Abstract
Late detection of depression is having detrimental consequences including suicide thus there is a serious need for an accurate computer-aided system for early diagnosis of depression. In this research, we suggested a novel strategy for the diagnosis of depression based on several geometric features derived from the Electroencephalography (EEG) signal shape of the second-order differential plot (SODP). First, various geometrical features of normal and depression EEG signals were derived from SODP including standard descriptors, a summation of the angles between consecutive vectors, a summation of distances to coordinate, a summation of the triangle area using three successive points, a summation of the shortest distance from each point relative to the 45-degree line, a summation of the centroids to centroid distance of successive triangles, central tendency measure and summation of successive vector lengths. Second, Binary Particle Swarm Optimization was utilized for the selection of suitable features. At last, the features were fed to support vector machine and k-nearest neighbor (KNN) classifiers for the identification of normal and depressed signals. The performance of the proposed framework was evaluated by the recorded bipolar EEG signals from 22 normal and 22 depressed subjects. The results provide an average classification accuracy of 98.79% with the KNN classifier using city-block distance in a ten-fold cross-validation strategy. The proposed system is accurate and can be used for the early diagnosis of depression. We showed that the proposed geometrical features are better than extracted features in the time, frequency, time-frequency domains as it helps in visual inspection and provide up to 17.56% improvement in classification accuracy in contrast to those features.
Highlights
1.1 BackgroundDepression is taken into consideration as one of the common mental disorders worldwide that affect the different aspects of a person life
Aimed at the pattern of second-order differential plot (SODP) for normal and depression EEG signals, we proposed the number of 26 nonlinear geometrical features including Standard descriptors (STD), SAV, Summation of distances to coordinate (SDC), STA, SSHD, successive triangles (SCC), 19 Central tendency measure (CTM) (i.e.CTM5, CTM10, ..., CTM90, CTM95) and successive vector lengths (SSVL) for classification of normal and depressed subjects
We showed that mean and standard deviation of geometrical features in normal EEG signals are more than depression EEG signal; besides, SODP of depression EEG signals has more rhythmic and regular shape than normal EEG signals which it can may due to increasing connection between the synapses in depressed brain in compared to normal brain
Summary
1.1 BackgroundDepression is taken into consideration as one of the common mental disorders worldwide that affect the different aspects of a person life. According to the report of the World Health Organization (WHO) in 2017, there are more than 300 million people, who are living with depression. This situation had an increase of more than 18% between 2005 and 2015 [1]. Depression is treatable by medication cure and psychotherapy session, there are different reasons in which many people currently suffer from the illness worldwide. These reasons are dealt with lack of awareness, no timely diagnosis, improper detection, high cost of treatment and so on
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