Automated classification of depression electroencephalographic signals using discrete cosine transform and nonlinear dynamics

G. Muralidhar Bairy, Shreya Bhat, Lim Wei Jie Eugene, U. C. Niranjan, Subha D. Puthankattil, Paul K. Joseph

Research output: Contribution to journalArticle

10 Citations (Scopus)


Depression is a mental disorder that affects emotional and physical state of a person. It is a state of extremesadness and dejection. The electroencephalographic (EEG) signals can be used to detect the alterations in the brain's electrochemical potential. The highly irregular and complex EEG signal variations can be determined by different processing tools. The present work is based on the automated classification of the normal and depression EEG signals. The discrete cosine transform (DCT) decomposes the normal and depression EEG signals into different frequency sub-bands. Nonlinear methods such as sample entropy, correlation dimension, fractal dimension, largest Lyapunov exponent, Hurst exponent and detrended fluctuation analysis are applied to the DCT coefficients and the extracted characteristic features are ranked using t-value. These significant features are fed to decision tree (DT), support vector machine (SVM), k-nearest neighbor (kNN) and naïve Bayes (NB) classifiers. Five significant features are selected and the SVM classifier with radial basis function (RBF) results in a classification accuracy of 93.8%, sensitivity of 92% and specificity of 95.8%.

Original languageEnglish
Pages (from-to)635-640
Number of pages6
JournalJournal of Medical Imaging and Health Informatics
Issue number3
Publication statusPublished - 01-01-2015


All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging
  • Health Informatics

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