learning (ML) framework having eMVAR connectivity features. The following 8 causality measures, which interpret the interaction mechanisms among spectrally decomposed oscillations, were used to extract features from multivariate EEG time series: directed coherence (DC), directed transfer function (DTF), partial DC (PDC), generalized PDC (gPDC), extended DC (eDC), delayed DC (dDC), extended PDC (ePDC), and delayed PDC (dPDC). The classification accuracies were 84% with DC, 85% with DTF, 95.3% with PDC, 95.1% with gPDC, 84.8% with eDC, 84.6% with dDC, 84.2% with ePDC, and 95.9% with dPDC for the eMVAR framework. Through a DL framework (ResNet-50 + LSTM), the classification accuracy was achieved as 90.22%. The results demonstrate that our DL methodology is a competitive alternative to the strong feature extraction-based ML methods in depression classification.
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Affiliation(s)
| | - Sara de la Salle
- Institute of Mental Health Research, 6363University of Ottawa, Ottawa, ON, Canada.,6363University of Ottawa, Ottawa, ON, Canada
| | | | - Emma Lynn
- Institute of Mental Health Research, 6363University of Ottawa, Ottawa, ON, Canada.,6363University of Ottawa, Ottawa, ON, Canada
| | - Pierre Blier
- Institute of Mental Health Research, 6363University of Ottawa, Ottawa, ON, Canada.,6363University of Ottawa, Ottawa, ON, Canada
| | - Verner Knott
- Institute of Mental Health Research, 6363University of Ottawa, Ottawa, ON, Canada.,6363University of Ottawa, Ottawa, ON, Canada
| | | | | | - Nevzat Tarhan
- 232990Uskudar University, Istanbul, Turkey.,NPIstanbul Hospital, Istanbul, Turkey
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37
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Al-Ezzi A, Al-Shargabi AA, Al-Shargie F, Zahary AT. Complexity Analysis of EEG in Patients With Social Anxiety Disorder Using Fuzzy Entropy and Machine Learning Techniques. IEEE ACCESS 2022; 10:39926-39938. [DOI: 10.1109/access.2022.3165199] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Affiliation(s)
- Abdulhakim Al-Ezzi
- Electrical and Electronic Engineering Department, Centre for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS, Bandar, Seri Iskandar, Perak, Malaysia
| | - Amal A. Al-Shargabi
- Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
| | - Fares Al-Shargie
- Department of Electrical Engineering, American University of Sharjah, Sharjah, United Arab Emirates
| | - Ammar T. Zahary
- Department of Computer Science, Faculty of Computing and IT, University of Science and Technology, Sana’a, Yemen
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38
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Connecting Patients with Pre-diagnosis: A Multiple Graph Regularized Method for Mental Disorder Diagnosis. ARTIF INTELL 2022. [DOI: 10.1007/978-3-031-20500-2_30] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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39
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Liu Y, Pu C, Xia S, Deng D, Wang X, Li M. Machine learning approaches for diagnosing depression using EEG: A review. Transl Neurosci 2022; 13:224-235. [PMID: 36045698 PMCID: PMC9375981 DOI: 10.1515/tnsci-2022-0234] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 06/18/2022] [Accepted: 07/04/2022] [Indexed: 12/02/2022] Open
Abstract
Depression has become one of the most crucial public health issues, threatening the quality of life of over 300 million people throughout the world. Nevertheless, the clinical diagnosis of depression is now still hampered by behavioral diagnostic methods. Due to the lack of objective laboratory diagnostic criteria, accurate identification and diagnosis of depression remained elusive. With the rise of computational psychiatry, a growing number of studies have combined resting-state electroencephalography with machine learning (ML) to alleviate diagnosis of depression in recent years. Despite the exciting results, these were worrisome of these studies. As a result, ML prediction models should be continuously improved to better screen and diagnose depression. Finally, this technique would be used for the diagnosis of other psychiatric disorders in the future.
