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Li X, Kang Q, Gu H. A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder. Front Hum Neurosci 2023; 17:1280512. [PMID: 38021236 PMCID: PMC10646310 DOI: 10.3389/fnhum.2023.1280512] [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: 08/20/2023] [Accepted: 10/18/2023] [Indexed: 12/01/2023] Open
Abstract
Obsessive-compulsive disorder (OCD) is a common mental disease, which can exist as a separate disease or become one of the symptoms of other mental diseases. With the development of society, statistically, the incidence rate of obsessive-compulsive disorder has been increasing year by year. At present, in the diagnosis and treatment of OCD, The clinical performance of patients measured by scales is no longer the only quantitative indicator. Clinical workers and researchers are committed to using neuroimaging to explore the relationship between changes in patient neurological function and obsessive-compulsive disorder. Through machine learning and artificial learning, medical information in neuroimaging can be better displayed. In this article, we discuss recent advancements in artificial intelligence related to neuroimaging in the context of Obsessive-Compulsive Disorder.
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Affiliation(s)
- Xuanyi Li
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Qiang Kang
- Department of Radiology, Xing’an League People’s Hospital of Inner Mongolia, Mongolia, China
| | - Hanxing Gu
- Department of Geriatric Psychiatry, Qingdao Mental Health Center, Qingdao, Shandong, China
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Chen Z, Hu B, Liu X, Becker B, Eickhoff SB, Miao K, Gu X, Tang Y, Dai X, Li C, Leonov A, Xiao Z, Feng Z, Chen J, Chuan-Peng H. Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry. BMC Med 2023; 21:241. [PMID: 37400814 DOI: 10.1186/s12916-023-02941-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.
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Affiliation(s)
- Zhiyi Chen
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China.
- Faculty of Psychology, Southwest University, Chongqing, China.
| | - Bowen Hu
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Xuerong Liu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, Chengdu, China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kuan Miao
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Xingmei Gu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Yancheng Tang
- School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Xin Dai
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Chao Li
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangdong, China
| | - Artemiy Leonov
- School of Psychology, Clark University, Worcester, MA, USA
| | - Zhibing Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zhengzhi Feng
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.
- Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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Roy B, Malviya L, Kumar R, Mal S, Kumar A, Bhowmik T, Hu JW. Hybrid Deep Learning Approach for Stress Detection Using Decomposed EEG Signals. Diagnostics (Basel) 2023; 13:diagnostics13111936. [PMID: 37296788 DOI: 10.3390/diagnostics13111936] [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: 04/16/2023] [Revised: 05/14/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023] Open
Abstract
Stress has an impact, not only on a person's physical health, but also on the ability to perform at the workplace in daily life. The well-established relation between psychological stress and its pathogeneses highlights the need for detecting psychological stress early, in order to prevent disease advancement and to save human lives. Electroencephalography (EEG) signal recording tools are widely used to collect these psychological signals/brain rhythms in the form of electric waves. The aim of the current research was to apply automatic feature extraction to decomposed multichannel EEG recordings, in order to efficiently detect psychological stress. The traditional deep learning techniques, namely the convolution neural network (CNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU) and recurrent neural network (RNN) models, have been frequently used for stress detection. A hybrid combination of these techniques may provide improved performance, and can handle long-term dependencies in non-linear brain signals. Therefore, this study proposed an integration of deep learning models, called DWT-based CNN, BiLSTM, and two layers of a GRU network, to extract features and classify stress levels. Discrete wavelet transform (DWT) analysis was used to remove the non-linearity and non-stationarity from multi-channel (14 channel) EEG recordings, and to decompose them into different frequency bands. The decomposed signals were utilized for automatic feature extraction using the CNN, and the stress levels were classified using BiLSTM and two layers of GRU. This study compared five combinations of the CNN, LSTM, BiLSTM, GRU and RNN models with the proposed model. The proposed hybrid model performed better in classification accuracy compared to the other models. Therefore, hybrid combinations are appropriate for the clinical intervention and prevention of mental and physical problems.
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Affiliation(s)
- Bishwajit Roy
- Department of Computer Science Engineering-AI & ML, Siliguri Institute of Technology, Siliguri 734009, India
| | - Lokesh Malviya
- School of Computing Science and Engineering, Vellore Institute of Technology Bhopal University, Bhopal 466114, India
| | - Radhikesh Kumar
- Department of Computer Science and Engineering, National Institute of Technology, Patna 800001, India
| | - Sandip Mal
- School of Computing Science and Engineering, Vellore Institute of Technology Bhopal University, Bhopal 466114, India
| | - Amrendra Kumar
- Department of Civil Engineering, Roorkee Institute of Technology, Roorkee 247667, India
| | - Tanmay Bhowmik
- Department of Computer Science and Engineering, Pandit Deendayal Energy University, Gandhinagar 382426, India
| | - Jong Wan Hu
- Department of Civil and Environmental Engineering, Incheon National University, Incheon 22022, Republic of Korea
- Incheon Disaster Prevention Research Center, Incheon National University, Incheon 22022, Republic of Korea
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Bernardi D, Shannahoff-Khalsa D, Sale J, Wright JA, Fadiga L, Papo D. The time scales of irreversibility in spontaneous brain activity are altered in obsessive compulsive disorder. Front Psychiatry 2023; 14:1158404. [PMID: 37234212 PMCID: PMC10208430 DOI: 10.3389/fpsyt.2023.1158404] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 03/30/2023] [Indexed: 05/27/2023] Open
Abstract
We study how obsessive-compulsive disorder (OCD) affects the complexity and time-reversal symmetry-breaking (irreversibility) of the brain resting-state activity as measured by magnetoencephalography (MEG). Comparing MEG recordings from OCD patients and age/sex matched control subjects, we find that irreversibility is more concentrated at faster time scales and more uniformly distributed across different channels of the same hemisphere in OCD patients than in control subjects. Furthermore, the interhemispheric asymmetry between homologous areas of OCD patients and controls is also markedly different. Some of these differences were reduced by 1-year of Kundalini Yoga meditation treatment. Taken together, these results suggest that OCD alters the dynamic attractor of the brain's resting state and hint at a possible novel neurophysiological characterization of this psychiatric disorder and how this therapy can possibly modulate brain function.
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Affiliation(s)
- Davide Bernardi
- Center for Translational Neurophysiology of Speech and Communication, Fondazione Istituto Italiano di Tecnologia, Ferrara, Italy
| | - David Shannahoff-Khalsa
- BioCircuits Institute, University of California, San Diego, La Jolla, CA, United States
- Center for Integrative Medicine, University of California, San Diego, La Jolla, CA, United States
- The Khalsa Foundation for Medical Science, Del Mar, CA, United States
| | - Jeff Sale
- San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, United States
| | - Jon A. Wright
- BioCircuits Institute, University of California, San Diego, La Jolla, CA, United States
| | - Luciano Fadiga
- Center for Translational Neurophysiology of Speech and Communication, Fondazione Istituto Italiano di Tecnologia, Ferrara, Italy
- Department of Neuroscience and Rehabilitation, Section of Physiology, University of Ferrara, Ferrara, Italy
| | - David Papo
- Center for Translational Neurophysiology of Speech and Communication, Fondazione Istituto Italiano di Tecnologia, Ferrara, Italy
- Department of Neuroscience and Rehabilitation, Section of Physiology, University of Ferrara, Ferrara, Italy
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Chandrabhatla AS, Pomeraniec IJ, Horgan TM, Wat EK, Ksendzovsky A. Landscape and future directions of machine learning applications in closed-loop brain stimulation. NPJ Digit Med 2023; 6:79. [PMID: 37106034 PMCID: PMC10140375 DOI: 10.1038/s41746-023-00779-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 02/17/2023] [Indexed: 04/29/2023] Open
Abstract
Brain stimulation (BStim) encompasses multiple modalities (e.g., deep brain stimulation, responsive neurostimulation) that utilize electrodes implanted in deep brain structures to treat neurological disorders. Currently, BStim is primarily used to treat movement disorders such as Parkinson's, though indications are expanding to include neuropsychiatric disorders like depression and schizophrenia. Traditional BStim systems are "open-loop" and deliver constant electrical stimulation based on manually-determined parameters. Advancements in BStim have enabled development of "closed-loop" systems that analyze neural biomarkers (e.g., local field potentials in the sub-thalamic nucleus) and adjust electrical modulation in a dynamic, patient-specific, and energy efficient manner. These closed-loop systems enable real-time, context-specific stimulation adjustment to reduce symptom burden. Machine learning (ML) has emerged as a vital component in designing these closed-loop systems as ML models can predict / identify presence of disease symptoms based on neural activity and adaptively learn to modulate stimulation. We queried the US National Library of Medicine PubMed database to understand the role of ML in developing closed-loop BStim systems to treat epilepsy, movement disorders, and neuropsychiatric disorders. Both neural and non-neural network ML algorithms have successfully been leveraged to create closed-loop systems that perform comparably to open-loop systems. For disorders in which the underlying neural pathophysiology is relatively well understood (e.g., Parkinson's, essential tremor), most work has involved refining ML models that can classify neural signals as aberrant or normal. The same is seen for epilepsy, where most current research has focused on identifying optimal ML model design and integrating closed-loop systems into existing devices. For neuropsychiatric disorders, where the underlying pathologic neural circuitry is still being investigated, research is focused on identifying biomarkers (e.g., local field potentials from brain nuclei) that ML models can use to identify onset of symptoms and stratify severity of disease.
