CLASSIFICATION OF ADHD TYPES USING EEG SIGNAL DATA: A SUPERVISED MACHINE LEARNING APPROACH

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Samyuktha S1, Dr. M B Anandaraju2

Keywords

ADHD, KNN, PCA, EEG, Electrode, Conners

Abstract

One type of neuropsychiatric condition that affects children is attention deficit hyperactivity disorder. Children will benefit greatly from the early detection, identification, and treatment of this disease. The current study has offered a system for categorising ADHD as mixed or impulsive. For classification purposes, supervised machine learning's K-Nearest Neighbour classifier was employed. Data from the electrode and Conners were used as input. With Conners data, KNN is 73.68% accurate, while with electrode data, it is 36.84% accurate. The outcome has demonstrated the importance of Conners data in the categorization of ADHD.

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Feb 28, 2023

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Samyuktha S1, Dr. M B Anandaraju2 CLASSIFICATION OF ADHD TYPES USING EEG SIGNAL DATA: A SUPERVISED MACHINE LEARNING APPROACH . 2023 Feb. 13;94(1): 311-323. Available from: https://mattioli.1885journals.com/article/2023/311.html
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Vol. 94 No. 1 (2023)
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