January 1, 2015European Neurology270 citations
View Full PaperA Novel Depression Diagnosis Index Using Nonlinear Features in EEG Signals
Structured PICO
Does an automated EEG-based Depression Diagnosis Index using nonlinear features accurately diagnose depression?
P
Population
Subjects with depression and healthy controls (implied for classification task)
I
Intervention
Automated EEG-based diagnosis using nonlinear methods (fractal dimension, largest Lyapunov exponent, sample entropy, detrended fluctuation analysis, Hurst's exponent, higher order spectra, and recurrence quantification analysis) and a novel Depression Diagnosis Index (DDI) with SVM classifier
O
Outcome
Classification performance (accuracy, sensitivity, specificity)surrogate
A novel Depression Diagnosis Index based on nonlinear EEG features combined with an SVM classifier achieved high accuracy (98%) in diagnosing depression.
Abstract
Depression is a mental disorder characterized by persistent occurrences of lower mood states in the affected person. The electroencephalogram (EEG) signals are highly complex, nonlinear, and nonstationary in nature. The characteristics of the signal vary with the age and mental state of the subject. The signs of abnormality may be invisible to the naked eyes. Even when they are visible, deciphering the minute changes indicating abnormality is tedious and time consuming for the clinicians. This paper presents a novel method for automated EEG-based diagnosis of depression using nonlinear methods: fractal dimension, largest Lyapunov exponent, sample entropy, detrended fluctuation analysis, Hurst's exponent, higher order spectra, and recurrence quantification analysis. A novel Depression Diagnosis Index (DDI) is presented through judicious combination of the nonlinear features. The DDI calculated automatically based on the EEG recordings can be used to diagnose depression objectively using just one numeric value. Also, these features extracted from nonlinear methods are ranked using the t value and fed to the support vector machine (SVM) classifier. The SVM classifier yielded the highest classification performance with an average accuracy of about 98%, sensitivity of about 97%, and specificity of about 98.5%.