Computer-Aided Diagnosis of Depression Using EEG Signals

被引:141
作者
Acharya, U. Rajendra [1 ,2 ,3 ]
Sudarshan, Vidya K. [1 ]
Adeli, Hojjat [4 ,5 ,6 ,7 ,8 ]
Santhosh, Jayasree [3 ]
Koh, Joel E. W. [1 ]
Adeli, Amir [4 ]
机构
[1] Ngee Ann Polytech, Dept Elect & Comp Engn, Singapore 599489, Singapore
[2] SIM Univ, Sch Sci & Technol, Dept Biomed Engn, Singapore, Singapore
[3] Univ Malaya, Fac Engn, Dept Biomed Engn, Kuala Lumpur, Malaysia
[4] Ohio State Univ, Dept Neurol, Columbus, OH 43210 USA
[5] Ohio State Univ, Dept Neurosci, Columbus, OH 43210 USA
[6] Ohio State Univ, Dept Biomed Engn, Columbus, OH 43210 USA
[7] Ohio State Univ, Dept Biomed Informat, Columbus, OH 43210 USA
[8] Ohio State Univ, Dept Elect & Comp Engn, Columbus, OH 43210 USA
关键词
Brain stimulation; EEG; Emotion; Depression; Linear methods; Nonlinear methods; WAVELET-CHAOS METHODOLOGY; AUTOMATIC IDENTIFICATION; APPROXIMATE ENTROPY; QUANTITATIVE EEG; ALPHA ASYMMETRY; NEURAL-NETWORK; FRONTAL BRAIN; TIME-SERIES; COMPLEXITY; PARAMETERS;
D O I
10.1159/000381950
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
The complex, nonlinear and non-stationary electroencephalogram (EEG) signals are very tedious to interpret visually and highly difficult to extract the significant features from them. The linear and nonlinear methods are effective in identifying the changes in EEG signals for the detection of depression. Linear methods do not exhibit the complex dynamical variations in the EEG signals. Hence, chaos theory and nonlinear dynamic methods are widely used in extracting the EEG signal features for computer-aided diagnosis (CAD) of depression. Hence, this article presents the recent efforts on CAD of depression using EEG signals with a focus on using nonlinear methods. Such a CAD system is simple to use and may be used by the clinicians as a tool to confirm their diagnosis. It should be of a particular value to enable the early detection of depression. (C) 2015 S. Karger AG, Basel
引用
收藏
页码:329 / 336
页数:8
相关论文
共 98 条
[1]   Non-linear analysis of EEG signals at various sleep stages [J].
Acharya, R ;
Faust, O ;
Kannathal, N ;
Chua, T ;
Laxminarayan, S .
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2005, 80 (01) :37-45
[2]   Computer-aided diagnosis of alcoholism-related EEG signals [J].
Acharya, U. Rajendra ;
Vidya, S. ;
Bhat, Shreya ;
Adeli, Hojjat ;
Adeli, Amir .
EPILEPSY & BEHAVIOR, 2014, 41 :257-263
[3]   AUTOMATED DIAGNOSIS OF EPILEPSY USING CWT, HOS AND TEXTURE PARAMETERS [J].
Acharya, U. Rajendra ;
Yanti, Ratna ;
Wei, Zheng Jia ;
Krishnan, M. Muthu Rama ;
Hong, Tan Jen ;
Martis, Roshan Joy ;
Min, Lim Choo .
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, 2013, 23 (03)
[4]   ANALYSIS AND AUTOMATIC IDENTIFICATION OF SLEEP STAGES USING HIGHER ORDER SPECTRA [J].
Acharya U, Rajendra ;
Chua, Eric Chern-Pin ;
Chua, Kuang Chua ;
Min, Lim Choo ;
Tamura, Toshiyo .
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, 2010, 20 (06) :509-521
[5]  
Acharya UR, 2015, EUR NEUROL
[6]   Analysis of EEG records in an epileptic patient using wavelet transform [J].
Adeli, H ;
Zhou, Z ;
Dadmehr, N .
JOURNAL OF NEUROSCIENCE METHODS, 2003, 123 (01) :69-87
[7]  
Adeli H., 1995, Machine Learning - Neural Networks, Genetic Algorithms, and Fuzzy Systems
[8]  
Adeli H., 1998, Neurocomputing for Design Automation, P35
[9]   A spatio-temporal wavelet-chaos methodology for EEG-based diagnosis of Alzheimer's disease [J].
Adeli, Hojjat ;
Ghosh-Dastidar, Samanwoy ;
Dadmehr, Nahid .
NEUROSCIENCE LETTERS, 2008, 444 (02) :190-194
[10]   A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy [J].
Adeli, Hojjat ;
Ghosh-Dastidar, Samanwoy ;
Dadmehr, Nahid .
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2007, 54 (02) :205-211