Automated major depressive disorder detection using melamine pattern with EEG signals

Automated major depressive disorder detection using melamine pattern with EEG signals

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 timeconsuming, subjective and may cause human errors. Therefore, nowadays various automated systems have been developed todiagnose MDD accurately and rapidly. In this work, we have proposed a novel automated MDD detection system using EEGsignals. Our proposed model hasthreesteps: (i) Melamine pattern and discrete wavelet transform (DWT)- based multileveledfeature generation, (ii) selection of most relevant features using neighborhood component analysis (NCA) and (iii) classificationusing support vector machine (SVM) and k nearest neighbor (kNN) classifiers. The novelty of this work is the application ofmelamine pattern. The molecular structure of melamine (also named chemistry spider- ChemSpider) is used to generate 1536features. Also, various statistical features are extracted from DWT coefficients. The NCA is used to select the most relevantfeatures 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% and99.05% using Weighted kNN and Quadratic SVM respectively using A2A1 EEG channel. We have developed the automateddepression model using a big dataset and yielded high classification accuracies. These results indicate that our presented modelcan be used in mental health clinics to confirm the manual diagnosis of psychiatrists.

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