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Prediction of ADHD from a small dataset using an adaptive EEG Theta/Beta Ratio and PCA feature extraction

EEG Theta/beta ratio (TBR) is conventionally used as a biomarker in childhood Attention-Deficit/Hyperactivity Disorder (ADHD) prediction and treatment. Due to the heterogeneity of ADHD symptoms, several studies have applied machine learning algorithms for enhancing the recognition of ADHD. These m...

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Main Authors: Sase, Takumi, Othman, Marini
Format: Conference or Workshop Item
Language:English
English
Published: Springer Science and Business Media Deutschland GmbH 2022
Subjects:
RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Online Access:http://irep.iium.edu.my/98541/6/98541_Prediction%20of%20ADHD%20from%20a%20small%20dataset.pdf
http://irep.iium.edu.my/98541/7/98541_Scopus.pdf
http://irep.iium.edu.my/98541/
https://link.springer.com/chapter/10.1007/978-3-031-00828-3_10
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spelling my.iium.irep.985412022-07-04T01:15:24Z http://irep.iium.edu.my/98541/ Prediction of ADHD from a small dataset using an adaptive EEG Theta/Beta Ratio and PCA feature extraction Sase, Takumi Othman, Marini RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry EEG Theta/beta ratio (TBR) is conventionally used as a biomarker in childhood Attention-Deficit/Hyperactivity Disorder (ADHD) prediction and treatment. Due to the heterogeneity of ADHD symptoms, several studies have applied machine learning algorithms for enhancing the recognition of ADHD. These methods, however, have limited performance in a small dataset. In this paper, we propose an adaptive EEG feature extraction approach using TBR and PCA. Repeated TBR-PCA feature extraction, SVM classification and statistical testing were applied on a small EEG sample with ADHD/typically developing (TD) labels. The steps were repeated with an update of the feature extraction technique until a high accuracy is achieved, allowing the small samples to be correctly identified (r = 0.833, one-sided, Bonferroni-corrected p < 0.0166). Within subjects EEG samples analyses performed better compared to between subject analyses, with accuracy getting worse with the increase of EEG segments. The contribution of this work is two-fold: the practical application allows for a reliable adoption of machine learning in non-invasive EEG screening of small ADHD dataset, while the theoretical contribution extends beyond the eyes closed resting state condition considered in this study and provides a methodological approach when working with limited samples Springer Science and Business Media Deutschland GmbH 2022-05 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/98541/6/98541_Prediction%20of%20ADHD%20from%20a%20small%20dataset.pdf application/pdf en http://irep.iium.edu.my/98541/7/98541_Scopus.pdf Sase, Takumi and Othman, Marini (2022) Prediction of ADHD from a small dataset using an adaptive EEG Theta/Beta Ratio and PCA feature extraction. In: Proceedings of the Fifth International Conference on Soft Computing and Data Mining (SCDM), 30-31 May 2022, UTHM, (Virtual). https://link.springer.com/chapter/10.1007/978-3-031-00828-3_10
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
spellingShingle RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Sase, Takumi
Othman, Marini
Prediction of ADHD from a small dataset using an adaptive EEG Theta/Beta Ratio and PCA feature extraction
description EEG Theta/beta ratio (TBR) is conventionally used as a biomarker in childhood Attention-Deficit/Hyperactivity Disorder (ADHD) prediction and treatment. Due to the heterogeneity of ADHD symptoms, several studies have applied machine learning algorithms for enhancing the recognition of ADHD. These methods, however, have limited performance in a small dataset. In this paper, we propose an adaptive EEG feature extraction approach using TBR and PCA. Repeated TBR-PCA feature extraction, SVM classification and statistical testing were applied on a small EEG sample with ADHD/typically developing (TD) labels. The steps were repeated with an update of the feature extraction technique until a high accuracy is achieved, allowing the small samples to be correctly identified (r = 0.833, one-sided, Bonferroni-corrected p < 0.0166). Within subjects EEG samples analyses performed better compared to between subject analyses, with accuracy getting worse with the increase of EEG segments. The contribution of this work is two-fold: the practical application allows for a reliable adoption of machine learning in non-invasive EEG screening of small ADHD dataset, while the theoretical contribution extends beyond the eyes closed resting state condition considered in this study and provides a methodological approach when working with limited samples
format Conference or Workshop Item
author Sase, Takumi
Othman, Marini
author_facet Sase, Takumi
Othman, Marini
author_sort Sase, Takumi
title Prediction of ADHD from a small dataset using an adaptive EEG Theta/Beta Ratio and PCA feature extraction
title_short Prediction of ADHD from a small dataset using an adaptive EEG Theta/Beta Ratio and PCA feature extraction
title_full Prediction of ADHD from a small dataset using an adaptive EEG Theta/Beta Ratio and PCA feature extraction
title_fullStr Prediction of ADHD from a small dataset using an adaptive EEG Theta/Beta Ratio and PCA feature extraction
title_full_unstemmed Prediction of ADHD from a small dataset using an adaptive EEG Theta/Beta Ratio and PCA feature extraction
title_sort prediction of adhd from a small dataset using an adaptive eeg theta/beta ratio and pca feature extraction
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2022
url http://irep.iium.edu.my/98541/6/98541_Prediction%20of%20ADHD%20from%20a%20small%20dataset.pdf
http://irep.iium.edu.my/98541/7/98541_Scopus.pdf
http://irep.iium.edu.my/98541/
https://link.springer.com/chapter/10.1007/978-3-031-00828-3_10
_version_ 1738510117834326016
score 13.211869

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