Detection of ADHD Disorder in Children using Layer-wise Relevance Propagation and Convolutional Neural Network: an EEG Analysis

Attention-Deficit-Hyperactivity-Disorder (ADHD) is a neurodevelopmental disorder that begins in early childhood and often persists into adulthood, causing personality issues and social behavior problems. Thus, detecting ADHD in its early stages and developing an effective therapy is of tremendous interest. This study presents a deep learning-based model for ADHD diagnosis in children.

The 'First-National-EEG-Data-Analysis-Competition-with-Clinical-Application' dataset is used for this purpose. Following preprocessing, data is segmented into 3-second epochs, and frequency features are extracted from these epochs. The Fourier transform is applied to each channel separately, and the resulting two-dimensional matrix (channel×frequency) for each epoch is used as the Convolutional Neural Network's (CNN) input. The CNN is made up of two convolutional layers, two max pooling layers and two fully connected layers as well as the output layer (a total of 9 layers) for classification. To improve the method's performance, the output of the classification of each input variable is analyzed. In other words, the role of each channel/frequency in the final classification is being investigated using the Layer-wise Relevance Propagation (LRP) algorithm.

According to the results of the LRP algorithm, only efficient channels are employed as Convolutional Neural Network (CNN) inputs in the following stage. This method yields a final accuracy of 94.52% for validation data. In this study, the feature space is visualized, useful channels are selected, and deep structure capabilities are exploited to diagnose ADHD disorder.

The findings suggest that the proposed technique can be used to effectively diagnose ADHD in children.