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Affiliation(s)
- Yuan Liu
- Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang 330006, Jiangxi Province, China
| | - Changqin Pu
- Queen Mary College, Nanchang University, Nanchang 330031, Jiangxi Province, China
| | - Shan Xia
- Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang 330006, Jiangxi Province, China
| | - Dingyu Deng
- Department of Internal Neurology, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi Province, China
| | - Xing Wang
- School of Life Sciences, Nanchang University, No.999 Xuefu Avenue, Honggutan District, Nanchang 330036, Jiangxi Province, China.,Clinical Diagnostics Laboratory, Clinical Medical Experiment Center, Nanchang University, Nanchang 330036, China
| | - Mengqian Li
- Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang 330006, Jiangxi Province, China
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40
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Fang X, Klawohn J, De Sabatino A, Kundnani H, Ryan J, Yu W, Hajcak G. Accurate classification of depression through optimized machine learning models on high-dimensional noisy data. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103237] [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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Wu CT, Huang HC, Huang S, Chen IM, Liao SC, Chen CK, Lin C, Lee SH, Chen MH, Tsai CF, Weng CH, Ko LW, Jung TP, Liu YH. Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset. BIOSENSORS 2021; 11:499. [PMID: 34940256 PMCID: PMC8699348 DOI: 10.3390/bios11120499] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 11/26/2021] [Accepted: 12/04/2021] [Indexed: 05/09/2023]
Abstract
Major depressive disorder (MDD) is a global healthcare issue and one of the leading causes of disability. Machine learning combined with non-invasive electroencephalography (EEG) has recently been shown to have the potential to diagnose MDD. However, most of these studies analyzed small samples of participants recruited from a single source, raising serious concerns about the generalizability of these results in clinical practice. Thus, it has become critical to re-evaluate the efficacy of various common EEG features for MDD detection across large and diverse datasets. To address this issue, we collected resting-state EEG data from 400 participants across four medical centers and tested classification performance of four common EEG features: band power (BP), coherence, Higuchi's fractal dimension, and Katz's fractal dimension. Then, a sequential backward selection (SBS) method was used to determine the optimal subset. To overcome the large data variability due to an increased data size and multi-site EEG recordings, we introduced the conformal kernel (CK) transformation to further improve the MDD as compared with the healthy control (HC) classification performance of support vector machine (SVM). The results show that (1) coherence features account for 98% of the optimal feature subset; (2) the CK-SVM outperforms other classifiers such as K-nearest neighbors (K-NN), linear discriminant analysis (LDA), and SVM; (3) the combination of the optimal feature subset and CK-SVM achieves a high five-fold cross-validation accuracy of 91.07% on the training set (140 MDD and 140 HC) and 84.16% on the independent test set (60 MDD and 60 HC). The current results suggest that the coherence-based connectivity is a more reliable feature for achieving high and generalizable MDD detection performance in real-life clinical practice.
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Affiliation(s)
- Chien-Te Wu
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo 113-0033, Japan;
| | - Hao-Chuan Huang
- Hipposcreen Neurotech Corp. (HNC), Taipei 114, Taiwan; (H.-C.H.); (S.H.); (C.-H.W.)
| | - Shiuan Huang
- Hipposcreen Neurotech Corp. (HNC), Taipei 114, Taiwan; (H.-C.H.); (S.H.); (C.-H.W.)
| | - I-Ming Chen
- Division of Psychosomatic Medicine, Department of Psychiatry, National Taiwan University Hospital, Taipei 100229, Taiwan; (I.-M.C.); (S.-C.L.)
- Institute of Health Policy and Management, National Taiwan University, Taipei 10617, Taiwan
| | - Shih-Cheng Liao
- Division of Psychosomatic Medicine, Department of Psychiatry, National Taiwan University Hospital, Taipei 100229, Taiwan; (I.-M.C.); (S.-C.L.)
| | - Chih-Ken Chen
- Department of Psychiatry & Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung 204, Taiwan; (C.-K.C.); (C.L.)
- College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan;
| | - Chemin Lin
- Department of Psychiatry & Community Medicine Research Center, Chang Gung Memorial Hospital, Keelung 204, Taiwan; (C.-K.C.); (C.L.)
- College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan;
| | - Shwu-Hua Lee
- College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan;
- Department of Psychiatry, Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan
| | - Mu-Hong Chen
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11217, Taiwan; (M.-H.C.); (C.-F.T.)
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei 11217, Taiwan
| | - Chia-Fen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11217, Taiwan; (M.-H.C.); (C.-F.T.)
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei 11217, Taiwan
| | - Chang-Hsin Weng
- Hipposcreen Neurotech Corp. (HNC), Taipei 114, Taiwan; (H.-C.H.); (S.H.); (C.-H.W.)
