Optimising ADHD Detection: An Autoencoder Approach for Multimodal Classification

• Published in: IEEE transactions on artificial intelligence pp. 1 - 11
• Main Authors: Nash, Christian, Nair, Rajesh, Naqvi, Syed Mohsen
• Format: Journal Article
• Language: English
• Published: IEEE 2025
• Subjects: Accuracy; Attention Deficit Hyperactivity Disorder; Autoencoders; Computational modeling; Costs; Data models; Deep Learning; Machine Learning; Mental health; Multimodal Data; Pediatrics; Recording; Spectrogram; Training
• ISSN: 2691-4581; 2691-4581
• DOI: 10.1109/TAI.2025.3592157

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 I...