Key result
An SVM classifier with radial basis function using five significant features from EEG signals achieved a classification accuracy of 93.8%, sensitivity of 92%, and specificity of 95.8% for depression.
Why the study?
Does an automated classification system using DCT and nonlinear dynamics accurately classify normal and depression EEG signals?
Does an automated classification system using DCT and nonlinear dynamics accurately classify normal and depression EEG signals?
An automated classification system using discrete cosine transform and nonlinear dynamics with an SVM classifier can accurately distinguish between normal and depression EEG signals.
May aid EEG-based depression screening; hypothesis-generating and requires prospective validation before clinical adoption.
Depression is a mental disorder that affects emotional and physical state of a person. It is a state of extreme sadness 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 naive 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%.
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Bairy et al. (2015) studied Depression. Automated classification using discrete cosine transform and nonlinear dynamics with SVM classifier was evaluated on Classification accuracy, sensitivity, and specificity. An SVM classifier with radial basis function using five significant features from EEG signals achieved a classification accuracy of 93.8%, sensitivity of 92%, and specificity of 95.8% for depression.
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