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Affiliation(s)
- Anirudha S Chandrabhatla
- School of Medicine, University of Virginia Health Sciences Center, Charlottesville, VA, 22903, USA
| | - I Jonathan Pomeraniec
- Surgical Neurology Branch, National Institutes of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA.
- Department of Neurosurgery, University of Virginia Health Sciences Center, Charlottesville, VA, 22903, USA.
| | - Taylor M Horgan
- School of Medicine, University of Virginia Health Sciences Center, Charlottesville, VA, 22903, USA
| | - Elizabeth K Wat
- School of Medicine, University of Virginia Health Sciences Center, Charlottesville, VA, 22903, USA
| | - Alexander Ksendzovsky
- Department of Neurosurgery, University of Maryland Medical System, Baltimore, MD, 21201, USA
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Jianbiao M, Xinzui W, Zhaobo L, Juan L, Zhongwei Z, Hui F. EEG signal classification of tinnitus based on SVM and sample entropy. Comput Methods Biomech Biomed Engin 2023; 26:580-594. [PMID: 35850561 DOI: 10.1080/10255842.2022.2075698] [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] [Indexed: 11/03/2022]
Abstract
The prevalence of tinnitus is high and seriously affects the daily life of patients. As the pathogenesis of tinnitus is not yet clear, there is a lack of rapid and objective diagnostic modalities. In order to provide clinicians with an objective diagnostic approach, this paper combines time-frequency domain and non-linear power analysis to investigate the differences in the specificity of the EEG signal in tinnitus patients compared to healthy subjects. In this paper, resting-state electroencephalograms (EEG) were collected from 10 cases each of tinnitus patients and healthy subjects, and the data from the two groups were compared in the δ (0.5 - 3 .5 Hz), θ (4 - 7.5 Hz), α1 (8 - 10 Hz), α2 (10 - 12 Hz), β1 (13 - 18 Hz), β2 (18.5 - 21 Hz), β3 (21.5 - 30 Hz), and γ (30.5 - 44 Hz) bands for the differences in sample entropy values. The results of the resting state experiment revealed that the δ, α2 and β1 band samples of tinnitus patients all had greater entropy values than healthy subjects, with extremely significant differences compared to healthy subjects (p < 0.01). It is mainly concentrated in the δ band in the right parietal region of the cerebral cortex, the α2 band in the central region, and the γ band in the left prefrontal region. Finally, support vector machines combined with optimal feature combinations were used to achieve objective recognition of tinnitus disorders, with an 8.58% increase in accuracy compared to other features. Through the above study, entropy reflects the degree of chaos in the brain and the chaotic characteristics of the resting state EEG signal can characterise the onset of tinnitus, the results of which can help clinicians in the early diagnosis of tinnitus.
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Affiliation(s)
- Mai Jianbiao
- Jihua Institute of Biomedical Engineering and Technology, Ji Hua Laboratory, Foshan, Guangdong, China
- School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, Guangdong, China
| | - Wang Xinzui
- Jihua Institute of Biomedical Engineering and Technology, Ji Hua Laboratory, Foshan, Guangdong, China
| | - Li Zhaobo
- Jihua Institute of Biomedical Engineering and Technology, Ji Hua Laboratory, Foshan, Guangdong, China
| | - Liu Juan
- Jihua Institute of Biomedical Engineering and Technology, Ji Hua Laboratory, Foshan, Guangdong, China
| | - Zhang Zhongwei
- Jihua Institute of Biomedical Engineering and Technology, Ji Hua Laboratory, Foshan, Guangdong, China
| | - Fu Hui
- School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, Guangdong, China
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7
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Chen Z, Liu X, Yang Q, Wang YJ, Miao K, Gong Z, Yu Y, Leonov A, Liu C, Feng Z, Chuan-Peng H. Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review. JAMA Netw Open 2023; 6:e231671. [PMID: 36877519 PMCID: PMC9989906 DOI: 10.1001/jamanetworkopen.2023.1671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
Abstract
IMPORTANCE Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated. OBJECTIVE To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis. EVIDENCE REVIEW PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality. FINDINGS A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%). CONCLUSIONS AND RELEVANCE This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.
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Affiliation(s)
- Zhiyi Chen
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Xuerong Liu
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Qingwu Yang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Yan-Jiang Wang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Kuan Miao
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Zheng Gong
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Yang Yu
- School of Psychology, Third Military Medical University, Chongqing, China
| | - Artemiy Leonov
- Department of Psychology, Clark University, Worcester, Massachusetts
| | - Chunlei Liu
- School of Psychology, Qufu Normal University, Qufu, China
| | - Zhengzhi Feng
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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Joucla C, Gabriel D, Ortega JP, Haffen E. Three simple steps to improve the interpretability of EEG-SVM studies. J Neurophysiol 2022; 128:1375-1382. [PMID: 36169205 DOI: 10.1152/jn.00221.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Machine-learning systems that classify electroencephalography (EEG) data offer important perspectives for the diagnosis and prognosis of a wide variety of neurological and psychiatric conditions, but their clinical adoption remains low. We propose here that much of the difficulties translating EEG-machine-learning research to the clinic result from consistent inaccuracies in their technical reporting, which severely impair the interpretability of their often-high claims of performance. Taking example from a major class of machine-learning algorithms used in EEG research, the support-vector machine (SVM), we highlight three important aspects of model development (normalization, hyperparameter optimization, and cross-validation) and show that, while these three aspects can make or break the performance of the system, they are left entirely undocumented in a shockingly vast majority of the research literature. Providing a more systematic description of these aspects of model development constitute three simple steps to improve the interpretability of EEG-SVM research and, in fine, its clinical adoption.
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Affiliation(s)
- Coralie Joucla
- Laboratoire de Recherches Intégratives en Neurosciences et Psychologie Cognitive (LINC), Université de Bourgogne Franche-Comté, Besançon, France.,FEMTO-ST Institute (CNRS/Université de Bourgogne Franche Comté), Besançon, France
| | - Damien Gabriel
- Laboratoire de Recherches Intégratives en Neurosciences et Psychologie Cognitive (LINC), Université de Bourgogne Franche-Comté, Besançon, France.,Hôpital Universitaire CHRU, Besançon, France
| | - Juan-Pablo Ortega
- Division of Mathematical Sciences, Nanyang Technological University, Singapore
| | - Emmanuel Haffen
- Laboratoire de Recherches Intégratives en Neurosciences et Psychologie Cognitive (LINC), Université de Bourgogne Franche-Comté, Besançon, France.,Hôpital Universitaire CHRU, Besançon, France.,Clinical Psychiatry, Hôpital Universitaire CHRU, Besançon, France
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9
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Blair DS, Soriano-Mas C, Cabral J, Moreira P, Morgado P, Deco G. Complexity changes in functional state dynamics suggest focal connectivity reductions. Front Hum Neurosci 2022; 16:958706. [PMID: 36211126 PMCID: PMC9540393 DOI: 10.3389/fnhum.2022.958706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/03/2022] [Indexed: 11/13/2022] Open
Abstract
The past two decades have seen an explosion in the methods and directions of neuroscience research. Along with many others, complexity research has rapidly gained traction as both an independent research field and a valuable subdiscipline in computational neuroscience. In the past decade alone, several studies have suggested that psychiatric disorders affect the spatiotemporal complexity of both global and region-specific brain activity (Liu et al., 2013; Adhikari et al., 2017; Li et al., 2018). However, many of these studies have not accounted for the distributed nature of cognition in either the global or regional complexity estimates, which may lead to erroneous interpretations of both global and region-specific entropy estimates. To alleviate this concern, we propose a novel method for estimating complexity. This method relies upon projecting dynamic functional connectivity into a low-dimensional space which captures the distributed nature of brain activity. Dimension-specific entropy may be estimated within this space, which in turn allows for a rapid estimate of global signal complexity. Testing this method on a recently acquired obsessive-compulsive disorder dataset reveals substantial increases in the complexity of both global and dimension-specific activity versus healthy controls, suggesting that obsessive-compulsive patients may experience increased disorder in cognition. To probe the potential causes of this alteration, we estimate subject-level effective connectivity via a Hopf oscillator-based model dynamic model, the results of which suggest that obsessive-compulsive patients may experience abnormally high connectivity across a broad network in the cortex. These findings are broadly in line with results from previous studies, suggesting that this method is both robust and sensitive to group-level complexity alterations.
