EEG-based mild depressive detection using feature selection methods and classifiers

Computer Methods and Programs in Biomedicine - Tập 136 - Trang 151-161 - 2016
Xiaowei Li1, Bin Hu1, Shuting Sun1, Hanshu Cai1
1School of Information Science and Engineering, Lanzhou University, Lanzhou, China

Tài liệu tham khảo

Marcus, 2012 Brundtland, 2001, From the World Health Organization. Mental health: new understanding, new hope, JAMA, 286, 2391, 10.1001/jama.286.19.2391 Othieno, 2014, Depression among university students in Kenya: prevalence and sociodemographic correlates, J. Affect. Disord, 165, 120, 10.1016/j.jad.2014.04.070 Peltzer, 2013, Depression and associated factors among university students in Western Nigeria, J. Psychol. Afr, 23, 459, 10.1080/14330237.2013.10820652 Ibrahim, 2013, A systematic review of studies of depression prevalence in university students, J. Psychiatr. Res, 47, 391, 10.1016/j.jpsychires.2012.11.015 Ibrahim, 2012, Analysis of an Egyptian study on the socioeconomic distribution of depressive symptoms among undergraduates, Soc. Psychiatry Psychiatr. Epidemiol, 47, 927, 10.1007/s00127-011-0400-x Oppong Asante, 2014, Prevalence and determinants of depressive symptoms among university students in Ghana, J. Affect. Disord, 171c, 161 Sung, 2005, 595 Giannakopoulos, 2009, Electrophysiological markers of rapid cognitive decline in mild cognitive impairment, Front. Neurol. Neurosci, 24, 39, 10.1159/000197898 Parvinnia, 2014, Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm, J. King Saud Univ. Comput. Inf. Sci, 26, 1 Henriques, 1991, Left frontal hypoactivation in depression, J. Abnorm. Psychol, 100, 535, 10.1037/0021-843X.100.4.535 Fingelkurts, 2006, Composition of brain oscillations in ongoing EEG during major depression disorder, Neurosci. Res, 56, 133, 10.1016/j.neures.2006.06.006 Erguzel, 2015, Feature selection and classification of electroencephalographic signals: an artificial neural network and genetic algorithm based approach, Clin. EEG Neurosci, 46, 321, 10.1177/1550059414523764 Hosseinifard, 2011, Classifying depression patients and normal subjects using machine learning techniques, 1 Hosseinifard, 2013, Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal, Comput. Methods Programs Biomed, 109, 339, 10.1016/j.cmpb.2012.10.008 Spyrou, 2016, Geriatric depression symptoms coexisting with cognitive decline: a comparison of classification methodologies, Biomed. Signal Process. Control, 25, 118, 10.1016/j.bspc.2015.10.006 George, 1998, Abnormal facial emotion recognition in depression: serial testing in an ultra-rapid-cycling patient, Behav. Modif, 22, 192, 10.1177/01454455980222007 Gotlib, 2004, Attentional biases for negative interpersonal stimuli in clinical depression, J. Abnorm. Psychol, 113, 121, 10.1037/0021-843X.113.1.121 Kellough, 2008, Time course of selective attention in clinically depressed young adults: an eye tracking study, Behav. Res. Ther, 46, 1238, 10.1016/j.brat.2008.07.004 Rinck, 2005, A comparison of attentional biases and memory biases in women with social phobia and major depression, J. Abnorm. Psychol, 114, 62, 10.1037/0021-843X.114.1.62 Gotlib, 2004, Attentional biases for negative interpersonal stimuli in clinical depression, J. Abnorm. Psychol, 113, 127, 10.1037/0021-843X.113.1.121 Beck, 1996, Comparison of Beck Depression Inventories-IA and-II in psychiatric outpatients, J. Pers. Assess, 67, 588, 10.1207/s15327752jpa6703_13 Gong, 2011, Revision of the Chinese facial affective picture system, Chin. Ment. Health J., 25, 40 Blackhart, 2006, Can EEG asymmetry patterns predict future development of anxiety and depression? A preliminary study, Biol. Psychol, 72, 46, 10.1016/j.biopsycho.2005.06.010 Akar, 2015, Nonlinear analysis of EEGs of patients