| | - Li-Wei Ko
- Department of Bio Science & Tech., National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan;
| | - Tzyy-Ping Jung
- Institute for Neural Computation, University of California, San Diego, CA 92093, USA
| | - Yi-Hung Liu
- Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan
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42
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Sadiq MT, Akbari H, Siuly S, Yousaf A, Rehman AU. A novel computer-aided diagnosis framework for EEG-based identification of neural diseases. Comput Biol Med 2021; 138:104922. [PMID: 34656865 DOI: 10.1016/j.compbiomed.2021.104922] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 10/03/2021] [Accepted: 10/05/2021] [Indexed: 10/20/2022]
Abstract
Recent advances in electroencephalogram (EEG) signal classification have primarily focused on domain-specific approaches, which impede algorithm cross-discipline capability. This study introduces a new computer-aided diagnosis (CAD) system for the classification of two distinct EEG domains under a unified sequential framework. The key motivation to consider two neural diseases by one framework is to develop a unified algorithm for EEG classification. The main contributions of this study are five-fold. First, EEG signals are decomposed into 10 intrinsic mode functions (IMFs) with the help of empirical wavelet transform. Second, a novel two-dimensional (2D) modeling of IMFs is plotted to visualize the complexity of EEG signals. Third, several new geometrical features are extracted to analyze the dynamic and chaotic essence. Fourth, significant features are selected by binary particle swarm optimization algorithm (B-PSO). Fifth, selected features are fed to the k-nearest neighbor classifier for EEG signal classification purposes. All the experiments are executed on one depression and two epileptic EEG datasets in a leave one out cross-validation strategy. The proposed CAD system provides an average classification accuracy of 93.35% in depression detection, 99.33% for regular against ictal, and 97.33% for interictal versus ictal respectively. The overall empirical analysis authenticates that the proposed CAD outperforms the existing domain-specific methods in terms of classification accuracies and multirole adaptability, thus, can be endorsed as an effective automated neural rehabilitation system.
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Affiliation(s)
- Muhammad Tariq Sadiq
- School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China; Department of Electrical Engineering, The University of Lahore, Lahore, 54000, Pakistan.
| | - Hesam Akbari
- Department of Biomedical Engineering, Islamic Azad University, Tehran, 1411718541, Iran.
| | - Siuly Siuly
- Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, 14428, Australia.
| | - Adnan Yousaf
- Department of Electrical Engineering, Superior University, Lahore, 54000, Pakistan.
| | - Ateeq Ur Rehman
- Department of Electrical Engineering, Government College University, Lahore, 54000, Pakistan.
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Lian J, Song Y, Zhang Y, Guo X, Wen J, Luo Y. Characterization of specific spatial functional connectivity difference in depression during sleep. J Neurosci Res 2021; 99:3021-3034. [PMID: 34637550 DOI: 10.1002/jnr.24947] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 07/14/2021] [Accepted: 08/04/2021] [Indexed: 11/08/2022]
Abstract
Depression is a common mental illness and a large number of researchers have been still devoted to exploring effective biomarkers for the identification of depression. Few researches have been conducted on functional connectivity (FC) during sleep in depression. In this paper, a novel depression characterization is proposed using specific spatial FC features of sleep electroencephalography (EEG). Overnight polysomnography recordings were obtained from 26 healthy individuals and 25 patients with depression. The weighted phase lag indexes (WPLIs) of four frequency bands and five sleep periods were obtained from 16 EEG channels. The high discriminative connections extracted via feature evaluation and the cross-within variation (CW)-the spatial feature constructed to characterize the different performances in inter- and intra-hemispheric FC based on WPLIs, were utilized to classify patients and normal controls. The results showed that enhanced average FC and spatial differences, higher inter-hemispheric FC and lower intra-hemispheric FC, were found in patients. Furthermore, abnormalities in the inter-hemispheric connections of the temporal lobe in the theta band should be important indicators of depression. Finally, both CW and high discriminative WPLI features performed well in depression screening and CW was more specific for characterizing abnormal cortical EEG performance of depression. Our work investigated and characterized the abnormalities in sleep cortical activity in patients with depression, and may provide potential biomarkers for assisting with depression identification and new insights into the understanding of pathological mechanisms in depression.
