Automated Delimitation and Classification of Autistic Disorder Using EEG Signal

Subudhi, Asit; Mohanty, Monalisa; Sahoo, Subhadra; Mohanty, Sandeep; Mohanty, Bibhuprasad

doi:10.1080/03772063.2020.1844076

Cited by 13 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cheong et al [73] and Sinha et al [53] had also extracted nonlinear features similar to our study, but obtained lower accuracies than our study. Although Subudhi et al [78] and Tawhid et al [79] had obtained higher accuracies than our study, the authors had used lesser data as compared to our study. Pham et al [72] did a similar classification to that of our study and achieved the same accuracy as ours (98.7%).…”

Section: Resultscontrasting

confidence: 66%

See 1 more Smart Citation

A novel automated autism spectrum disorder detection system

Vicnesh

Arunkumar

et al. 2021

Complex Intell. Syst.

View full text Add to dashboard Cite

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.

show abstract

Section: Resultscontrasting

confidence: 66%

“…Subudhi et al [78] acquired EEG signals from 41 autistic patients and 32 healthy subjects and extracted nonlinear features from the signals. The significant features were classified thereafter using the support vector machine classifier, yielding an accuracy of 90.41%.…”

Section: Resultsmentioning

confidence: 99%

A novel automated autism spectrum disorder detection system

Vicnesh

Arunkumar

et al. 2021

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…ASD reflects a decrease in brain function, this is because autistic children have a stronger spectrum at higher frequencies than normal children. The difference between the two groups was 2 points on the crown and 2 points on the occipital region [3]. Autistic people have communication and behavior problems, such as eye contact and lack of facial expressions.…”

Section: Introductionmentioning

confidence: 97%

Design and Implementation of Mobile Application for CNN-Based EEG Identification of Autism Spectrum Disorder

Melinda,

Arnia,

Yafi

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

View full text Add to dashboard Cite

Autism spectrum disorder (ASD) is a disorder of the nervous system from birth and during infancy. This disorder affects children's development, making it difficult for nerve function to develop, and causes the child concerned to have difficulty in fostering social relationships. Early detection of children with ASD is needed so that treatment is fast and on target. Currently, facilities and research on early diagnosis of ASD patients through EEG signals are still very few, requiring much cost and more effort to analyze EEG signals in examinations related to ASD detection cases. This study proposes a mobile phone application that can distinguish people living with ASD and normal data signals based on asynchronous EEG brain signals. This research also produces a preprocessing algorithm and BCI2000 EEG data signal so that it can be automated using Python. This research also produces an output model, namely the Deep Learning Convolutional Neural Network, which is deployed using Python-Flask so that the diagnosis of EEG signals with ASD and normal patients can be used on various platforms through restAPI. This research is also expected to help the community and support the diagnosis of ASD sufferers so that they can be handled appropriately. Data for ASD sufferers and normal data were correctly classified into the appropriate class. Handling this disease requires close and integrated cooperation, so this ASD classification will be very helpful for patients and can make a diagnosis in a faster time, enabling patients to receive targeted treatment and therapy.

show abstract

“…[9] introduced a quantitative method based on EEG data for the automated detection of ASD in TD children, utilizing the area under a second-order difference plot as a distinctive feature. [10] assessed ASD by capturing nonlinear attributes from EEG signals. The dataset comprises a total of 73 EEG signals obtained from various patients, with 41 displaying ASD and 32 exhibiting typical neural activity.…”

Section: Introductionmentioning

confidence: 99%

Novel Framework for Autism Spectrum Disorder Identification and Tailored Education With Effective Data Mining and Ensemble Learning Techniques

Hajjej,

Ayouni,

Alohali

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Autism Spectrum Disorder (ASD) is a neurological condition that significantly affects cognitive abilities, language comprehension, object recognition, interpersonal skills, and communication capabilities. Its primary origin is genetic, and early detection and intervention can mitigate the need for costly medical procedures and lengthy examinations for individuals affected by ASD. An effective teaching approach may be challenging to determine for a child with autism. Autism Spectrum Disorder is highly diverse, with each affected child being unique. It is often stated that no two autistic children are alike, meaning that what benefits one child may not be suitable for another. Two ASD screening datasets of toddlers are merged in this study. The SMOTE method to balance the dataset, followed by feature selection methods. The research introduces a two-phase system: the first phase employs various machine learning models, including an ensemble of random forest and XGBoost classifiers, for accurate ASD identification. In the second phase, the study focuses on identifying appropriate teaching methods for children with ASD by evaluating their physical, verbal, and behavioural performance. This research aims to provide personalized educational approaches for individuals with ASD, harnessing machine learning to enhance precision in addressing their unique needs.

show abstract

Automated Delimitation and Classification of Autistic Disorder Using EEG Signal

Cited by 13 publications

References 19 publications

A novel automated autism spectrum disorder detection system

A novel automated autism spectrum disorder detection system

Design and Implementation of Mobile Application for CNN-Based EEG Identification of Autism Spectrum Disorder

Novel Framework for Autism Spectrum Disorder Identification and Tailored Education With Effective Data Mining and Ensemble Learning Techniques

Contact Info

Product

Resources

About