Computer Aided Diagnosis System to Distinguish Adhd from Similar Behavioral Disorders
Siba Shankar Beriha
Department of Pediatrics Sardar Vallavbhai Patel Post Graduate Institute of PaediatricsSCB Medical College Chandinchowk, Cuttack 753002, India.
Corresponding Author E-mail: dssb1975@gmail.com
Abstract: ADHD is one of the most prevalent psychiatric disorder of childhood, characterized by inattention and distractibility, with or without accompanying hyperactivity. The main aim of this research work is to develop a Computer Aided Diagnosis (CAD) technique with minimal steps that can differentiate the ADHD children from the other similar children behavioral disorders such as anxiety, depression and conduct disorder based on the Electroencephalogram (EEG) signal features and symptoms. The proposed technique is based on soft computing and bio inspired computing algorithms. Four non-linear features are extracted from the EEG such as Higuchi fractal dimension, Katz fractal dimension, Sevick fractal dimension and Lyapunov exponent and 14 symptoms which are most important in differentiation are extracted by experts in the field of psychiatry. Particle Swarm Optimization (PSO) tuned Back Propagation Neural Network (BPNN) and PSO tuned Radial Basis Function (RBF) employed as a classifier. By investigating these integrated features, we obtained good classification accuracy. Simulation results suggest that the proposed technique offer high potential in the diagnosis of ADHD and may be a good preliminary assistant for psychiatrists in diagnosing high risk behavioral disorders of children.
Keywords: ADHD; Bio-inspired Computing; Electroencephalogram; RBF; Soft Computing Algorithm Back to TOC