Detecting Brain Activity in ADHD Children and Healthy Controls Using Machine Learning Techniques

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aut.relation.conferenceACSW 2024: 2024 Australasian Computer Science Week
aut.relation.endpage74
aut.relation.startpage69
dc.contributor.authorNatarajan, Priyadarshini
dc.contributor.authorMadanian, Samaneh
dc.contributor.editorAbramson, D
dc.contributor.editorVassar, S
dc.contributor.editorJin, C
dc.date.accessioned2024-05-16T22:47:05Z
dc.date.available2024-05-16T22:47:05Z
dc.date.issued2024-05-13
dc.description.abstractThis study focuses on Attention Deficit Hyperactivity Disorder (ADHD), a neurodevelopmental disorder that affects both children and adults. Individuals with ADHD often struggle with difficulties related to attention, impulse control, and hyperactivity. To learn more about ADHD, researchers have employed a variety of neuroimaging modalities and analysis techniques over the years. To research brain activity in children with ADHD, this study examines the characteristics of Electroencephalogram (EEG) data using Machine Learning Techniques, which can be a trustworthy diagnostic tool for physicians. After analyzing the EEG data obtained, we can infer from this empirical investigation that the frontal regions of the brain are mostly active and model accuracy is 80% for ADHD classification.
dc.identifier.doi10.1145/3641142.3641156
dc.identifier.isbn9798400717307
dc.identifier.urihttp://hdl.handle.net/10292/17549
dc.publisherACM
dc.relation.urihttp://dx.doi.org/10.1145/3641142.3641156
dc.rightsCopyright © 2024 ACM Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted.
dc.rights.accessrightsOpenAccess
dc.titleDetecting Brain Activity in ADHD Children and Healthy Controls Using Machine Learning Techniques
dc.typeConference Contribution
pubs.elements-id552551
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