Keywords : Variational Mode Decomposition
PERFORMANCE ANALYSIS OF SUPERVISED LEARNING ALGORITHMS FOR IDENTIFICATION OF AUTISM SPECTRUM DISORDER USING EEG SIGNALS
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 9, Pages 1156-1167
Autism Spectrum Disorder (ASD) is a sort of developmental issue of the
nervous system, with center impedances in social connections, creative mind,
communications, adaptability of thought,intrigue andrestricted range of activities.
Examination of electroencephalographic (EEG) signals based on autism is explored in
this work. Even so, it is critical to identify autism by the analysis of the EEG signal.
Hence feature extraction based on the EEG signals takes part a prominent role in
autism recognition. A practical feature extraction technique variational mode
decomposition (VMD) to diagnose autism is narrated in this paper. Further, the features
extracted are fed to classifiers ANN, KNNand SVM to stratify autism.SVM classifier
shows a finer classification performance when compared to extant techniques