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 me...
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my.iium.irep.979722022-05-20T09:16:12Z http://irep.iium.edu.my/97972/ Prediction of ADHD from a small dataset using an adaptive EEG theta/beta ratio and PCA feature extraction Sase, Takumi Othman, Marini QA75 Electronic computers. Computer science 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 Nature Switzerland 2022-05-30 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/97972/3/97972_Prediction%20of%20ADHD%20from%20a%20Small%20Dataset.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 2022), 30-31 May 2022, Online. 10.1007/978-3-031-00828-3 |
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QA75 Electronic computers. Computer science 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. |
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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 Nature Switzerland |
publishDate |
2022 |
url |
http://irep.iium.edu.my/97972/3/97972_Prediction%20of%20ADHD%20from%20a%20Small%20Dataset.pdf http://irep.iium.edu.my/97972/ |
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1734299973172854784 |
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13.18916 |