Optimizing ADHD Detection: An Autoencoder Approach for Multimodal Classification

Attention Deficit Hyperactivity Disorder (ADHD) is commonly found in children, with the prevalence in adults said to be under-reported. In this paper, we aim to detect adult ADHD symptoms using two autoencoder architectures. We train and test on the novel multimodal ADHD dataset recorded under the Intelligent Sensing ADHD Trial in collaboration with the Cumbria, Northumberland, Tyne and Wear NHS Foundation Trust, UK. The autoencoder architectures perform an image reconstruction task to optimise the latent bottleneck feature space to perform downstream classification tasks to detect ADHD subjects or control participants. The RGB video data is specifically exploited to inform the autoencoders about the hyperactivity symptoms. The Audio data is used to further support hyperactivity symptoms while also hoping to gain scope on inattentive symptoms. The self report questionnaire is a subjective measure, where the individual can provide details of ADHD symptoms that they experience. It is a vital data source to include in the proposed work for providing the autoencoders previously unidentifiable symptoms. An ablation study is undertaken to demonstrate the effectiveness of the individual data modality, attempting to distinguish the associated discriminatory power. Using rigorous validation techniques, we achieve a state-of-the-art classification accuracy, sensitivity and specificity of 98.9%, 99.2% and 98.5% respectively. With ADHD classification being a preliminary subjective decision, the proposed work demonstrates that an objective system can provide robust support to ADHD clinicians in the future.