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Affiliation(s)
| | - Carles Soriano-Mas
- Psychiatry and Mental Health Group, Neuroscience Program, Institut d’Investigació Biomèdica de Bellvitge, Barcelona, Spain
- Network Center for Biomedical Research on Mental Health, Carlos III Health Institute, Madrid, Spain
- Department of Social Psychology and Quantitative Psychology, Universitat de Barcelona, Barcelona, Spain
| | - Joana Cabral
- Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal
| | - Pedro Moreira
- Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B’s, PT Government Associate Laboratory, Braga, Portugal
- Psychological Neuroscience Lab, CIPsi, School of Psychology, University of Minho, Braga, Portugal
| | - Pedro Morgado
- Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B’s, PT Government Associate Laboratory, Braga, Portugal
- Clinical Academic Center—Braga, Braga, Portugal
| | - Gustavo Deco
- Facultad de Comunicación, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- School of Psychological Sciences, Monash University, Clayton, VIC, Australia
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11
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Xin X, Feng Y, Zang Y, Lou Y, Yao K, Gao X. Multivariate Classification of Brain Blood-Oxygen Signal Complexity for the Diagnosis of Children with Tourette Syndrome. Mol Neurobiol 2022; 59:1249-1261. [PMID: 34981418 DOI: 10.1007/s12035-021-02707-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 12/17/2021] [Indexed: 10/19/2022]
Abstract
Tourette syndrome (TS) is a childhood-onset neuropsychiatric disorder characterized by the presence of multiple motor and vocal tics. Because of its varied clinical expressions and lack of reliable diagnostic biomarker, present TS diagnosis still depends on qualitative descriptions of symptoms. Our study aimed to investigate whether the complexity of resting state brain activity can serve as a potential biomarker for TS diagnosis, since it has been used successfully in various neuropsychiatric disorders, including two common TS comorbidities: attention-deficit hyperactivity disorder (ADHD) and obsessive-compulsive disorder (OCD). In the current study, we used both univariate analysis and multivariate searchlight analysis with both linear and non-linear classification methods to explore the group differences in the complexity of resting state brain blood oxygen level-dependent (BOLD) signals between 25 TS boys without comorbidity and 25 sex, age and educational years matched healthy controls (HCs). We also investigated the relation between symptom severity in TS patients (YGTSS scores) and complexity indices derived from different analysis methods. We found: i) univariate analysis revealed reduced complexity in TS patients in the left cerebellum, left superior frontal gyrus, and left medial frontal gyrus; ii) multivariate analysis with non-linear classification method achieved the highest performance (accuracy: 0.94, sensitivity: 0.96, specificity: 0.92, AUC: 0.95) in bilateral supplementary motor areas; iii) significant correlations were found between complexity index derived from multivariate analysis with non-linear classification method and Tic severity (YGTSS scores) in the left cerebellum (r = 0.523, with YGTSS phonic) and in the right supplementary motor area (r = 0.767, with YGTSS motor). Taken together, these results suggested that complexity of resting state BOLD activity is a highly effective index for differentiating TS patients from normal controls. It has a good potential to be a quantitative biomarker for TS diagnosis.
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Affiliation(s)
- Xiaoyang Xin
- Center for Psychological Sciences, Zhejiang University, Hangzhou, 310027, China
| | - Yixuan Feng
- Eye Center of the 2Nd Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310009, China.,Zhejiang Provincial Key Lab of Ophthalmology, Hangzhou, 310009, China
| | - Yufeng Zang
- Center for Cognition and Brain Disorders and the Affiliated Hospital, Hangzhou Normal University, Hangzhou, 210015, China
| | - Yuting Lou
- Department of Pediatrics, School of Medicine, the Second Affiliated Hospital, Zhejiang University, Hangzhou, 310058, China
| | - Ke Yao
- Eye Center of the 2Nd Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310009, China. .,Zhejiang Provincial Key Lab of Ophthalmology, Hangzhou, 310009, China.
| | - Xiaoqing Gao
- Center for Psychological Sciences, Zhejiang University, Hangzhou, 310027, China.
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12
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McLoughlin G, Gyurkovics M, Aydin Ü. What Has Been Learned from Using EEG Methods in Research of ADHD? Curr Top Behav Neurosci 2022; 57:415-444. [PMID: 35637406 DOI: 10.1007/7854_2022_344] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Electrophysiological recording methods, including electroencephalography (EEG) and magnetoencephalography (MEG), have an unparalleled capacity to provide insights into the timing and frequency (spectral) composition of rapidly changing neural activity associated with various cognitive processes. The current chapter provides an overview of EEG studies examining alterations in brain activity in ADHD, measured both at rest and during cognitive tasks. While EEG resting state studies of ADHD indicate no universal alterations in the disorder, event-related studies reveal consistent deficits in attentional and inhibitory control and consequently inform the proposed cognitive models of ADHD. Similar to other neuroimaging measures, EEG research indicates alterations in multiple neural circuits and cognitive functions. EEG methods - supported by the constant refinement of analytic strategies - have the potential to contribute to improved diagnostics and interventions for ADHD, underlining their clinical utility.
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Affiliation(s)
- Gráinne McLoughlin
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Máté Gyurkovics
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Ümit Aydin
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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13
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Ozel P, Olamat A, Akan A. A Diagnostic Strategy via Multiresolution Synchrosqueezing Transform on Obsessive Compulsive Disorder. Int J Neural Syst 2021; 31:2150044. [PMID: 34514974 DOI: 10.1142/s0129065721500441] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This research presents a new method for detecting obsessive-compulsive disorder (OCD) based on time-frequency analysis of multi-channel electroencephalogram (EEG) signals using the multi-variate synchrosqueezing transform (MSST). With the evolution of multi-channel sensor implementations, the employment of multi-channel techniques for the extraction of features arising from multi-channel dependency and mono-channel characteristics has become common. MSST has recently been proposed as a method for modeling the combined oscillatory mechanisms of multi-channel signals. It makes use of the concepts of instantaneous frequency (IF) and bandwidth. Electrophysiological data, like other nonstationary signals, necessitates both joint time-frequency analysis and independent time and frequency domain studies. The usefulness and effectiveness of a multi-variate, wavelet-based synchrosqueezing algorithm paired with a band extraction method are tested using electroencephalography data obtained from OCD patients and control groups in this research. The proposed methodology yields substantial results when analyzing differences between patient and control groups.
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Affiliation(s)
- Pinar Ozel
- Biomedical Engineering Department, Nevsehir HBV University, 50300 Nevsehir, Turkey
| | - Ali Olamat
- Biomedical Engineering Program, Yildiz Technical University, 34349 Istanbul, Turkey
| | - Aydin Akan
- Electrical and Electronics Engineering Department, Izmir University of Economics, 35330 Izmir, Turkey
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14
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A Novel Algorithm for Breast Mass Classification in Digital Mammography Based on Feature Fusion. JOURNAL OF HEALTHCARE ENGINEERING 2020; 2020:8860011. [PMID: 33425311 PMCID: PMC7772044 DOI: 10.1155/2020/8860011] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 12/07/2020] [Accepted: 12/13/2020] [Indexed: 01/01/2023]
Abstract
Prompt diagnosis of benign and malignant breast masses is essential for early breast cancer screening. Convolutional neural networks (CNNs) can be used to assist in the classification of benign and malignant breast masses. A persistent problem in current mammography mass classification via CNN is the lack of local-invariant features, which cannot effectively respond to geometric image transformations or changes caused by imaging angles. In this study, a novel model that trains both texton representation and deep CNN representation for mass classification tasks is proposed. Rotation-invariant features provided by the maximum response filter bank are incorporated with the CNN-based classification. The fusion after implementing the reduction approach is used to address the deficiencies of CNN in extracting mass features. This model is tested on public datasets, CBIS-DDSM, and a combined dataset, namely, mini-MIAS and INbreast. The fusion after implementing the reduction approach on the CBIS-DDSM dataset outperforms that of the other models in terms of area under the receiver operating curve (0.97), accuracy (94.30%), and specificity (97.19%). Therefore, our proposed method can be integrated with computer-aided diagnosis systems to achieve precise screening of breast masses.
