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Automated major depressive disorder detection using melamine pattern with EEG signals

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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.

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Authors and Affiliations

  1. Department of Management Information, College of Management, Sakarya University, Sakarya, Turkey

    Emrah Aydemir

  2. Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey

    Turker Tuncer & Sengul Dogan

  3. School of Management and Enterprise, University of Southern Queensland, Toowoomba, Australia

    Raj Gururajan

  4. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, 599489, Singapore

    U. Rajendra Acharya

  5. Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore, Singapore

    U. Rajendra Acharya

  6. Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan

    U. Rajendra Acharya

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  1. Emrah Aydemir
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  2. Turker Tuncer
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  3. Sengul Dogan
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  4. Raj Gururajan
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  5. U. Rajendra Acharya
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Correspondence to U. Rajendra Acharya.

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Aydemir, E., Tuncer, T., Dogan, S. et al. Automated major depressive disorder detection using melamine pattern with EEG signals. Appl Intell 51, 6449–6466 (2021). https://doi.org/10.1007/s10489-021-02426-y

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