Identification of attention deficit hyperactivity disorder with deep learning model

• Main Author: Kasim, Ö (Author)
• Format: Article
• Language: English
• Published: Springer Science and Business Media Deutschland GmbH 2023
• Subjects: Attention deficit hyperactivity disorder; Biomedical signal processing; Brain; Control groups; Deep learning; Deep learning model; Discriminant analysis; Diseases; Electroencephalography; Electrophysiology; Learning models; Learning systems; Linear discriminant analyze; Neighborhood component analysis; Neurobehavioural; Power spectral density; Signal decomposition; Support vector machines; Support vectors machine; Unstable behavior; Variational mode decomposition; Wavelet decomposition
• Online Access: View Fulltext in Publisher

 This article explores the detection of Attention Deficit Hyperactivity Disorder, a neurobehavioral disorder, from electroencephalography signals. Due to the unstable behavior of electroencephalography signals caused by complex neuronal activity in the brain, frequency analysis methods are required to extract the hidden patterns. In this study, the feature extraction was performed with the Multitaper and Multivariate Variational Mode Decomposition methods. Then, these features were analyzed with the neighborhood component analysis and the features that contribute effectively to the classification were selected. The deep learning model including the convolution, pooling, and bidirectional long short term cell and fully connected layer was trained with the selected features. The trained model could effectively classify the subjects with Attention Deficit Hyperactivity Disorder with a deep learning model, support vector machines and linear discriminant analysis. The experiments were validated with an Attention Deficit Hyperactivity Disorder open access dataset (https://doi.org/10.21227/rzfh-zn36). In validation, the deep learning model was able to classify 1210 test samples (600 subjects in the control group as Normal and 610 subjects in the ADHD group as ADHD) in 0.1 s with an accuracy of 95.54%. This accuracy rate is quite high compared to the Linear Discriminant Analysis (76.38%) and Support Vector Machines (81.69%). Experimental results showed that the proposed approach can innovatively classify Attention Deficit Hyperactivity Disorder subjects from the Control group effectively. © 2023, Australasian College of Physical Scientists and Engineers in Medicine.