An Efficient Approach for Detection of Autism Spectrum Disorder Using Electroencephalography Signal

Sinha, Tulikapriya; Munot, Mousami V.; Sreemathy, R.

doi:10.1080/03772063.2019.1622462

Cited by 21 publications

(8 citation statements)

References 5 publications

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“…Sinha et al [75] obtained EEG signals from 20 healthy subjects and 10 autistic patients and pre-processed the acquired signals using the discrete wavelet transform and extracted nonlinear features thereafter. These features were fed to the K-NN classifier, yielding a classification accuracy of 92.8%.…”

Section: Resultsmentioning

confidence: 99%

A novel automated autism spectrum disorder detection system

Vicnesh

Arunkumar

et al. 2021

Complex Intell. Syst.

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Autism spectrum disorder (ASD) is a neurological and developmental disorder that begins early in childhood and lasts throughout a person’s life. Autism is influenced by both genetic and environmental factors. Lack of social interaction, communication problems, and a limited range of behaviors and interests are possible characteristics of autism in children, alongside other symptoms. Electroencephalograms provide useful information about changes in brain activity and hence are efficaciously used for diagnosis of neurological disease. Eighteen nonlinear features were extracted from EEG signals of 40 children with a diagnosis of autism spectrum disorder and 37 children with no diagnosis of neuro developmental disorder children. Feature selection was performed using Student’s t test, and Marginal Fisher Analysis was employed for data reduction. The features were ranked according to Student’s t test. The three most significant features were used to develop the autism index, while the ranked feature set was input to SVM polynomials 1, 2, and 3 for classification. The SVM polynomial 2 yielded the highest classification accuracy of 98.70% with 20 features. The developed classification system is likely to aid healthcare professionals as a diagnostic tool to detect autism. With more data, in our future work, we intend to employ deep learning models and to explore a cloud-based detection system for the detection of autism. Our study is novel, as we have analyzed all nonlinear features, and we are one of the first groups to have uniquely developed an autism (ASD) index using the extracted features.

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Section: Resultsmentioning

confidence: 99%

A novel automated autism spectrum disorder detection system

Vicnesh

Arunkumar

et al. 2021

Complex Intell. Syst.

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“…On the website of IPNA, BSMMU of Bangladesh mentions that depending on the autistic child's situation, an EEG test may be suggested. In India (Sinha et al, 2019), some research has been done on EEG signal-based ASD detection. So, in Bangladesh as well as in other…”

Section: Use Of Brain Signal-based Methods For Asd Detectionmentioning

confidence: 99%

“…Among South-Asian countries, in India, EEG signal-based autism detection is established mainly for research purposes (Arunkumar et al, 2020;Sinha et al, 2019). There are few and expensive services accessible in Pakistan for people with autism and their families (Imran and Azeem, 2014;Nadeem et al, 2019).…”

Section: Autism Detection In South Asian Countriesmentioning

confidence: 99%

Autism Detection from Brain Signals: South Asia Perspective

Peya

Rahman²,

Akhand

2023

J. of Eng. Sci.

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Autism Spectrum Disorder (ASD), simply autism, is a complex neurodevelopmental disorder that leads to serious social, communication, and behavioral challenges. The impact of ASD on the family, general well-being, society, and economy is becoming increasingly important due to its high prevalence and the extensive range of clinical treatments required for affected children. Early ASD detection is important because it could help improve access to intervention measures and may help improve developmental outcomes. Autism exposes a critical issue for developing countries, especially in South Asian countries. In the South Asia region, a few institutes are available in the capital or a few big metropolitan cities for diagnosing ASD. Generally, ASD is diagnosed through some conventional methods (e.g., screening). As a neurodevelopmental condition, ASD can be detected through brain signals. On account of easy operation and to maintain low-risk factors, only noninvasive neuroimaging methods, such as electroencephalography (EEG) are considered to measure a child's neural behaviors for classifying ASD. Machine learning methods are used with EEG signals for classifying ASD subjects in different studies than conventional methods. The main concern of this study is to provide some guidelines for machine learning-based automatic ASD detection through EEG signals which might be a prospectus for South Asian countries. Through the advanced system, people from all areas of the country will get proper ASD diagnosis and treatment facilities. Therefore, it is a timely approach of employing brain signals (especially EEG signals) and machine learning-based effective techniques for ASD detection in South Asian countries. Journal of Engineering Science 13(2), 2022, 69-81

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“…The ASD EEG‐extracted features have been studied in a number of approaches as inputs to certain machine learning algorithms with the intention to discriminate between ASD and other conditions (Ahuja & Darvinder, 2014): Discriminant analysis (Sinha et al, 2019), artificial neural network (Raja & Priya, 2017a), radial basis function (Buyukoflaz & Ozturk, 2018), neuro‐fuzzy (Ahuja & Darvinder, 2014), learning vector quantization (Hung et al, 2011), statistics based classifiers (Castelhano et al, 2018), nearest neighbour (Sinha et al, 2019), and support vector machine (Kaur et al, 2017). Table 1 illustrates the findings in a number of recent related articles.…”

Section: Introductionmentioning

confidence: 99%

Autism diagnosis via correlation between vectors of direct quadrature instantaneous frequency of EEG analytic normalized intrinsic mode functions

et al. 2021

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Autism spectrum disorder (ASD) is a neurological and developmental disorder that commences usually in the early years of age. It impacts the social interaction, communication and learning. It is believed that it is mainly caused by an abnormal connectivity between brain zones. The presented work applies an EEG‐based nonlinear method for the classification of ASD and neuro‐typical groups. The suggested procedure does not require pre‐assumptions and is completely data‐driven. Also, the main advantage is the accurate tracking of the pace of EEG activity without the effect of amplitude modulation on the spectral information. In addition, the tracking is conducted point‐by‐point to avoid shortcomings of inexact global features. First, for every (ASD or neuro‐typical) volunteer, the recorded EEG channels (64 channels) are decomposed by empirical mode decomposition (EMD) in order to get the underlying components (intrinsic mode functions—IMFs). Second, the direct quadrature (DQ) method is used to normalize the IMFs, and to dissociate between amplitude and frequency contents of the resulted components, as well as to help extract then the point‐by‐point spectral information from the analytic normalized IMFs by Hilbert transform. Third, the correlation coefficients between the instantaneous frequency vectors of the counterpart components will be computed over all channels (i.e., between components number ‘i’ of channels ‘x’ and ‘y’, 1 < i < number of components, 1 < x < 64, 1 < y < 64). Fourth, correlation coefficients array is constructed. Fifth, the dimension of the feature array is reduced without loss of significant information. Sixth, classification of reduced features is achieved via neural network. Finally, the statistical assessment of the classification outcome is conducted. The proposed method yields a test accuracy of 94.1%–100%.

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An Efficient Approach for Detection of Autism Spectrum Disorder Using Electroencephalography Signal

Cited by 21 publications

References 5 publications

A novel automated autism spectrum disorder detection system

A novel automated autism spectrum disorder detection system

Autism Detection from Brain Signals: South Asia Perspective

Autism diagnosis via correlation between vectors of direct quadrature instantaneous frequency of EEG analytic normalized intrinsic mode functions

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