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15
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Erguzel TT, Uyulan C, Unsalver B, Evrensel A, Cebi M, Noyan CO, Metin B, Eryilmaz G, Sayar GH, Tarhan N. Entropy: A Promising EEG Biomarker Dichotomizing Subjects With Opioid Use Disorder and Healthy Controls. Clin EEG Neurosci 2020; 51:373-381. [PMID: 32043373 DOI: 10.1177/1550059420905724] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Electroencephalography (EEG) signals are known to be nonstationary and often multicomponential signals containing information about the condition of the brain. Since the EEG signal has complex, nonlinear, nonstationary, and highly random behaviour, numerous linear feature extraction methods related to the short-time windowing technique do not satisfy higher classification accuracy. Since biosignals are highly subjective, the symptoms may appear at random in the time scale and very small variations in EEG signals may depict a definite type of brain abnormality it is valuable and vital to extract and analyze the EEG signal parameters using computers. The challenge is to design and develop signal processing algorithms that extract this subtle information and use it for diagnosis, monitoring, and treatment of subjects suffering from psychiatric disorders. For this purpose, finite impulse response-based filtering process was employed rather than traditional time and frequency domain methods. Finite impulse response subbands were analyzed further to obtain feature vectors of different entropy markers and these features were fed into a classifier namely multilayer perceptron. The performances of the classifiers were finally compared considering overall classification accuracies, area under receiver operating characteristic curve scores. Our results underline the potential benefit of the introduced methodology is promising and is to be treated as a clinical interface in dichotomizing substance use disorders subjects and for other medical data analysis studies. The results also indicate that entropy estimators can distinguish normal and opioid use disorder subjects. EEG data and theta frequency band have distinctive capability for almost all types of entropies while nonextensive Tsallis entropy outperforms compared with other types of entropies.
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Affiliation(s)
- Turker Tekin Erguzel
- Department of Software Engineering, Faculty of Engineering and Natural Sciences, Uskudar University, Istanbul, Turkey
| | - Caglar Uyulan
- Department of Mechatronics, Faculty of Engineering, Bulent Evevit University, Zonguldak, Turkey
| | - Baris Unsalver
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| | - Alper Evrensel
- 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
| | - Cemal Onur Noyan
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| | - Baris Metin
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| | - Gul Eryilmaz
- 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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16
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Ozel P, Karaca A, Olamat A, Akan A, Ozcoban MA, Tan O. Intrinsic Synchronization Analysis of Brain Activity in Obsessive-compulsive Disorders. Int J Neural Syst 2020; 30:2050046. [PMID: 32902344 DOI: 10.1142/s012906572050046x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Obsessive-compulsive disorder (OCD) is one of the neuropsychiatric disorders qualified by intrusive and iterative annoying thoughts and mental attitudes that are activated by these thoughts. In recent studies, advanced signal processing techniques have been favored to diagnose OCD. This research suggests four different measurements; intrinsic phase-locked value, intrinsic coherence, intrinsic synchronization likelihood, and intrinsic visibility graph similarity that quantifies the synchronization level and complexity in electroencephalography (EEG) signals. This intrinsic synchronization is achieved by utilizing Multivariate Empirical Mode Decomposition (MEMD), a data-driven method that resolves nonlinear and nonstationary data into their intrinsic mode functions. Our intrinsic technique in this study demonstrates that MEMD-based synchronization analysis gives us much more detailed knowledge rather than utilizing the synchronization method alone. Furthermore, the nonlinear synchronization method presents more consistent results considering OCD heterogeneity. Statistical evaluation using sample [Formula: see text]-test and [Formula: see text]-test has shown the significance of such new methodology.
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Affiliation(s)
- Pinar Ozel
- Department of Biomedical Engineering, Nevsehir Haci Bektas Veli University, Nevsehir, Turkey
| | - Ali Karaca
- Department of Electrical and Electronics Engineering, Inonu University, Malatya, Turkey
| | - Ali Olamat
- Department of Biomedical Engineering, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Aydin Akan
- Department of Electrical and Electronics Engineering, Izmir University of Economics, Izmir
| | - Mehmet Akif Ozcoban
- Department of Electronic and Automation in Junior Technical College, Gaziantep University, Gaziantep, Turkey
| | - Oguz Tan
- Neuropsychiatry Health, Practice and Research Centre, Uskudar University, Istanbul, Turkey
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17
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Altuğlu TB, Metin B, Tülay EE, Tan O, Sayar GH, Taş C, Arikan K, Tarhan N. Prediction of treatment resistance in obsessive compulsive disorder patients based on EEG complexity as a biomarker. Clin Neurophysiol 2020; 131:716-724. [PMID: 32000072 DOI: 10.1016/j.clinph.2019.11.063] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Revised: 10/23/2019] [Accepted: 11/25/2019] [Indexed: 12/26/2022]
Abstract
OBJECTIVE This study aimed to identify an Electroencephalography (EEG) complexity biomarker that could predict treatment resistance in Obsessive compulsive disorder (OCD) patients. Additionally, the statistical differences between EEG complexity values in treatment-resistant and treatment-responsive patients were determined. Moreover, the existence of correlations between EEG complexity and Yale-Brown Obsessive Compulsive Scale (YBOCS) score were evaluated. METHODS EEG data for 29 treatment-resistant and 28 treatment-responsive OCD patients were retrospectively evaluated. Approximate entropy (ApEn) method was used to extract the EEG complexity from both whole EEG data and filtered EEG data, according to 4 common frequency bands, namely delta, theta, alpha, and beta. The random forests method was used to classify ApEn complexity. RESULTS ApEn complexity extracted from beta band EEG segments discriminated treatment-responsive and treatment-resistant OCD patients with an accuracy of 89.66% (sensitivity: 89.44%; specificity: 90.64%). Beta band EEG complexity was lower in the treatment-resistant patients and the severity of OCD, as measured by YBOCS score, was inversely correlated with complexity values. CONCLUSIONS The results indicate that, EEG complexity could be considered a biomarker for predicting treatment response in OCD patients. SIGNIFICANCE The prediction of treatment response in OCD patients might help clinicians devise and administer individualized treatment plans.
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Affiliation(s)
- Tuğçe Ballı Altuğlu
- Uskudar University, Faculty of Engineering and Natural Sciences, Istanbul, Turkey.
| | - Barış Metin
- Uskudar University, Faculty of Humanities and Social Sciences, Department of Psychology, Istanbul, Turkey
| | - Emine Elif Tülay
- Uskudar University, Faculty of Engineering and Natural Sciences, Istanbul, Turkey
| | - Oğuz Tan
- Uskudar University, Faculty of Humanities and Social Sciences, Department of Psychology, Istanbul, Turkey; NPIstanbul Brain Hospital, Department of Psychiatry, Istanbul, Turkey
| | - Gökben Hızlı Sayar
- Uskudar University, Faculty of Humanities and Social Sciences, Department of Psychology, Istanbul, Turkey; NPIstanbul Brain Hospital, Department of Psychiatry, Istanbul, Turkey
| | - Cumhur Taş
- Uskudar University, Faculty of Humanities and Social Sciences, Department of Psychology, Istanbul, Turkey
| | - Kemal Arikan
- Uskudar University, Faculty of Humanities and Social Sciences, Department of Psychology, Istanbul, Turkey
| | - Nevzat Tarhan
- Uskudar University, Faculty of Humanities and Social Sciences, Department of Psychology, Istanbul, Turkey; NPIstanbul Brain Hospital, Department of Psychiatry, Istanbul, Turkey
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18
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Ferreri F, Bourla A, Peretti CS, Segawa T, Jaafari N, Mouchabac S. How New Technologies Can Improve Prediction, Assessment, and Intervention in Obsessive-Compulsive Disorder (e-OCD): Review. JMIR Ment Health 2019; 6:e11643. [PMID: 31821153 PMCID: PMC6930507 DOI: 10.2196/11643] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Revised: 11/29/2018] [Accepted: 03/06/2019] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND New technologies are set to profoundly change the way we understand and manage psychiatric disorders, including obsessive-compulsive disorder (OCD). Developments in imaging and biomarkers, along with medical informatics, may well allow for better assessments and interventions in the future. Recent advances in the concept of digital phenotype, which involves using computerized measurement tools to capture the characteristics of a given psychiatric disorder, is one paradigmatic example. OBJECTIVE The impact of new technologies on health professionals' practice in OCD care remains to be determined. Recent developments could disrupt not just their clinical practices, but also their beliefs, ethics, and representations, even going so far as to question their professional culture. This study aimed to conduct an extensive review of new technologies in OCD. METHODS We conducted the review by looking for titles in the PubMed database up to December 2017 that contained the following terms: [Obsessive] AND [Smartphone] OR [phone] OR [Internet] OR [Device] OR [Wearable] OR [Mobile] OR [Machine learning] OR [Artificial] OR [Biofeedback] OR [Neurofeedback] OR [Momentary] OR [Computerized] OR [Heart rate variability] OR [actigraphy] OR [actimetry] OR [digital] OR [virtual reality] OR [Tele] OR [video]. RESULTS We analyzed 364 articles, of which 62 were included. Our review was divided into 3 parts: prediction, assessment (including diagnosis, screening, and monitoring), and intervention. CONCLUSIONS The review showed that the place of connected objects, machine learning, and remote monitoring has yet to be defined in OCD. Smartphone assessment apps and the Web Screening Questionnaire demonstrated good sensitivity and adequate specificity for detecting OCD symptoms when compared with a full-length structured clinical interview. The ecological momentary assessment procedure may also represent a worthy addition to the current suite of assessment tools. In the field of intervention, CBT supported by smartphone, internet, or computer may not be more effective than that delivered by a qualified practitioner, but it is easy to use, well accepted by patients, reproducible, and cost-effective. Finally, new technologies are enabling the development of new therapies, including biofeedback and virtual reality, which focus on the learning of coping skills. For them to be used, these tools must be properly explained and tailored to individual physician and patient profiles.