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Affiliation(s)
- Jiakai Lian
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yingjie Song
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yangting Zhang
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Xinwen Guo
- Psychology Department, Guangdong 999 Brain Hospital, Guangzhou, China
| | - Jinfeng Wen
- Psychology Department, Guangdong 999 Brain Hospital, Guangzhou, China
| | - Yuxi Luo
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China.,Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, Sun Yat-Sen University, Guangzhou, China
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44
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Knociková JA, Petrásek T. Quantitative electroencephalographic biomarkers behind major depressive disorder. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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45
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46
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Mesquita EDM, Rodrigues FB, Rodrigues AP, Lemes TS, Andrade AO, Vieira MF. Discrimination capability of linear and nonlinear gait features in group classification. Med Eng Phys 2021; 93:59-71. [PMID: 34154776 DOI: 10.1016/j.medengphy.2021.05.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 02/26/2021] [Accepted: 05/25/2021] [Indexed: 11/17/2022]
Abstract
The variability of human movement can be defined as normal variations occurring in motor activity and quantified using linear statistics or nonlinear methods. In the human movement field, linear and nonlinear measures of variability have been used to discriminate groups and conditions in different contexts. Indeed, some authors support the idea that these gait features provide complementary information about movement. However, it is unclear which type of gait variability measure best discriminates different groups or conditions, as a comparison of the discrimination capacity between linear and nonlinear gait variability features in different groups has not been assessed. Therefore, the main objective of this study was to test the discrimination capacity of linear and nonlinear gait features to determine which type of feature would be the most efficient for discriminating older and younger adults and between lower limb amputees and nonamputees using classification algorithms. Data from previously published studies were used. The classification task was performed using the k-nearest neighbors and random forest algorithms. Our results showed that using a combination of linear and nonlinear features resulted in the highest mean accuracy rates (>90%) in group classification, reinforcing the idea that these features are complementary and express different aspects of movement.
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Affiliation(s)
- Eduardo de Mendonça Mesquita
- Bioengineering and Biomechanics Laboratory, Federal University of Goiás, Avenida Esperança s/n, Campus Samambaia, 74690-900 Goiânia, Goiás, Brazil.
| | - Fábio Barbosa Rodrigues
- Bioengineering and Biomechanics Laboratory, Federal University of Goiás, Avenida Esperança s/n, Campus Samambaia, 74690-900 Goiânia, Goiás, Brazil; State University of Goiás - UnU Trindade, Trindade, Brazil
| | - Adriano Péricles Rodrigues
- Bioengineering and Biomechanics Laboratory, Federal University of Goiás, Avenida Esperança s/n, Campus Samambaia, 74690-900 Goiânia, Goiás, Brazil
| | - Thiago Santana Lemes
- Bioengineering and Biomechanics Laboratory, Federal University of Goiás, Avenida Esperança s/n, Campus Samambaia, 74690-900 Goiânia, Goiás, Brazil
| | - Adriano O Andrade
- Center for Innovation and Technology Assessment in Health, Federal University of Uberlândia, Uberlândia, Brazil
| | - Marcus Fraga Vieira
- Bioengineering and Biomechanics Laboratory, Federal University of Goiás, Avenida Esperança s/n, Campus Samambaia, 74690-900 Goiânia, Goiás, Brazil
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47
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Movahed RA, Jahromi GP, Shahyad S, Meftahi GH. A major depressive disorder classification framework based on EEG signals using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis. J Neurosci Methods 2021; 358:109209. [PMID: 33957158 DOI: 10.1016/j.jneumeth.2021.109209] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 04/22/2021] [Accepted: 04/26/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND Major depressive disorder (MDD) is a prevalent mental illness that is diagnosed through questionnaire-based approaches; however, these methods may not lead to an accurate diagnosis. In this regard, many studies have focused on using electroencephalogram (EEG) signals and machine learning techniques to diagnose MDD. NEW METHOD This paper proposes a machine learning framework for MDD diagnosis, which uses different types of EEG-derived features. The features are extracted using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis methods. The sequential backward feature selection (SBFS) algorithm is also employed to perform feature selection. Various classifier models are utilized to select the best one for the proposed framework. RESULTS The proposed method is validated with a public EEG dataset, including the EEG data of 34 MDD patients and 30 healthy subjects. The evaluation of the proposed framework is conducted using 10-fold cross-validation, providing the metrics such as accuracy (AC), sensitivity (SE), specificity (SP), F1-score (F1), and false discovery rate (FDR). The best performance of the proposed method has provided an average AC of 99%, SE of 98.4%, SP of 99.6%, F1 of 98.9%, and FDR of 0.4% using the support vector machine with RBF kernel (RBFSVM) classifier. COMPARISON WITH EXISTING METHODS The obtained results demonstrate that the proposed method outperforms other approaches for MDD classification based on EEG signals. CONCLUSIONS According to the obtained results, a highly accurate MDD diagnosis would be provided using the proposed method, while it can be utilized to develop a computer-aided diagnosis (CAD) tool for clinical purposes.
