A Novel Depression Diagnosis Index Using Nonlinear Features in EEG Signals

作者：Acharya U Rajendra; Sudarshan Vidya K^*; Adeli Hojjat; Santhosh Jayasree; Koh Joel E W; Puthankatti Subha D; Adeli Amir

来源：European Neurology, 2015, 74(1-2): 79-83.

DOI：10.1159/000438457

• Depression
• Electroencephalogram signal
• Nonlinear analysis
• Recurrence quantification analysis
• Higher order spectra
• Classifiers
• Sample entropy
• Largest Lyapunov exponent
• Detrended fluctuation analysis
• Hurst's exponent

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%.

• 出版日期2015