A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD)
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[1] Yuhai Wu,et al. Statistical Learning Theory , 2021, Technometrics.
[2] Aamir Saeed Malik,et al. Automatic diagnosis of alcohol use disorder using EEG features , 2016, Knowl. Based Syst..
[3] Russell Greiner,et al. Accuracy of automated classification of major depressive disorder as a function of symptom severity , 2016, NeuroImage: Clinical.
[4] Bijan Raahemi,et al. Data mining EEG signals in depression for their diagnostic value , 2015, BMC Medical Informatics and Decision Making.
[5] Jun Sakuma,et al. Privacy-preserving genome-wide association studies on cloud environment using fully homomorphic encryption , 2015, BMC Medical Informatics and Decision Making.
[6] Serhat Ozekes,et al. Feature Selection and Classification of Electroencephalographic Signals , 2015, Clinical EEG and neuroscience.
[7] Aamir Saeed Malik,et al. Review on EEG and ERP predictive biomarkers for major depressive disorder , 2015, Biomed. Signal Process. Control..
[8] Vidya K. Sudarshan,et al. A Novel Depression Diagnosis Index Using Nonlinear Features in EEG Signals , 2015, European Neurology.
[9] Joel E. W. Koh,et al. Computer-Aided Diagnosis of Depression Using EEG Signals , 2015, European Neurology.
[10] Ronald C Kessler,et al. The economic burden of adults with major depressive disorder in the United States (2005 and 2010). , 2015, The Journal of clinical psychiatry.
[11] Dewen Hu,et al. Unsupervised classification of major depression using functional connectivity MRI , 2014, Human brain mapping.
[12] Larry Culpepper,et al. Misdiagnosis of bipolar depression in primary care practices. , 2014, The Journal of clinical psychiatry.
[13] S. Olbrich,et al. Functional connectivity in major depression: Increased phase synchronization between frontal cortical EEG-source estimates , 2014, Psychiatry Research: Neuroimaging.
[14] P. Willner,et al. The neurobiology of depression and antidepressant action , 2013, Neuroscience & Biobehavioral Reviews.
[15] M. Arns,et al. EEG biomarkers in major depressive disorder: Discriminative power and prediction of treatment response , 2013, International review of psychiatry.
[16] G. A. Kenna,et al. Bacchus by Caravaggio as the Visual Diagnosis of Alcohol Use Disorder from the Fifth Edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) , 2013, Front. Psychiatry.
[17] M. Härter,et al. The prevalence of mental disorders in older people in Western countries – a meta-analysis , 2013, Ageing Research Reviews.
[18] Haifang Li,et al. Machine learning classifier using abnormal brain network topological metrics in major depressive disorder , 2012, Neuroreport.
[19] Subha D. Puthankattil,et al. CLASSIFICATION OF EEG SIGNALS IN NORMAL AND DEPRESSION CONDITIONS BY ANN USING RWE AND SIGNAL ENTROPY , 2012 .
[20] H. Adeli,et al. Fractality analysis of frontal brain in major depressive disorder. , 2012, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.
[21] Klaus P. Ebmeier,et al. Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder. , 2012, Brain : a journal of neurology.
[22] U. Rajendra Acharya,et al. Application of Non-Linear and Wavelet Based Features for the Automated Identification of Epileptic EEG signals , 2012, Int. J. Neural Syst..
[23] Marc M. Van Hulle,et al. Enhancing the Yield of High-Density electrode Arrays through Automated electrode Selection , 2012, Int. J. Neural Syst..
[24] A. Mechelli,et al. Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review , 2012, Neuroscience & Biobehavioral Reviews.
[25] Tien-Wen Lee,et al. The implication of functional connectivity strength in predicting treatment response of major depressive disorder: A resting EEG study , 2011, Psychiatry Research: Neuroimaging.
[26] Mahdi Jalili,et al. EEG-based functional networks in schizophrenia , 2011, Comput. Biol. Medicine.
[27] Clifford R. Jack,et al. Diagnostic neuroimaging across diseases , 2011, NeuroImage.
[28] R. H. McAllister-Williams,et al. The use of the EEG in measuring therapeutic drug action: focus on depression and antidepressants , 2011, Journal of psychopharmacology.