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Affiliation(s)
- Reza Akbari Movahed
- Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Gila Pirzad Jahromi
- Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran.
| | - Shima Shahyad
- Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
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Saeedi A, Saeedi M, Maghsoudi A, Shalbaf A. Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach. Cogn Neurodyn 2021; 15:239-252. [PMID: 33854642 PMCID: PMC7969675 DOI: 10.1007/s11571-020-09619-0] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 07/13/2020] [Accepted: 07/16/2020] [Indexed: 02/07/2023] Open
Abstract
Deep learning techniques have recently made considerable advances in the field of artificial intelligence. These methodologies can assist psychologists in early diagnosis of mental disorders and preventing severe trauma. Major Depression Disorder (MDD) is a common and serious medical condition whose exact manifestations are not fully understood. So, early discovery of MDD patients helps to cure or limit the adverse effects. Electroencephalogram (EEG) is prominently used to study brain diseases such as MDD due to having high temporal resolution information, and being a noninvasive, inexpensive and portable method. This paper has proposed an EEG-based deep learning framework that automatically discriminates MDD patients from healthy controls. First, the relationships among EEG channels in the form of effective brain connectivity analysis are extracted by Generalized Partial Directed Coherence (GPDC) and Direct directed transfer function (dDTF) methods. A novel combination of sixteen connectivity methods (GPDC and dDTF in eight frequency bands) was used to construct an image for each individual. Finally, the constructed images of EEG signals are applied to the five different deep learning architectures. The first and second algorithms were based on one and two-dimensional convolutional neural network (1DCNN-2DCNN). The third method is based on long short-term memory (LSTM) model, while the fourth and fifth algorithms utilized a combination of CNN with LSTM model namely, 1DCNN-LSTM and 2DCNN-LSTM. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals. The efficiency of the proposed algorithms is evaluated on resting state EEG data obtained from 30 healthy subjects and 34 MDD patients. The experiments show that the 1DCNN-LSTM applied on constructed image of effective connectivity achieves best results with accuracy of 99.24% due to specific architecture which captures the presence of spatial and temporal relations in the brain connectivity. The proposed method as a diagnostic tool is able to help clinicians for diagnosing the MDD patients for early diagnosis and treatment.
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Affiliation(s)
- Abdolkarim Saeedi
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Maryam Saeedi
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Arash Maghsoudi
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Sharma G, Parashar A, Joshi AM. DepHNN: A novel hybrid neural network for electroencephalogram (EEG)-based screening of depression. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102393] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Uyulan C, Ergüzel TT, Unubol H, Cebi M, Sayar GH, Nezhad Asad M, Tarhan N. Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach. Clin EEG Neurosci 2021; 52:38-51. [PMID: 32491928 DOI: 10.1177/1550059420916634] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The human brain is characterized by complex structural, functional connections that integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation of both structural and functional connections of the brain and the effects in the diagnosis and treatment of neurodegenerative diseases. Currently, there is no clinically specific diagnostic biomarker capable of confirming the diagnosis of major depressive disorder (MDD). Therefore, exploring translational biomarkers of mood disorders based on deep learning (DL) has valuable potential with its recently underlined promising outcomes. In this article, an electroencephalography (EEG)-based diagnosis model for MDD is built through advanced computational neuroscience methodology coupled with a deep convolutional neural network (CNN) approach. EEG recordings are analyzed by modeling 3 different deep CNN structure, namely, ResNet-50, MobileNet, Inception-v3, in order to dichotomize MDD patients and healthy controls. EEG data are collected for 4 main frequency bands (Δ, θ, α, and β, accompanying spatial resolution with location information by collecting data from 19 electrodes. Following the pre-processing step, different DL architectures were employed to underline discrimination performance by comparing classification accuracies. The classification performance of models based on location data, MobileNet architecture generated 89.33% and 92.66% classification accuracy. As to the frequency bands, delta frequency band outperformed compared to other bands with 90.22% predictive accuracy and area under curve (AUC) value of 0.9 for ResNet-50 architecture. The main contribution of the study is the delineation of distinctive spatial and temporal features using various DL architectures to dichotomize 46 MDD subjects from 46 healthy subjects. Exploring translational biomarkers of mood disorders based on DL perspective is the main focus of this study and, though it is challenging, with its promising potential to improve our understanding of the psychiatric disorders, computational methods are highly worthy for the diagnosis process and valuable in terms of both speed and accuracy compared with classical approaches.
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Affiliation(s)
- Caglar Uyulan
- Department of Mechatronics, Faculty of Engineering, Bulent Ecevit University, Zonguldak, Turkey
| | - Türker Tekin Ergüzel
- Department of Software Engineering, Faculty of Engineering and Natural Sciences, Uskudar University, Istanbul, Turkey
| | - Huseyin Unubol
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| | - Merve Cebi
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| | - Gokben Hizli Sayar
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| | | | - Nevzat Tarhan
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
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