Automated major depressive disorder detection using melamine pattern with EEG signals

Abstract

Major depressive disorder (MDD) is one of the most common modern ailments affected huge population throughout the world. The electroencephalogram (EEG) signal is widely used to screen the MDD. The manual diagnosis of MDD using EEG is time consuming, subjective and may cause human errors. Therefore, nowadays various automated systems have been developed to diagnose MDD accurately and rapidly. In this work, we have proposed a novel automated MDD detection system using EEG signals. Our proposed model has three steps: (i) Melamine pattern and discrete wavelet transform (DWT)-based multileveled feature generation, (ii) selection of most relevant features using neighborhood component analysis (NCA) and (iii) classification using support vector machine (SVM) and k nearest neighbor (kNN) classifiers. The novelty of this work is the application of melamine pattern. The molecular structure of melamine (also named chemistry spider-ChemSpider) is used to generate 1536 features. Also, various statistical features are extracted from DWT coefficients. The NCA is used to select the most relevant features and these selected features are classified using SVM and kNN classifiers. The presented model attained greater than 95% accuracies using all channels with quadratic SVM classifier. Our results obtained highest classification accuracy of 99.11% and 99.05% using Weighted kNN and Quadratic SVM respectively using A2A1 EEG channel. We have developed the automated depression model using a big dataset and yielded high classification accuracies. These results indicate that our presented model can be used in mental health clinics to confirm the manual diagnosis of psychiatrists.

Automated Detection of Major Depressive Disorder With EEG Signals: A Time Series Classification Using Deep Learning

“…Most of the studies on this topic have followed either feature extraction, time-frequency representation, or subsampling approaches. Research works based on feature extraction methods focused mainly on using band powers as well as other hand-crafted features for the classification task using EEG data and machine learning techniques [29][30][31]. Other research works, which employ the STFT approach, were interested in applying image processing techniques on transformed EEG to image data.…”

“…Aydemir et al [31] also proposed a classification model based on hand-crafted features in three steps to detect MDD. They used melamine patterns and discrete wavelet transform (DWT) to generate features.…”

Automatic Diagnosis of Mild Cognitive Impairment Based on Spectral, Functional Connectivity, and Nonlinear EEG-Based Features

“…Many researchers are urged to employ machine learning techniques for automated EEG signal analysis in light of the advancements in computer science and artificial intelligence. For instance, numerous studies have been conducted to automatically detect different neurological disorders such depression [6][7][8], epilepsy [9][10][11], seizure [12][13][14][15], Parkinson's disease [16,17], and schizophrenia [18].…”

DKPNet41: Directed knight pattern network-based cough sound classification model for automatic disease diagnosis

“…Their high levels of attained classification accuracy render them suitable for many biomedical applications [ 13 , 14 ]. However, automated disease diagnostic systems incorporating machine learning based on handcrafted features can also be effective [15] , [16] , [17] . While deep networks can achieve high accuracy with large data sets [18] , handcrafted models exact lower computational costs [16] .…”

“…However, automated disease diagnostic systems incorporating machine learning based on handcrafted features can also be effective [15] , [16] , [17] . While deep networks can achieve high accuracy with large data sets [18] , handcrafted models exact lower computational costs [16] . As such, either method can be deployed in intelligent medical systems to optimize accuracy or computational efficiency [19] , [20] , [21] .…”