[29] Reza Rostami,et al. Classifying depression patients and normal subjects using machine learning techniques , 2011, 2011 19th Iranian Conference on Electrical Engineering.
[30] Peng Xu,et al. A comparative study of different references for EEG default mode network: The use of the infinity reference , 2010, Clinical Neurophysiology.
[31] H. Benali,et al. Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI , 2009, Neuroradiology.
[32] J. Sleigh,et al. Permutation entropy of the electroencephalogram: a measure of anaesthetic drug effect. , 2008, British journal of anaesthesia.
[33] J. P. Hamilton,et al. Neural Substrates of Increased Memory Sensitivity for Negative Stimuli in Major Depression , 2008, Biological Psychiatry.
[34] Shanbao Tong,et al. More normal EEGs of depression patients during mental arithmetic than rest , 2007, 2007 Joint Meeting of the 6th International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging.
[35] Jang-Han Lee,et al. Detrended fluctuation analysis of resting EEG in depressed outpatients and healthy controls , 2007, Clinical Neurophysiology.
[36] Hiroshi Motoda,et al. Computational Methods of Feature Selection , 2022 .
[37] Christoph Lehmann,et al. Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG) , 2007, Journal of Neuroscience Methods.
[38] A. Fingelkurts,et al. Impaired functional connectivity at EEG alpha and theta frequency bands in major depression , 2007, Human brain mapping.
[39] Hojjat Adeli,et al. A Wavelet-Chaos Methodology for Analysis of EEGs and EEG Subbands to Detect Seizure and Epilepsy , 2007, IEEE Transactions on Biomedical Engineering.
[40] James R. MacFall,et al. Dorsolateral Prefrontal Cortex and Anterior Cingulate Cortex White Matter Alterations in Late-Life Depression , 2006, Biological Psychiatry.
[41] Hiroshi Mamitsuka,et al. Selecting features in microarray classification using ROC curves , 2006, Pattern Recognit..
[42] U. Rajendra Acharya,et al. Non-linear analysis of EEG signals at various sleep stages , 2005, Comput. Methods Programs Biomed..
[43] M. Lowe,et al. Activity and Connectivity of Brain Mood Regulating Circuit in Depression: A Functional Magnetic Resonance Study , 2005, Biological Psychiatry.
[44] S. Rombouts,et al. Disturbed fluctuations of resting state EEG synchronization in Alzheimer's disease , 2005, Clinical Neurophysiology.
[45] Ioannis Kalatzis,et al. Design and implementation of an SVM-based computer classification system for discriminating depressive patients from healthy controls using the P600 component of ERP signals , 2004, Comput. Methods Programs Biomed..
[46] Mahmood Nazar Mohamed,et al. Analysis of the Psychometric Properties of the Malay Version of Beck Depression Inventory II (BDI-II) Among Postpartum Women in Kedah, North West of Peninsular Malaysia. , 2004, The Malaysian journal of medical sciences : MJMS.
[47] C. Stam,et al. Synchronization likelihood: an unbiased measure of generalized synchronization in multivariate data sets , 2002 .
[48] V. Knott,et al. EEG power, frequency, asymmetry and coherence in male depression , 2001, Psychiatry Research: Neuroimaging.
[49] P Berg,et al. A multiple source approach to the correction of eye artifacts. , 1994, Electroencephalography and clinical neurophysiology.
[50] D. Hosmer,et al. Applied Logistic Regression , 1991 .
[51] Theiler,et al. Spurious dimension from correlation algorithms applied to limited time-series data. , 1986, Physical review. A, General physics.
[52] Aamir Saeed Malik,et al. Electroencephalogram (EEG)-based computer-aided technique to diagnose major depressive disorder (MDD) , 2017, Biomed. Signal Process. Control..
[53] Jaeseung Jeong,et al. Decreased Phase Synchronization of the EEG in Patients with Major Depressive Disorder , 2007 .
[54] J. Davies,et al. Incorporation of amino acids within the surface reactive layers of bioactive glass in vitro: an XPS study , 2000, Journal of materials science. Materials in medicine.
[55] H. Jasper,et al. The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology. , 1999, Electroencephalography and clinical neurophysiology. Supplement.
[56] F. Takens. Detecting strange attractors in turbulence , 1981 .