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Affiliation(s)
- Florian Ferreri
- Sorbonne Université, Department of Adult Psychiatry and Medical Psychology, APHP, Saint-Antoine Hospital, Paris, France
| | - Alexis Bourla
- Sorbonne Université, Department of Adult Psychiatry and Medical Psychology, APHP, Saint-Antoine Hospital, Paris, France.,Jeanne d'Arc Hospital, INICEA Group, Saint Mandé, France
| | - Charles-Siegfried Peretti
- Sorbonne Université, Department of Adult Psychiatry and Medical Psychology, APHP, Saint-Antoine Hospital, Paris, France
| | - Tomoyuki Segawa
- Sorbonne Université, Department of Adult Psychiatry and Medical Psychology, APHP, Saint-Antoine Hospital, Paris, France
| | - Nemat Jaafari
- INSERM, Pierre Deniker Clinical Research Unit, Henri Laborit Hospital & Experimental and Clinical Neuroscience Laboratory, Poitiers University Hospital, Poitier, France
| | - Stéphane Mouchabac
- Sorbonne Université, Department of Adult Psychiatry and Medical Psychology, APHP, Saint-Antoine Hospital, Paris, France
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19
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Ma Z. Reachability Analysis of Neural Masses and Seizure Control Based on Combination Convolutional Neural Network. Int J Neural Syst 2019; 30:1950023. [DOI: 10.1142/s0129065719500230] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Epileptic seizures arise from synchronous firing of multiple spatially separated neural masses; therefore, many synchrony measures are used for seizure detection and characterization. However, synchrony measures reflect only the overall interaction strength among populations of neurons but cannot reveal the coupling strengths among individual populations, which is more important for seizure control. The concepts of reachability and reachable cluster were proposed to denote the coupling strengths of a set of neural masses. Here, we describe a seizure control method based on coupling strengths using combination convolutional neural network (CCNN) modeling. The neurophysiologically based neural mass model (NMM), which can bridge signal processing and neurophysiology, was used to simulate the proposed controller. Although the adjacency matrix and reachability matrix could not be identified perfectly, the vast majority of adjacency values were identified, reaching 95.64% using the CCNN with an optimal threshold. For cases of discrete and continuous coupling strengths, the proposed controller maintained the average reachable cluster strengths at about 0.1, indicating effective seizure control.
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Affiliation(s)
- Zhen Ma
- Department of Information Engineering, Binzhou University, Binzhou 256600, P. R. China
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20
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A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer’s disease using EEG signals. J Neurosci Methods 2019; 322:88-95. [DOI: 10.1016/j.jneumeth.2019.04.013] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 04/27/2019] [Accepted: 04/27/2019] [Indexed: 11/20/2022]
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21
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Zhu L, Cui G, Cao J, Cichocki A, Zhang J, Zhou C. A Hybrid System for Distinguishing between Brain Death and Coma Using Diverse EEG Features. SENSORS 2019; 19:s19061342. [PMID: 30889817 PMCID: PMC6470643 DOI: 10.3390/s19061342] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2019] [Revised: 03/14/2019] [Accepted: 03/14/2019] [Indexed: 01/16/2023]
Abstract
Electroencephalography (EEG) signals may provide abundant information reflecting the developmental changes in brain status. It usually takes a long time to finally judge whether a brain is dead, so an effective pre-test of brain states method is needed. In this paper, we present a hybrid processing pipeline to differentiate brain death and coma patients based on canonical correlation analysis (CCA) of power spectral density, complexity features, and feature fusion for group analysis. In addition, time-varying power spectrum and complexity were observed based on the analysis of individual patients, which can be used to monitor the change of brain status over time. Results showed three major differences between brain death and coma groups of EEG signal: slowing, increased complexity, and the improvement on classification accuracy with feature fusion. To the best of our knowledge, this is the first scheme for joint general analysis and time-varying state monitoring. Delta-band relative power spectrum density and permutation entropy could effectively be regarded as potential features of discrimination analysis on brain death and coma patients.
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Affiliation(s)
- Li Zhu
- Cognitive Science Department, Xiamen University, Xiamen 361005, China.
| | - Gaochao Cui
- National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki 305-8560, Japan.
| | - Jianting Cao
- Department of Information System, Saitama Institute of Technology, Fukaya, Saitama 369-0203, Japan.
- RIKEN Center for Advanced Intelligence Project, RIKEN, Nihonbashi, Tokyo 103-0027, Japan.
| | - Andrzej Cichocki
- Skolkovo Institute of Science and Technology (Skoltech), 143026 Moscow, Russia.
- Department of Informatics, Nicolaus Copernicus University, 87-100 Torun, Poland.
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China.
| | - Jianhai Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China.
| | - Changle Zhou
- Cognitive Science Department, Xiamen University, Xiamen 361005, China.
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22
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Aydın S, Güdücü Ç, Kutluk F, Öniz A, Özgören M. The impact of musical experience on neural sound encoding performance. Neurosci Lett 2018; 694:124-128. [PMID: 30503922 DOI: 10.1016/j.neulet.2018.11.034] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2018] [Revised: 10/26/2018] [Accepted: 11/13/2018] [Indexed: 11/24/2022]
Abstract
In this study, 64-channel single trial auditory brain oscillations (STABO) have been firstly analyzed by using complexity metrics to observe the effect of musical experience on brain functions. Experimental data was recorded from eyes-opened volunteers during listening the musical chords by piano. Complexity estimation methods were compared to each other for classification of groups (professional musicians and non-musicians) by using both classifiers (support vector machine (SVM), Naive Bayes (NB)) and statistical tests (one-way ANOVA) with respect to electrode locations. Permutation entropy (PermEn) is found to be the best metric (p ≪ 0.0001, 98.37% and 98.41% accuracies for tonal and atonal ensembles) at fronto-temporal regions which are responsible for cognitive task evaluation and perception of sound. PermEn also provides the meaningful results at the whole cortex (p ≪ 0.0001, 99.81% accuracy for both tonal and atonal ensembles) through SVM with Radial Basis kernels superior to Gaussians. Almost the similar performance is also obtained for temporal features. Although, performance improvements are observed for spectral methods with NB, the considerable better results are obtained with SVM. The results indicate that musical stimuli cause pattern variations instead of spectral variations on STABO due to relatively higher neuronal activities around auditory cortex. In conclusion, temporal regions produce response to auditory stimuli, while frontal area integrates the auditory task at the same time. As well, the parietal cortex produces neural information according to the degree of attention generated by environmental changes such as atonal stimuli. It can be clearly stated that musical experience enhances the neural encoding performance of sound tonality at mostly fronto-temporal regions.
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Affiliation(s)
- Serap Aydın
- Department of Biophysics, Faculty of Medicine, University of Hacettepe, Ankara, Turkey
| | - Çağdaş Güdücü
- Department of Biophysics, Faculty of Medicine, University of Dokuz Eylül, Izmir, Turkey
| | - Fırat Kutluk
- Department of Musicology, Faculty of Fine Arts, University of Dokuz Eylül, Izmir, Turkey
| | - Adile Öniz
- Department of Biophysics, Faculty of Medicine, University of Dokuz Eylül, Izmir, Turkey
| | - Murat Özgören
- Department of Biophysics, Faculty of Medicine, University of Dokuz Eylül, Izmir, Turkey
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23
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Yazdi-Ravandi S, Akhavanpour H, Shamsaei F, Matinnia N, Ahmadpanah M, Ghaleiha A, Khosrowabadi R. Differential pattern of brain functional connectome in obsessive-compulsive disorder versus healthy controls. EXCLI JOURNAL 2018; 17:1090-1100. [PMID: 30564085 PMCID: PMC6295628 DOI: 10.17179/excli2018-1757] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Accepted: 10/31/2018] [Indexed: 02/06/2023]
Abstract
Researchers believe that recognition of functional impairment in some of brain networks such as frontal-parietal, default mode network (DMN), anterior medial prefrontal cortex (MPFC) and striatal structures could be a beneficial biomarker for diagnosis of obsessive-compulsive disorder (OCD). Although it is well recognized brain functional connectome in OCD patients shows changes, debate still remains on characteristics of the changes. In this regard, little has been done so far to statistically assess the altered pattern using whole brain electroencephalography. In this study, resting state EEG data of 39 outpatients with OCD and 19 healthy controls (HC) were recorded. After, brain functional network was estimated from the cleaned EEG data using the weighted phase lag index algorithm. Output matrices of OCD group and HCs were then statistically compared to represent meaningful differences. Significant differences in functional connectivity pattern were demonstrated in several regions. As expected the most significant changes were observed in frontal cortex, more significant in frontal-temporal connections (between F3 and F7, and T5 regions). These results in OCD patients are consistent with previous studies and confirm the role of frontal and temporal brain regions in OCD.
