title: Computer-Aided Diagnosis of Depression Using EEG Signals.
abstract: 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.
authors.affiliation: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore.
authors.forename: U Rajendra
authors.initials: UR
authors.lastname: Acharya
authors.forename: Vidya K
authors.initials: VK
authors.lastname: Sudarshan
authors.forename: Hojjat
authors.initials: H
authors.lastname: Adeli
authors.forename: Jayasree
authors.initials: J
authors.lastname: Santhosh
authors.forename: Joel E W
authors.initials: JE
authors.lastname: Koh
authors.forename: Amir
authors.initials: A
authors.lastname: Adeli
mesh_terms: D003863:Depression / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted