Open AccessInvestigating EEG Signals of Autistic Individuals Using Detrended Fluctuation Analysis
Author(s) -
Menaka Radhakrishnan,
Karthik Ramamurthy,
Avantika Kothandaraman,
Gauri Madaan,
Harini Machavaram
Publication year - 2021
Publication title -
traitement du signal/ts. traitement du signal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.279
H-Index - 11
eISSN - 1958-5608
pISSN - 0765-0019
DOI - 10.18280/ts.380528
Subject(s) - detrended fluctuation analysis , electroencephalography , hurst exponent , autism spectrum disorder , psychology , pattern recognition (psychology) , similarity (geometry) , scalp , mismatch negativity , audiology , autism , computer science , artificial intelligence , developmental psychology , neuroscience , mathematics , statistics , medicine , geometry , scaling , image (mathematics) , anatomy
To record all electrical activity of the human brain, an electroencephalogram (EEG) test using electrodes attached to the scalp is conducted. Analysis of EEG signals plays an important role in the diagnosis and treatment of brain diseases in the biomedical field. One of the brain diseases found in early ages include autism. Autistic behaviours are hard to distinguish, varying from mild impairments, to intensive interruption in daily life. The non-linear EEG signals arising from various lobes of the brain have been studied with the help of a robust technique called Detrended Fluctuation Analysis (DFA). Here, we study the EEG signals of Typically Developing (TD) and children with Autism Spectrum Disorder (ASD) using DFA. The Hurst exponents, which are the outputs of DFA, are used to find out the strength of self-similarity in the signals. Our analysis works towards analysing if DFA can be a helpful analysis for the early detection of ASD.