Automated diagnosis of autism

In search of a mathematical marker

Shreya Bhat, U. Rajendra Acharya, Hojjat Adeli, G. Muralidhar Bairy, Amir Adeli

Research output: Contribution to journalArticle

29 Citations (Scopus)

Abstract

Autism is a type of neurodevelopmental disorder affecting the memory, behavior, emotion, learning ability, and communication of an individual. An early detection of the abnormality, due to irregular processing in the brain, can be achieved using electroencephalograms (EEG). The variations in the EEG signals cannot be deciphered by mere visual inspection. Computer-aided diagnostic tools can be used to recognize the subtle and invisible information present in the irregular EEG pattern and diagnose autism. This paper presents a state-of-theart review of automated EEG-based diagnosis of autism. Various time domain, frequency domain, time-frequency domain, and nonlinear dynamics for the analysis of autistic EEG signals are described briefly. A focus of the review is the use of nonlinear dynamics and chaos theory to discover the mathematical biomarkers for the diagnosis of the autism analogous to biological markers. A combination of the time-frequency and nonlinear dynamic analysis is the most effective approach to characterize the nonstationary and chaotic physiological signals for the automated EEGbased diagnosis of autism spectrum disorder (ASD). The features extracted using these nonlinear methods can be used as mathematical markers to detect the early stage of autism and aid the clinicians in their diagnosis. This will expedite the administration of appropriate therapies to treat the disorder.

Original languageEnglish
Pages (from-to)851-861
Number of pages11
JournalReviews in the Neurosciences
Volume25
Issue number6
DOIs
Publication statusPublished - 01-01-2014

Fingerprint

Autistic Disorder
Nonlinear Dynamics
Electroencephalography
Biomarkers
Aptitude
Emotions
Communication
Learning
Brain
Therapeutics

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)

Cite this

Bhat, Shreya ; Acharya, U. Rajendra ; Adeli, Hojjat ; Bairy, G. Muralidhar ; Adeli, Amir. / Automated diagnosis of autism : In search of a mathematical marker. In: Reviews in the Neurosciences. 2014 ; Vol. 25, No. 6. pp. 851-861.
@article{b96eed9bc1de45b3b938eec095598ef1,
title = "Automated diagnosis of autism: In search of a mathematical marker",
abstract = "Autism is a type of neurodevelopmental disorder affecting the memory, behavior, emotion, learning ability, and communication of an individual. An early detection of the abnormality, due to irregular processing in the brain, can be achieved using electroencephalograms (EEG). The variations in the EEG signals cannot be deciphered by mere visual inspection. Computer-aided diagnostic tools can be used to recognize the subtle and invisible information present in the irregular EEG pattern and diagnose autism. This paper presents a state-of-theart review of automated EEG-based diagnosis of autism. Various time domain, frequency domain, time-frequency domain, and nonlinear dynamics for the analysis of autistic EEG signals are described briefly. A focus of the review is the use of nonlinear dynamics and chaos theory to discover the mathematical biomarkers for the diagnosis of the autism analogous to biological markers. A combination of the time-frequency and nonlinear dynamic analysis is the most effective approach to characterize the nonstationary and chaotic physiological signals for the automated EEGbased diagnosis of autism spectrum disorder (ASD). The features extracted using these nonlinear methods can be used as mathematical markers to detect the early stage of autism and aid the clinicians in their diagnosis. This will expedite the administration of appropriate therapies to treat the disorder.",
author = "Shreya Bhat and Acharya, {U. Rajendra} and Hojjat Adeli and Bairy, {G. Muralidhar} and Amir Adeli",
year = "2014",
month = "1",
day = "1",
doi = "10.1515/revneuro-2014-0036",
language = "English",
volume = "25",
pages = "851--861",
journal = "Reviews in the Neurosciences",
issn = "0334-1763",
publisher = "Walter de Gruyter GmbH & Co. KG",
number = "6",

}

Automated diagnosis of autism : In search of a mathematical marker. / Bhat, Shreya; Acharya, U. Rajendra; Adeli, Hojjat; Bairy, G. Muralidhar; Adeli, Amir.

In: Reviews in the Neurosciences, Vol. 25, No. 6, 01.01.2014, p. 851-861.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Automated diagnosis of autism

T2 - In search of a mathematical marker

AU - Bhat, Shreya

AU - Acharya, U. Rajendra

AU - Adeli, Hojjat

AU - Bairy, G. Muralidhar

AU - Adeli, Amir

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Autism is a type of neurodevelopmental disorder affecting the memory, behavior, emotion, learning ability, and communication of an individual. An early detection of the abnormality, due to irregular processing in the brain, can be achieved using electroencephalograms (EEG). The variations in the EEG signals cannot be deciphered by mere visual inspection. Computer-aided diagnostic tools can be used to recognize the subtle and invisible information present in the irregular EEG pattern and diagnose autism. This paper presents a state-of-theart review of automated EEG-based diagnosis of autism. Various time domain, frequency domain, time-frequency domain, and nonlinear dynamics for the analysis of autistic EEG signals are described briefly. A focus of the review is the use of nonlinear dynamics and chaos theory to discover the mathematical biomarkers for the diagnosis of the autism analogous to biological markers. A combination of the time-frequency and nonlinear dynamic analysis is the most effective approach to characterize the nonstationary and chaotic physiological signals for the automated EEGbased diagnosis of autism spectrum disorder (ASD). The features extracted using these nonlinear methods can be used as mathematical markers to detect the early stage of autism and aid the clinicians in their diagnosis. This will expedite the administration of appropriate therapies to treat the disorder.

AB - Autism is a type of neurodevelopmental disorder affecting the memory, behavior, emotion, learning ability, and communication of an individual. An early detection of the abnormality, due to irregular processing in the brain, can be achieved using electroencephalograms (EEG). The variations in the EEG signals cannot be deciphered by mere visual inspection. Computer-aided diagnostic tools can be used to recognize the subtle and invisible information present in the irregular EEG pattern and diagnose autism. This paper presents a state-of-theart review of automated EEG-based diagnosis of autism. Various time domain, frequency domain, time-frequency domain, and nonlinear dynamics for the analysis of autistic EEG signals are described briefly. A focus of the review is the use of nonlinear dynamics and chaos theory to discover the mathematical biomarkers for the diagnosis of the autism analogous to biological markers. A combination of the time-frequency and nonlinear dynamic analysis is the most effective approach to characterize the nonstationary and chaotic physiological signals for the automated EEGbased diagnosis of autism spectrum disorder (ASD). The features extracted using these nonlinear methods can be used as mathematical markers to detect the early stage of autism and aid the clinicians in their diagnosis. This will expedite the administration of appropriate therapies to treat the disorder.

UR - http://www.scopus.com/inward/record.url?scp=84925286949&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84925286949&partnerID=8YFLogxK

U2 - 10.1515/revneuro-2014-0036

DO - 10.1515/revneuro-2014-0036

M3 - Article

VL - 25

SP - 851

EP - 861

JO - Reviews in the Neurosciences

JF - Reviews in the Neurosciences

SN - 0334-1763

IS - 6

ER -