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Affiliation(s)
- Saeid Yazdi-Ravandi
- Behavioral Disorders and Substance Abuse Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Hassan Akhavanpour
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University GC, Tehran, Iran
| | - Farshid Shamsaei
- Behavioral Disorders and Substance Abuse Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Nasrin Matinnia
- Department of Nursing, College of Basic Science, Hamadan Branch, Islamic Azad University, Hamadan, Iran
| | - Mohammad Ahmadpanah
- Behavioral Disorders and Substance Abuse Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Ali Ghaleiha
- Behavioral Disorders and Substance Abuse Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Reza Khosrowabadi
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University GC, Tehran, Iran
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Shanir PPM, Khan KA, Khan YU, Farooq O, Adeli H. Automatic Seizure Detection Based on Morphological Features Using One-Dimensional Local Binary Pattern on Long-Term EEG. Clin EEG Neurosci 2018; 49:351-362. [PMID: 29214865 DOI: 10.1177/1550059417744890] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Epileptic neurological disorder of the brain is widely diagnosed using the electroencephalography (EEG) technique. EEG signals are nonstationary in nature and show abnormal neural activity during the ictal period. Seizures can be identified by analyzing and obtaining features of EEG signal that can detect these abnormal activities. The present work proposes a novel morphological feature extraction technique based on the local binary pattern (LBP) operator. LBP provides a unique decimal value to a sample point by weighing the binary outcomes after thresholding the neighboring samples with the present sample point. These LBP values assist in capturing the rising and falling edges of the EEG signal, thus providing a morphologically featured discriminating pattern for epilepsy detection. In the present work, the variability in the LBP values is measured by calculating the sum of absolute difference of the consecutive LBP values. Interquartile range is calculated over the preprocessed EEG signal to provide dispersion measure in the signal. For classification purpose, K-nearest neighbor classifier is used, and the performance is evaluated on 896.9 hours of data from CHB-MIT continuous EEG database. Mean accuracy of 99.7% and mean specificity of 99.8% is obtained with average false detection rate of 0.47/h and sensitivity of 99.2% for 136 seizures.
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Affiliation(s)
- P P Muhammed Shanir
- 1 Department of Electrical and Electronics Engineering, Thangal Kunju Musaliar College of Engineering, Kollam, Kerala, India.,2 Department of Electrical Engineering, Zakir Husain College of Engineering and Technology, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
| | - Kashif Ahmad Khan
- 3 School of Electrical and Electronics Engineering, Lovely Professional University, Phagwara, Punjab, India
| | - Yusuf Uzzaman Khan
- 2 Department of Electrical Engineering, Zakir Husain College of Engineering and Technology, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
| | - Omar Farooq
- 4 Department of Electronics Engineering, Zakir Husain College of Engineering and Technology, AMU Aligarh, Aligarh, Uttar Pradesh, India
| | - Hojjat Adeli
- 5 College of Engineering, The Ohio State University, Columbus, OH, USA
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25
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Yuan S, Zhou W, Chen L. Epileptic Seizure Prediction Using Diffusion Distance and Bayesian Linear Discriminate Analysis on Intracranial EEG. Int J Neural Syst 2017; 28:1750043. [DOI: 10.1142/s0129065717500435] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Epilepsy is a chronic neurological disorder characterized by sudden and apparently unpredictable seizures. A system capable of forecasting the occurrence of seizures is crucial and could open new therapeutic possibilities for human health. This paper addresses an algorithm for seizure prediction using a novel feature — diffusion distance (DD) in intracranial Electroencephalograph (iEEG) recordings. Wavelet decomposition is conducted on segmented electroencephalograph (EEG) epochs and subband signals at scales 3, 4 and 5 are utilized to extract the diffusion distance. The features of all channels composing a feature vector are then fed into a Bayesian Linear Discriminant Analysis (BLDA) classifier. Finally, postprocessing procedure is applied to reduce false prediction alarms. The prediction method is evaluated on the public intracranial EEG dataset, which consists of 577.67[Formula: see text]h of intracranial EEG recordings from 21 patients with 87 seizures. We achieved a sensitivity of 85.11% for a seizure occurrence period of 30[Formula: see text]min and a sensitivity of 93.62% for a seizure occurrence period of 50[Formula: see text]min, both with the seizure prediction horizon of 10[Formula: see text]s. Our false prediction rate was 0.08/h. The proposed method yields a high sensitivity as well as a low false prediction rate, which demonstrates its potential for real-time prediction of seizures.
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Affiliation(s)
- Shasha Yuan
- School of Microelectronics, Shandong University, Jinan 250100, P. R. China
- School of Information Science and Engineering, Qufu Normal University, Rizhao 276826, P. R. China
| | - Weidong Zhou
- School of Microelectronics, Shandong University, Jinan 250100, P. R.China
| | - Liyan Chen
- School of Microelectronics, Shandong University, Jinan 250100, P. R.China
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26
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Chaim-Avancini TM, Doshi J, Zanetti MV, Erus G, Silva MA, Duran FLS, Cavallet M, Serpa MH, Caetano SC, Louza MR, Davatzikos C, Busatto GF. Neurobiological support to the diagnosis of ADHD in stimulant-naïve adults: pattern recognition analyses of MRI data. Acta Psychiatr Scand 2017; 136:623-636. [PMID: 29080396 DOI: 10.1111/acps.12824] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/28/2017] [Indexed: 02/01/2023]
Abstract
OBJECTIVE In adulthood, the diagnosis of attention-deficit/hyperactivity disorder (ADHD) has been subject of recent controversy. We searched for a neuroanatomical signature associated with ADHD spectrum symptoms in adults by applying, for the first time, machine learning-based pattern classification methods to structural MRI and diffusion tensor imaging (DTI) data obtained from stimulant-naïve adults with childhood-onset ADHD and healthy controls (HC). METHOD Sixty-seven ADHD patients and 66 HC underwent high-resolution T1-weighted and DTI acquisitions. A support vector machine (SVM) classifier with a non-linear kernel was applied on multimodal image features extracted on regions of interest placed across the whole brain. RESULTS The discrimination between a mixed-gender ADHD subgroup and individually matched HC (n = 58 each) yielded area-under-the-curve (AUC) and diagnostic accuracy (DA) values of up to 0.71% and 66% (P = 0.003) respectively. AUC and DA values increased to 0.74% and 74% (P = 0.0001) when analyses were restricted to males (52 ADHD vs. 44 HC). CONCLUSION Although not at the level of clinically definitive DA, the neuroanatomical signature identified herein may provide additional, objective information that could influence treatment decisions in adults with ADHD spectrum symptoms.
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Affiliation(s)
- T M Chaim-Avancini
- Laboratory of Psychiatric Neuroimaging (LIM-21), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, Sao Paulo, Brazil
- Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, Sao Paulo, Brazil
| | - J Doshi
- Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - M V Zanetti
- Laboratory of Psychiatric Neuroimaging (LIM-21), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, Sao Paulo, Brazil
- Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, Sao Paulo, Brazil
| | - G Erus
- Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - M A Silva
- Program for Attention Deficit Hyperactivity Disorder (PRODATH), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, Sao Paulo, Brazil
| | - F L S Duran
- Laboratory of Psychiatric Neuroimaging (LIM-21), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, Sao Paulo, Brazil
- Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, Sao Paulo, Brazil
| | - M Cavallet
- Laboratory of Psychiatric Neuroimaging (LIM-21), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, Sao Paulo, Brazil
- Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, Sao Paulo, Brazil
| | - M H Serpa
- Laboratory of Psychiatric Neuroimaging (LIM-21), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, Sao Paulo, Brazil
- Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, Sao Paulo, Brazil
| | - S C Caetano
- Department of Psychiatry, Child and Adolescent Psychiatry Unit (UPIA), Universidade Federal de São Paulo, São Paulo, Brazil
| | - M R Louza
- Program for Attention Deficit Hyperactivity Disorder (PRODATH), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, Sao Paulo, Brazil
| | - C Davatzikos
- Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - G F Busatto
- Laboratory of Psychiatric Neuroimaging (LIM-21), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, Sao Paulo, Brazil
- Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, Sao Paulo, Brazil
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27
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Rafiei MH, Adeli H. A New Neural Dynamic Classification Algorithm. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2017; 28:3074-3083. [PMID: 28749358 DOI: 10.1109/tnnls.2017.2682102] [Citation(s) in RCA: 100] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The keys for the development of an effective classification algorithm are: 1) discovering feature spaces with large margins between clusters and close proximity of the classmates and 2) discovering the smallest number of the features to perform accurate classification. In this paper, a new supervised classification algorithm, called neural dynamic classification (NDC), is presented with the goal of: 1) discovering the most effective feature spaces and 2) finding the optimum number of features required for accurate classification using the patented robust neural dynamic optimization model of Adeli and Park. The new classification algorithm is compared with the probabilistic neural network (PNN), enhanced PNN (EPNN), and support vector machine using two sets of classification problems. The first set consists of five standard benchmark problems. The second set is a large benchmark problem called Mixed National Institute of Standards and Technology database of handwritten digits. In general, NDC yields the most accurate classification results followed by EPNN. A beauty of the new algorithm is the smoothness of convergence curves which is an indication of robustness and good performance of the algorithm. The main aim is to maximize the prediction accuracy.
