RETRACTED ARTICLE: Recognition of autism in children via electroencephalogram behaviour using particle swarm optimization based ANFIS classifier

Abstract

Autism spectrum disorders (ASD) are pervasive neuro developmental conditions portrayed by disabilities in social intercommunication, besides stereotyped conduct. Since Electroencephalogram (EEG) recording together with analysis stands one among the basic devices in diagnosing along with recognizing the issue in neurophysiology, utilized the signals of EEG aimed at diagnosing persons with ASD. These signals have heaps of data which mirror the conduct of brain functions which thusly catches the marker for autism, help to early analyze and speed the treatment. To beat such disadvantage, this given work proposes an Adaptive Neuro-Fuzzy Inference System classifier joined with Particle Swarm Optimization that is named as PSO-ANFIS for classifying the diagnosing signals of EEG. To start with, utilizing Savitzky Golay (S-G) filter pre-processed the input signal, after that by variational mode decomposition (VMD) disintegrated the signal. Presently, features are extracted; additionally, these are trained and also characterized utilizing PSO-ANFIS, which classifies whether the signal seems normal or else autism signal. The proposed strategy classified the abnormal besides normal signal, all the more precisely, when contrasted with the current ones are established through the experiment.