Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach.

Deep learning techniques have recently made considerable advances in the field of artificial intelligence. These methodologies can assist psychologists in early diagnosis of mental disorders and preventing severe trauma. Major Depression Disorder (MDD) is a common and serious medical condition whose exact manifestations are not fully understood. So, early discovery of MDD patients helps to cure or limit the adverse effects. Electroencephalogram (EEG) is prominently used to study brain diseases such as MDD due to having high temporal resolution information, and being a noninvasive, inexpensive and portable method. This paper has proposed an EEG-based deep learning framework that automatically discriminates MDD patients from healthy controls. First, the relationships among EEG channels in the form of effective brain connectivity analysis are extracted by Generalized Partial Directed Coherence (GPDC) and Direct directed transfer function (dDTF) methods. A novel combination of sixteen connectivity methods (GPDC and dDTF in eight frequency bands) was used to construct an image for each individual. Finally, the constructed images of EEG signals are applied to the five different deep learning architectures. The first and second algorithms were based on one and two-dimensional convolutional neural network (1DCNN-2DCNN). The third method is based on long short-term memory (LSTM) model, while the fourth and fifth algorithms utilized a combination of CNN with LSTM model namely, 1DCNN-LSTM and 2DCNN-LSTM. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals. The efficiency of the proposed algorithms is evaluated on resting state EEG data obtained from 30 healthy subjects and 34 MDD patients. The experiments show that the 1DCNN-LSTM applied on constructed image of effective connectivity achieves best results with accuracy of 99.24% due to specific architecture which captures the presence of spatial and temporal relations in the brain connectivity. The proposed method as a diagnostic tool is able to help clinicians for diagnosing the MDD patients for early diagnosis and treatment.

Major depressive disorder assessment via enhanced k-nearest neighbor method and EEG signals.

The aim of this paper is to introduce a novel method using short-term EEG signals to separate depressed patients from healthy controls. Five common frequency bands (delta, theta, alpha, beta and gamma) were extracted from the signals as linear features, as well as, wavelet packet decomposition to break down signals into certain frequency bands. Afterwards, two entropy measures, namely sample entropy and approximate entropy were applied on the wavelet packet coefficients as nonlinear features, and significant features were selected via genetic algorithm (GA). Three machine-learning algorithms were used for classification; including support vector machine (SVM), multilayer perceptron (MLP) a novel enhanced K-nearest neighbors (E-KNN), which uses GA to optimize the feature-space distances and provides a feature importance index. The highest accuracy obtained by using frequency-based features was from gamma oscillations which resulted in 91.38%. Performance of nonlinear features were better compared to the frequency-based features and the results showed 94.28% accuracy. The combination of the features showed 98.44% accuracy with the new proposed E-KNN classifier.