Objective Attention deficit hyperactivity disorder (ADHD), is a general neurodevelopmental syndrome. This affects both adults and children, causing issues like hyperactivity, inattention, and impulsivity. Diagnosis, typically reliant on patient narratives and questionnaires, can sometimes be inaccurate, leading to distress. We propose utilizing empirical mode decomposition (EMD) for feature extraction and a machine learning (ML) algorithm to categorize ADHD and control.