Automated major depressive disorder detection using melamine pattern with EEG signals

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 51; no. 9; pp. 6449 - 6466
• Main Authors: Aydemir, Emrah, Tuncer, Turker, Dogan, Sengul, Gururajan, Raj, Acharya, U. Rajendra
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2021; Springer Nature B.V
• Subjects: 30th Anniversary Special Issue; Artificial Intelligence; Automation; Classification; Classifiers; Computer Science; Diagnosis; Discrete Wavelet Transform; Electroencephalography; Feature extraction; Human error; Machines; Manufacturing; Mechanical Engineering; Melamine; Mental depression; Mental health; Molecular structure; Processes; Support vector machines; Wavelet transforms; Major depression detection; Melamine pattern; Statistical feature generation; NCA selector; EEG signal processing
• DOI: 10.1007/s10489-021-02426-y
• ISSN: 0924-669X

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.