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28
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Decreased global field synchronization of multichannel frontal EEG measurements in obsessive-compulsive disorders. Med Biol Eng Comput 2017; 56:331-338. [PMID: 28741170 DOI: 10.1007/s11517-017-1689-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Accepted: 07/16/2017] [Indexed: 10/19/2022]
Abstract
Global field synchronization (GFS) quantifies the synchronization level of brain oscillations. The GFS method has been introduced to measure functional synchronization of EEG data in the frequency domain. GFS also detects phase interactions between EEG signals acquired from all of the electrodes. If a considerable amount of local brain neurons has the same phase, these neurons appear to interact with each other. EEG data were received from 17 obsessive-compulsive disorder (OCD) patients and 17 healthy controls (HC). OCD effects on local and large-scale brain circuits were studied. Analysis of the GFS results showed significantly decreased values in the delta and full frequency bands. This research suggests that OCD causes synchronization disconnection in both the frontal and large-scale regions. This may be related to motivational, emotional and cognitive dysfunctions.
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Acharya UR, Bhat S, Koh JEW, Bhandary SV, Adeli H. A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images. Comput Biol Med 2017; 88:72-83. [PMID: 28700902 DOI: 10.1016/j.compbiomed.2017.06.022] [Citation(s) in RCA: 76] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Revised: 06/28/2017] [Accepted: 06/28/2017] [Indexed: 01/17/2023]
Abstract
Glaucoma is an optic neuropathy defined by characteristic damage to the optic nerve and accompanying visual field deficits. Early diagnosis and treatment are critical to prevent irreversible vision loss and ultimate blindness. Current techniques for computer-aided analysis of the optic nerve and retinal nerve fiber layer (RNFL) are expensive and require keen interpretation by trained specialists. Hence, an automated system is highly desirable for a cost-effective and accurate screening for the diagnosis of glaucoma. This paper presents a new methodology and a computerized diagnostic system. Adaptive histogram equalization is used to convert color images to grayscale images followed by convolution of these images with Leung-Malik (LM), Schmid (S), and maximum response (MR4 and MR8) filter banks. The basic microstructures in typical images are called textons. The convolution process produces textons. Local configuration pattern (LCP) features are extracted from these textons. The significant features are selected using a sequential floating forward search (SFFS) method and ranked using the statistical t-test. Finally, various classifiers are used for classification of images into normal and glaucomatous classes. A high classification accuracy of 95.8% is achieved using six features obtained from the LM filter bank and the k-nearest neighbor (kNN) classifier. A glaucoma integrative index (GRI) is also formulated to obtain a reliable and effective system.
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Affiliation(s)
- U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, 599491, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Malaysia.
| | - Shreya Bhat
- Department of Biomedical Engineering, Manipal Institute of Technology, Manipal, 576104, India
| | - Joel E W Koh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore
| | - Sulatha V Bhandary
- Department of Ophthalmology, Kasturba Medical College, Manipal, 576104, India
| | - Hojjat Adeli
- Departments of Neuroscience, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43210, United States; Departments of Neurology, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43210, United States; Departments of Biomedical Engineering, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43210, United States; Departments of Biomedical Informatics, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43210, United States; Departments of Civil, Environmental, and Geodetic Engineering, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43210, United States
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30
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Comparison of hemispheric asymmetry measurements for emotional recordings from controls. Neural Comput Appl 2017. [DOI: 10.1007/s00521-017-3006-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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31
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Abstract
The electroencephalogram (EEG) is the most common tool used to study mental disorders. In the last years, the use of this recording for recognition of negative stress has been receiving growing attention. However, precise identification of this emotional state is still an interesting unsolved challenge. Nowadays, stress presents a high prevalence in developed countries and, moreover, its chronic condition often leads to concomitant physical and mental health problems. Recently, a measure of time series irregularity, such as quadratic sample entropy (QSEn), has been suggested as a promising single index for discerning between emotions of calm and stress. Unfortunately, this index only considers repetitiveness of similar patterns and, hence, it is unable to quantify successfully dynamics associated with the data temporal structure. With the aim of extending QSEn ability for identification of stress from the EEG signal, permutation entropy (PEn) and its modification to be amplitude-aware (AAPEn) have been analyzed in the present work. These metrics assess repetitiveness of ordinal patterns, thus considering causal information within each one of them and obtaining improved estimates of predictability. Results have shown that PEn and AAPEn present a discriminant power between emotional states of calm and stress similar to QSEn, i.e., around 65%. Additionally, they have also revealed complementary dynamics to those quantified by QSEn, thus suggesting a synchronized behavior between frontal and parietal counterparts from both hemispheres of the brain. More precisely, increased stress levels have resulted in activation of the left frontal and right parietal regions and, simultaneously, in relaxing of the right frontal and left parietal areas. Taking advantage of this brain behavior, a discriminant model only based on AAPEn and QSEn computed from the EEG channels P3 and P4 has reached a diagnostic accuracy greater than 80%, which improves slightly the current state of the art. Moreover, because this classification system is notably easier than others previously proposed, it could be used for continuous monitoring of negative stress, as well as for its regulation towards more positive moods in controlled environments.
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32
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Mozos OM, Sandulescu V, Andrews S, Ellis D, Bellotto N, Dobrescu R, Ferrandez JM. Stress Detection Using Wearable Physiological and Sociometric Sensors. Int J Neural Syst 2016; 27:1650041. [DOI: 10.1142/s0129065716500416] [Citation(s) in RCA: 100] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that capture physiological and social responses. We compare the performance using different classifiers including support vector machine, AdaBoost, and [Formula: see text]-nearest neighbor. Our experimental results show that by combining the measurements from both sensor systems, we could accurately discriminate between stressful and neutral situations during a controlled Trier social stress test (TSST). Moreover, this paper assesses the discriminative ability of each sensor modality individually and considers their suitability for real-time stress detection. Finally, we present an study of the most discriminative features for stress detection.
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Affiliation(s)
- Oscar Martinez Mozos
- DETCP, Technical University of Cartagena, Plaza del Hospital, n1, 30202 Cartagena, Spain
| | - Virginia Sandulescu
- Department of Automatic Control and Computer Science, Politehnica University of Bucharest, 313 Splaiul Independentei, Bucharest 060042, Romania
| | - Sally Andrews
- Division of Psychology, Nottingham Trent University, Burton Street, Nottingham, NG1 4BU, UK
| | - David Ellis
- Department of Psychology, Lancaster University, Bailrigg, Lancaster, LA1 4YW, UK
| | - Nicola Bellotto
- School of Computer Science, University of Lincoln, Brayford Pool, Lincoln, LN67TS, UK
| | - Radu Dobrescu
- Department of Automatic Control and Computer Science, Politehnica University of Bucharest, 313 Splaiul Independentei, Bucharest 060042, Romania
| | - Jose Manuel Ferrandez
- DETCP, Technical University of Cartagena, Plaza del Hospital, n1, 30202 Cartagena, Spain
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33
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Amezquita-Sanchez JP, Adeli A, Adeli H. A new methodology for automated diagnosis of mild cognitive impairment (MCI) using magnetoencephalography (MEG). Behav Brain Res 2016; 305:174-80. [DOI: 10.1016/j.bbr.2016.02.035] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Revised: 02/24/2016] [Accepted: 02/26/2016] [Indexed: 10/22/2022]
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34
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Aydin S, Demirtaş S, Ateş K, Tunga MA. Emotion Recognition with Eigen Features of Frequency Band Activities Embedded in Induced Brain Oscillations Mediated by Affective Pictures. Int J Neural Syst 2016; 26:1650013. [DOI: 10.1142/s0129065716500131] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
In this study, singular spectrum analysis (SSA) has been used for the first time in order to extract emotional features from well-defined electroencephalography (EEG) frequency band activities (BAs) so-called delta (0.5–4[Formula: see text]Hz), theta (4–8[Formula: see text]Hz), alpha (8–16[Formula: see text]Hz), beta (16–32[Formula: see text]Hz), gamma (32–64[Formula: see text]Hz). These five BAs were estimated by applying sixth-level multi-resolution wavelet decomposition (MRWD) with Daubechies wavelets (db-8) to single channel nonaveraged emotional EEG oscillations of 6 s for each scalp location over 16 recording sites (Fp1, Fp2, F3, F4, F7, F8, C3, C4, P3, P4, T3, T4, T5, T6, O1, O2). Every trial was mediated by different emotional stimuli which were selected from international affective picture system (IAPS) to induce emotional states such as pleasant (P), neutral (N), and unpleasant (UP). Largest principal components (PCs) of BAs were considered as emotional features and data mining approaches were used for the first time in order to classify both three different (P, N, UP) and two contrasting (P and UP) emotional states for 30 healthy controls. Emotional features extracted from gamma BAs (GBAs) for 16 recording sites provided the high classification accuracies of 87.1% and 100% for classification of three emotional states and two contrasting emotional states, respectively. In conclusion, we found the followings: (1) Eigenspectra of high frequency BAs in EEG are highly sensitive to emotional hemispheric activations, (2) emotional states are mostly mediated by GBA, (3) pleasant pictures induce the higher cortical activation in contrast to unpleasant pictures, (4) contrasting emotions induce opposite cortical activations, (5) cognitive activities are necessary for an emotion to occur.
