A novel approach to identify the brain regions that best classify ADHD by means of EEG and deep learning

Abstract

Objective: Attention-Deficit Hyperactivity Disorder (ADHD) is one of the most widespread neurodevelopmental disorders diagnosed in childhood. ADHD is diagnosed by following the guidelines of Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). According to DSM-5, ADHD has not yet identified a specific cause, and thus researchers continue to investigate this field. Therefore, the primary objective of this work is to present a study to find the subset of channels or brain regions that best classify ADHD vs Typically Developing children by means of Electroencephalograms (EEG). Methods: To achieve this goal, we present a novel approach to identify the brain regions that best classify ADHD using EEG and Deep Learning (DL). First, we perform a filtering and artefact removal process on the EEG signal. Then we generate different subsets of EEG channels depending on their location on the scalp (hemispheres, lobes, sets of lobes and single channels) and using backward and forward stepwise feature selection methods. Finally, we feed the DL neural network with each set, and compute the f1-score. Results and conclusions: Based on the obtained results, the Frontal Lobe (FL) (0.8081 f1-score) and the Left Hemisphere (LH) (0.8056 f1-score) provide more significant information detecting individuals with ADHD, than using the entire set of EEG Channels (0.8067 f1-score). However, when combining the Temporal, Parietal and Occipital Lobes (TL, PL, OL), better results (0.8097 f1-score) were obtained compared with using only the FL and LH subsets. The best performance was obtained using Feature Selection Methods. In the case of the Backward Stepwise Feature Selection method, a combination of 14 EEG channels yielded a 0.8281 f1-score. Similarly, using the Forward Stepwise Feature Selection method, a combination of 11 EEG channels yielded a 0.8271 f1-score. These findings hold significant value for physicians in the quest to better understand the underlying causes of ADHD.

Keywords

• Attention-deficit hyperactivity disorder
• Brain regions
• Electroencephalogram
• Feature selection methods
• Deep learning