Detection of attention state in children with autism spectrum disorder based on neural network classification of electroencephalograms

Autism spectrum disorder (ASD) is a neurological condition characterized by impairments in social interaction. This diagnosis carries economic and social implications due to its high prevalence and associated morbidity. Data from electroencephalogram (EEG) sensors is numerical and serves as the inpu...

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Главные авторы:	Lyakhov, P. A., Ляхов, П. А., Lyakhova, U. A., Ляхова, У. А., Baboshina, V. A., Бабошина, В. А., Baryshev, V. V., Барышев, В. В., Nagornov, N. N., Нагорнов, Н. Н.
格式:	Статья
语言:	English
出版:	Saint Petersburg State University 2025
主题:	Autism spectrum disorder Processing from electroencephalogram Electroencephalogram Ensembling Multilayer linear perceptron Neural network
在线阅读:	https://dspace.ncfu.ru/handle/123456789/30648
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id	ir-123456789-30648
record_format	dspace
spelling	ir-123456789-306482025-07-02T08:02:31Z Detection of attention state in children with autism spectrum disorder based on neural network classification of electroencephalograms Выявление состояния внимания у детей с расстройствами аутистического спектра на основе нейросетевой классификации электроэнцефалограмм Lyakhov, P. A. Ляхов, П. А. Lyakhova, U. A. Ляхова, У. А. Baboshina, V. A. Бабошина, В. А. Baryshev, V. V. Барышев, В. В. Nagornov, N. N. Нагорнов, Н. Н. Autism spectrum disorder Processing from electroencephalogram Electroencephalogram Ensembling Multilayer linear perceptron Neural network Autism spectrum disorder (ASD) is a neurological condition characterized by impairments in social interaction. This diagnosis carries economic and social implications due to its high prevalence and associated morbidity. Data from electroencephalogram (EEG) sensors is numerical and serves as the input for machine learning-based predictions. The input data in this research includes features extracted from EEG signals, such as theta/beta ratio, theta/alpha ratio, and other relative power metrics, which are closely linked to cognitive control and attentional dynamics. These data are organized into two balanced classes: “Attention” and “No Attention,” comprising a total of 33 936 samples. This paper proposes 12 weighted and weighted-average ensemble models to enhance the accuracy of predicting attentional cues in individuals with ASD. For ensembling three multilayer perceptron architectures were developed and trained using various optimizers. The accuracy of the employed ensemble model of three multilayer perceptrons reached 95.90 %. The findings of this research can contribute to the advancement of novel diagnostic approaches and educational initiatives and serve as a foundation for future research utilizing machine learning techniques and the creation of innovative technologies for attention monitoring and training. 2025-07-02T08:00:40Z 2025-07-02T08:00:40Z 2025 Статья Lyakhov P.A., Lyakhova U.A., Baboshina V.A., Baryshev V.V., Nagornov N.N. Detection of attention state in children with autism spectrum disorder based on neural network classification of electroencephalograms // Vestnik Sankt-Peterburgskogo Universiteta, Prikladnaya Matematika, Informatika, Protsessy Upravleniya. - 2025. - 21 (1). - pp. 92 - 111. - DOI: 10.21638/spbu10.2025.107 https://dspace.ncfu.ru/handle/123456789/30648 en Vestnik Sankt-Peterburgskogo Universiteta, Prikladnaya Matematika, Informatika, Protsessy Upravleniya application/pdf application/pdf Saint Petersburg State University
institution	СКФУ
collection	Репозиторий
language	English
topic	Autism spectrum disorder Processing from electroencephalogram Electroencephalogram Ensembling Multilayer linear perceptron Neural network
spellingShingle	Autism spectrum disorder Processing from electroencephalogram Electroencephalogram Ensembling Multilayer linear perceptron Neural network Lyakhov, P. A. Ляхов, П. А. Lyakhova, U. A. Ляхова, У. А. Baboshina, V. A. Бабошина, В. А. Baryshev, V. V. Барышев, В. В. Nagornov, N. N. Нагорнов, Н. Н. Detection of attention state in children with autism spectrum disorder based on neural network classification of electroencephalograms
description	Autism spectrum disorder (ASD) is a neurological condition characterized by impairments in social interaction. This diagnosis carries economic and social implications due to its high prevalence and associated morbidity. Data from electroencephalogram (EEG) sensors is numerical and serves as the input for machine learning-based predictions. The input data in this research includes features extracted from EEG signals, such as theta/beta ratio, theta/alpha ratio, and other relative power metrics, which are closely linked to cognitive control and attentional dynamics. These data are organized into two balanced classes: “Attention” and “No Attention,” comprising a total of 33 936 samples. This paper proposes 12 weighted and weighted-average ensemble models to enhance the accuracy of predicting attentional cues in individuals with ASD. For ensembling three multilayer perceptron architectures were developed and trained using various optimizers. The accuracy of the employed ensemble model of three multilayer perceptrons reached 95.90 %. The findings of this research can contribute to the advancement of novel diagnostic approaches and educational initiatives and serve as a foundation for future research utilizing machine learning techniques and the creation of innovative technologies for attention monitoring and training.
format	Статья
author	Lyakhov, P. A. Ляхов, П. А. Lyakhova, U. A. Ляхова, У. А. Baboshina, V. A. Бабошина, В. А. Baryshev, V. V. Барышев, В. В. Nagornov, N. N. Нагорнов, Н. Н.
author_facet	Lyakhov, P. A. Ляхов, П. А. Lyakhova, U. A. Ляхова, У. А. Baboshina, V. A. Бабошина, В. А. Baryshev, V. V. Барышев, В. В. Nagornov, N. N. Нагорнов, Н. Н.
author_sort	Lyakhov, P. A.
title	Detection of attention state in children with autism spectrum disorder based on neural network classification of electroencephalograms
title_short	Detection of attention state in children with autism spectrum disorder based on neural network classification of electroencephalograms
title_full	Detection of attention state in children with autism spectrum disorder based on neural network classification of electroencephalograms
title_fullStr	Detection of attention state in children with autism spectrum disorder based on neural network classification of electroencephalograms
title_full_unstemmed	Detection of attention state in children with autism spectrum disorder based on neural network classification of electroencephalograms
title_sort	detection of attention state in children with autism spectrum disorder based on neural network classification of electroencephalograms
publisher	Saint Petersburg State University
publishDate	2025
url	https://dspace.ncfu.ru/handle/123456789/30648
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Detection of attention state in children with autism spectrum disorder based on neural network classification of electroencephalograms

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