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Affiliation(s)
- Serap Aydin
- Biomedical Engineering Department, Bahçeşehir University, Beşiktaş Istanbul 34353, Turkey
| | - Serdar Demirtaş
- Department of Biophysics, Gülhane Military Medical Academy, Ankara, Turkey
| | - Kahraman Ateş
- Department of Biophysics, Gülhane Military Medical Academy, Ankara, Turkey
| | - M. Alper Tunga
- Software Engineering Department, Bahçeşehir University, Beşiktaş Istanbul 34353, Turkey
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35
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Tonoyan Y, Looney D, Mandic DP, Van Hulle MM. Discriminating Multiple Emotional States from EEG Using a Data-Adaptive, Multiscale Information-Theoretic Approach. Int J Neural Syst 2016; 26:1650005. [PMID: 26829885 DOI: 10.1142/s0129065716500052] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
A multivariate sample entropy metric of signal complexity is applied to EEG data recorded when subjects were viewing four prior-labeled emotion-inducing video clips from a publically available, validated database. Besides emotion category labels, the video clips also came with arousal scores. Our subjects were also asked to provide their own emotion labels. In total 30 subjects with age range 19–70 years participated in our study. Rather than relying on predefined frequency bands, we estimate multivariate sample entropy over multiple data-driven scales using the multivariate empirical mode decomposition (MEMD) technique and show that in this way we can discriminate between five self-reported emotions ([Formula: see text]). These results could not be obtained by analyzing the relation between arousal scores and video clips, signal complexity and arousal scores, and self-reported emotions and traditional power spectral densities and their hemispheric asymmetries in the theta, alpha, beta, and gamma frequency bands. This shows that multivariate, multiscale sample entropy is a promising technique to discriminate multiple emotional states from EEG recordings.
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Affiliation(s)
- Yelena Tonoyan
- Research Group Neurophysiology, Laboratory for Neuro- and Psychophysiology, O&N II Herestraat 49 – Box 1021, 3000 Leuven, Belgium
| | - David Looney
- Communication and Signal Processing Research Group, Department of Electrical and Electronic Engineering, Imperial College, Room 813, Level 8, Exhibition Road, London, SW7 2BT, United Kingdom
| | - Danilo P. Mandic
- Communication and Signal Processing Research Group, Department of Electrical and Electronic Engineering, Imperial College, Room 813, Level 8, Exhibition Road, London, SW7 2BT, United Kingdom
| | - Marc M. Van Hulle
- Research Group Neurophysiology, Laboratory for Neuro- and Psychophysiology, O&N II Herestraat 49 – Box 1021, 3000 Leuven, Belgium
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36
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Montani F, Oliynyk A, Fadiga L. Superlinear Summation of Information in Premotor Neuron Pairs. Int J Neural Syst 2015; 27:1650009. [PMID: 26906455 DOI: 10.1142/s012906571650009x] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Whether premotor/motor neurons encode information in terms of spiking frequency or by their relative time of firing, which may display synchronization, is still undetermined. To address this issue, we used an information theory approach to analyze neuronal responses recorded in the premotor (area F5) and primary motor (area F1) cortices of macaque monkeys under four different conditions of visual feedback during hand grasping. To evaluate the sensitivity of spike timing correlation between single neurons, we investigated the stimulus dependent synchronization in our population of pairs. We first investigated the degree of correlation of trial-to-trial fluctuations in response strength between neighboring neurons for each condition, and second estimated the stimulus dependent synchronization by means of an information theoretical approach. We compared the information conveyed by pairs of simultaneously recorded neurons with the sum of information provided by the respective individual cells. The information transmission across pairs of cells in the primary motor cortex seems largely independent, whereas information transmission across pairs of premotor neurons is summed superlinearly. The brain could take advantage of both the accuracy provided by the independency of F1 and the synergy allowed by the superlinear information population coding in F5, distinguishing thus the generalizing role of F5.
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Affiliation(s)
- Fernando Montani
- 1 Iflysib, Conicet & Universidad Nacional de La Plata, 59-789 La Plata, Argentina
| | - Andriy Oliynyk
- 2 Section of Human Physiology, Department of Biomedical Sciences and Advanced Therapies, Faculty of Medicine, University of Ferrara, Via Fossato di Mortara 17/19, 44121 Ferrara, Italy
| | - Luciano Fadiga
- 3 IIT@UNIFE Center for Translational Neurophysiology, Istituto Italiano di Tecnologia, Ferrara, Italy.,4 Section of Human Physiology, University of Ferrara, Italy
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37
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Akar SA, Kara S, Latifoğlu F, Bilgiç V. Analysis of the Complexity Measures in the EEG of Schizophrenia Patients. Int J Neural Syst 2015; 26:1650008. [PMID: 26762866 DOI: 10.1142/s0129065716500088] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Complexity measures have been enormously used in schizophrenia patients to estimate brain dynamics. However, the conflicting results in terms of both increased and reduced complexity values have been reported in these studies depending on the patients' clinical status or symptom severity or medication and age status. The objective of this study is to investigate the nonlinear brain dynamics of chronic and medicated schizophrenia patients using distinct complexity estimators. EEG data were collected from 22 relaxed eyes-closed patients and age-matched healthy controls. A single-trial EEG series of 2 min was partitioned into identical epochs of 20 s intervals. The EEG complexity of participants were investigated and compared using approximate entropy (ApEn), Shannon entropy (ShEn), Kolmogorov complexity (KC) and Lempel-Ziv complexity (LZC). Lower complexity values were obtained in schizophrenia patients. The most significant complexity differences between patients and controls were obtained in especially left frontal (F3) and parietal (P3) regions of the brain when all complexity measures were applied individually. Significantly, we found that KC was more sensitive for detecting EEG complexity of patients than other estimators in all investigated brain regions. Moreover, significant inter-hemispheric complexity differences were found in the frontal and parietal areas of schizophrenics' brain. Our findings demonstrate that the utilizing of sensitive complexity estimators to analyze brain dynamics of patients might be a useful discriminative tool for diagnostic purposes. Therefore, we expect that nonlinear analysis will give us deeper understanding of schizophrenics' brain.
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Affiliation(s)
- S. Akdemir Akar
- Institute of Biomedical Engineering, Fatih University, Buyukcekmece, İstanbul 34500, Turkey
| | - S. Kara
- Institute of Biomedical Engineering, Fatih University, Buyukcekmece, İstanbul 34500, Turkey
| | - F. Latifoğlu
- Department of Biomedical Engineering, Erciyes University, Kayseri, Turkey
| | - V. Bilgiç
- Psychiatry Department, Faculty of Medicine, Fatih University, İstanbul 34500, Turkey
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38
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Acharya UR, Bhat S, Faust O, Adeli H, Chua ECP, Lim WJE, Koh JEW. Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection. Eur Neurol 2015; 74:268-87. [DOI: 10.1159/000441975] [Citation(s) in RCA: 72] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2015] [Accepted: 10/27/2015] [Indexed: 11/19/2022]
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39
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Akdemir Akar S, Kara S, Agambayev S, Bilgiç V. Nonlinear analysis of EEGs of patients with major depression during different emotional states. Comput Biol Med 2015; 67:49-60. [PMID: 26496702 DOI: 10.1016/j.compbiomed.2015.09.019] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2015] [Revised: 09/22/2015] [Accepted: 09/24/2015] [Indexed: 12/23/2022]
Affiliation(s)
- Saime Akdemir Akar
- Institute of Biomedical Engineering, Fatih University, Istanbul 34500, Turkey.
| | - Sadık Kara
- Institute of Biomedical Engineering, Fatih University, Istanbul 34500, Turkey
| | - Sümeyra Agambayev
- Institute of Biomedical Engineering, Fatih University, Istanbul 34500, Turkey
| | - Vedat Bilgiç
- Department of Psychiatry, School of Medicine, Fatih University, Istanbul 34500, Turkey
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