Enhancing autism spectrum disorder classification in children through the integration of traditional statistics and classical machine learning techniques in EEG analysis
Abstract Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder hallmarked by challenges in social communication, limited interests, and repetitive, stereotyped movements and behaviors. Numerous research efforts have indicated that individuals with ASD exhibit distinct brain connectivity pa...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-49048-7 |
| _version_ | 1797398005142257664 |
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| author | Jacek Rogala Jarosław Żygierewicz Urszula Malinowska Hanna Cygan Elżbieta Stawicka Adam Kobus Bart Vanrumste |
| author_facet | Jacek Rogala Jarosław Żygierewicz Urszula Malinowska Hanna Cygan Elżbieta Stawicka Adam Kobus Bart Vanrumste |
| author_sort | Jacek Rogala |
| collection | DOAJ |
| description | Abstract Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder hallmarked by challenges in social communication, limited interests, and repetitive, stereotyped movements and behaviors. Numerous research efforts have indicated that individuals with ASD exhibit distinct brain connectivity patterns compared to control groups. However, these investigations, often constrained by small sample sizes, have led to inconsistent results, suggesting both heightened and diminished long-range connectivity within ASD populations. To bolster our analysis and enhance their reliability, we conducted a retrospective study using two different connectivity metrics and employed both traditional statistical methods and machine learning techniques. The concurrent use of statistical analysis and classical machine learning techniques advanced our understanding of model predictions derived from the spectral or connectivity attributes of a subject's EEG signal, while also verifying these predictions. Significantly, the utilization of machine learning methodologies empowered us to identify a unique subgroup of correctly classified children with ASD, defined by the analyzed EEG features. This improved approach is expected to contribute significantly to the existing body of knowledge on ASD and potentially guide personalized treatment strategies. |
| first_indexed | 2024-03-09T01:19:25Z |
| format | Article |
| id | doaj.art-46ea00c5ffc341e6b5ed29cdc139e608 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T01:19:25Z |
| publishDate | 2023-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-46ea00c5ffc341e6b5ed29cdc139e6082023-12-10T12:16:11ZengNature PortfolioScientific Reports2045-23222023-12-0113111210.1038/s41598-023-49048-7Enhancing autism spectrum disorder classification in children through the integration of traditional statistics and classical machine learning techniques in EEG analysisJacek Rogala0Jarosław Żygierewicz1Urszula Malinowska2Hanna Cygan3Elżbieta Stawicka4Adam Kobus5Bart Vanrumste6Faculty of Physics, University of WarsawFaculty of Physics, University of WarsawFaculty of Physics, University of WarsawInstitute of Physiology and Pathology of Hearing, Bioimaging Research CenterClinic of Paediatric Neurology, Institute of Mother and ChildInstitute of Computer Science, Marie Curie-Skłodowska UniversityDepartment of Electrical Engineering (ESAT), eMedia Research Lab/STADIUS, KU LeuvenAbstract Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder hallmarked by challenges in social communication, limited interests, and repetitive, stereotyped movements and behaviors. Numerous research efforts have indicated that individuals with ASD exhibit distinct brain connectivity patterns compared to control groups. However, these investigations, often constrained by small sample sizes, have led to inconsistent results, suggesting both heightened and diminished long-range connectivity within ASD populations. To bolster our analysis and enhance their reliability, we conducted a retrospective study using two different connectivity metrics and employed both traditional statistical methods and machine learning techniques. The concurrent use of statistical analysis and classical machine learning techniques advanced our understanding of model predictions derived from the spectral or connectivity attributes of a subject's EEG signal, while also verifying these predictions. Significantly, the utilization of machine learning methodologies empowered us to identify a unique subgroup of correctly classified children with ASD, defined by the analyzed EEG features. This improved approach is expected to contribute significantly to the existing body of knowledge on ASD and potentially guide personalized treatment strategies.https://doi.org/10.1038/s41598-023-49048-7 |
| spellingShingle | Jacek Rogala Jarosław Żygierewicz Urszula Malinowska Hanna Cygan Elżbieta Stawicka Adam Kobus Bart Vanrumste Enhancing autism spectrum disorder classification in children through the integration of traditional statistics and classical machine learning techniques in EEG analysis Scientific Reports |
| title | Enhancing autism spectrum disorder classification in children through the integration of traditional statistics and classical machine learning techniques in EEG analysis |
| title_full | Enhancing autism spectrum disorder classification in children through the integration of traditional statistics and classical machine learning techniques in EEG analysis |
| title_fullStr | Enhancing autism spectrum disorder classification in children through the integration of traditional statistics and classical machine learning techniques in EEG analysis |
| title_full_unstemmed | Enhancing autism spectrum disorder classification in children through the integration of traditional statistics and classical machine learning techniques in EEG analysis |
| title_short | Enhancing autism spectrum disorder classification in children through the integration of traditional statistics and classical machine learning techniques in EEG analysis |
| title_sort | enhancing autism spectrum disorder classification in children through the integration of traditional statistics and classical machine learning techniques in eeg analysis |
| url | https://doi.org/10.1038/s41598-023-49048-7 |
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