Diagnosing ASD with fractal analysis

Abstract

Purpose -Neuroscience is providing new tools to potentially improve diagnosis and classification of autism spectrum disorder (ASD) based on biomarkers. The purpose of this paper, is to describe certain applications of fractal analysis, a tool used to measure information complexity observed within electroencephalograph (EEG) signals and neurogenetic code. It is argued here that a better method of diagnosis of ASD may exist based on these new tools. Design/methodology/approach -Selective review of literature focused on the diagnosis of ASD and recent technological advances in scientific approaches to diagnosis of ASD. It is argued that higher levels of complex, coherent data are inversely related to pathology; in biological systems, lower complexity EEG during specific tasks may reveal pathology. Findings -Clinicians and researchers are exploring new ways to describe mental illness based on biomarkers to improve reliability and validity of diagnostic methods. Specific application of chaos theory in the form of fractal analysis shows promise as one possible method. Originality/value -This is a conceptual paper addressing the advantages of employing fractal analysis of EEG and genomics for the diagnosis of ASD.