with major depression during different emotional states, Comput. Biol. Med, 67, 49, 10.1016/j.compbiomed.2015.09.019 Ferree, 2001, Scalp electrode impedance, infection risk, and EEG data quality, Clin. Neurophysiol, 112, 536, 10.1016/S1388-2457(00)00533-2 Hu, 2011, EEG-based cognitive interfaces for ubiquitous applications: developments and challenges, IEEE Intell. Syst, 26, 46, 10.1109/MIS.2011.58 Knott, 2001, EEG power, frequency, asymmetry and coherence in male depression, Psychiatry Res, 106, 123, 10.1016/S0925-4927(00)00080-9 Omel'chenko, 2002, Changes in the EEG-rhythms in endogenous depressive disorders and the effect of pharmacotherapy, Hum. Physiol, 28, 275, 10.1023/A:1015596416791 Begić, 2011, Quantitative electroencephalography in schizophrenia and depression, Psychiatr. Danub, 23, 355 Ricardo-Garcell, 2009, EEG sources in a group of patients with major depressive disorders, Int. J. Psychophysiol, 71, 70, 10.1016/j.ijpsycho.2008.07.021 Hjorth, 1975, Time domain descriptors and their relation to a particular model for generation of EEG activity, 3 Fan, 2006, Use of ANN and complexity measures in cognitive EEG discrimination Bachmann, 2015, Lempel Ziv complexity of EEG in depression Liang, 2015, EEG entropy measures in anesthesia, Front. Comput. Neurosci, 9, 10.3389/fncom.2015.00016 Nicolaou, 2012, Detection of epileptic electroencephalogram based on permutation entropy and support vector machines, Expert Syst. Appl, 39, 202, 10.1016/j.eswa.2011.07.008 Elder, 1996, A statistical perspective on KDD, 83 Lotte, 2007, A review of classification algorithms for EEG-based brain-computer interfaces, J. Neural Eng, 4, R1, 10.1088/1741-2560/4/2/R01 Hall, 1999, Feature selection for machine learning: comparing a correlation-based filter approach to the wrapper Kohavi, 1997, Wrappers for feature subset selection, Artif. Intell, 97, 273, 10.1016/S0004-3702(97)00043-X Goldberg, 1989 Gütlein, 2009, Large scale attribute selection using wrappers, 332 Hall, 2003, Benchmarking attribute selection techniques for discrete class data mining, IEEE Trans. Knowl. Data Eng, 15, 1437, 10.1109/TKDE.2003.1245283 Sohaib, 2013, Evaluating classifiers for emotion recognition using EEG, 492 Lehmann, 2007, Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG), J. Neurosci. Methods, 161, 342, 10.1016/j.jneumeth.2006.10.023 Hsu, 2003 Patel, 2016, Studying depression using imaging and machine learning methods, Neuroimage Clin, 10, 115, 10.1016/j.nicl.2015.11.003 Wang, 2014, Emotional state classification from EEG data using machine learning approach, Neurocomputing, 129, 94, 10.1016/j.neucom.2013.06.046 Cieslak, 2009, A framework for monitoring classifiers' performance: when and why failure occurs?, Knowl. Inf. Syst, 18, 83, 10.1007/s10115-008-0139-1 Shenoy, 2006, Towards adaptive classification for BCI, J. Neural Eng, 3, R13, 10.1088/1741-2560/3/1/R02 Parvin, 2008, MKNN: modified k-nearest neighbor Yu, 2010, An EEG-based classification system of Passenger's motion sickness level by using feature extraction/selection technologies Mantri, 2015, Non invasive EEG signal processing framework for real time depression analysis Alhaj, 2011, The use of the EEG in measuring therapeutic drug action: focus on depression and antidepressants, J. Psychopharmacol, 25, 1175, 10.1177/0269881110388323 Baskaran, 2012, The neurobiology of the EEG biomarker as a predictor of treatment response in depression, Neuropharmacology, 63, 507, 10.1016/j.neuropharm.2012.04.021 Li, 2015, A study on EEG-based brain electrical source of mild depressed subjects, Comput. Methods Programs Biomed, 120, 135, 10.1016/j.cmpb.2015.04.009 Koo, 2015, P124. QEEG and CSD power analysis in depression, Clin. Neurophysiol, 126, e151, 10.1016/j.clinph.2015.04.251