Enhancing ADHD Detection Using Diva Interview-Based Audio Signals and A Two-Stream Network

Shuanglin Li, Yang Sun, Rajesh Nair, Syed Mohsen Naqvi

Abstract: Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental condition that results in altered behaviour in social development and communication patterns. However, due to the dearth of medical psychiatrists globally, the diagnosis of ADHD is frequently delayed. With the burgeoning development of artificial intelligence, it is rational to introduce deep learning to facilitate the ADHD diagnosis. Previous deep learning methods mainly use functional magnetic resonance imaging (fMRI) or Electroencephalography (EEG) signals to detect ADHD, where the data are expensive to acquire, i.e. equipment cost and specialised staff for data collection. Over the past years, speech signals have gained increasing attention owing to their cost-effectiveness in data collection and non-intrusive characteristics. In this work, based on the Diagnostic Interview for ADHD in adults (DIVA), we design a questionnaire and collected the audio data of ADHD patients and normal controls in collaboration with the Cumbria, Northumberland, Tyne and Wear NHS Foundation Trust. Besides, we propose a two-stream model (TSM) to exploit local and global features to assist ADHD detection. Applying the TSM to the collected real ADHD audio data, the performance of the proposed method is promising with an average accuracy of 84.9%.