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TMP19: A Novel Ternary Motif Pattern-Based ADHD Detection Model Using EEG Signals

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental condition worldwide. In this research, we used an ADHD electroencephalography (EEG) dataset containing more than 4000 EEG signals. Moreover, these EEGs are noisy signals. A new hand-modeled EEG classification model has be...

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Autores principales: Barua, Prabal Datta, Dogan, Sengul, Baygin, Mehmet, Tuncer, Turker, Palmer, Elizabeth Emma, Ciaccio, Edward J., Acharya, U. Rajendra
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9600696/
https://www.ncbi.nlm.nih.gov/pubmed/36292233
http://dx.doi.org/10.3390/diagnostics12102544
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author Barua, Prabal Datta
Dogan, Sengul
Baygin, Mehmet
Tuncer, Turker
Palmer, Elizabeth Emma
Ciaccio, Edward J.
Acharya, U. Rajendra
author_facet Barua, Prabal Datta
Dogan, Sengul
Baygin, Mehmet
Tuncer, Turker
Palmer, Elizabeth Emma
Ciaccio, Edward J.
Acharya, U. Rajendra
author_sort Barua, Prabal Datta
collection PubMed
description Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental condition worldwide. In this research, we used an ADHD electroencephalography (EEG) dataset containing more than 4000 EEG signals. Moreover, these EEGs are noisy signals. A new hand-modeled EEG classification model has been proposed to separate healthy versus ADHD individuals using the EEG signals. In this model, a new ternary motif pattern (TMP) has been incorporated. We have mimicked deep learning networks to create this hand-modeled classification method. The Tunable Q Wavelet Transform (TQWT) has been utilized to generate wavelet subbands. We applied the proposed TMP and statistics to construct informative features from both raw EEG signals and wavelet bands by generating TQWT. Herein, features have been generated by 18 subbands and the original EEG signal. Thus, this model is named TMP19. The most informative features have been chosen by deploying neighborhood component analysis (NCA), and the selected features have been classified using the k-nearest neighbor (kNN) classifier. The used ADHD EEG dataset has 14 channels. Thus, these three phases—(i) feature extraction with TQWT, TMP, and statistics; (ii) feature selection by deploying NCA; and (iii) classification with kNN—have been applied to each channel. Iterative hard majority voting (IHMV) has been applied to obtain a higher and more general classification response. Our model attained 95.57% and 77.93% classification accuracies by deploying 10-fold and leave one subject out (LOSO) cross-validations, respectively.
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spelling pubmed-96006962022-10-27 TMP19: A Novel Ternary Motif Pattern-Based ADHD Detection Model Using EEG Signals Barua, Prabal Datta Dogan, Sengul Baygin, Mehmet Tuncer, Turker Palmer, Elizabeth Emma Ciaccio, Edward J. Acharya, U. Rajendra Diagnostics (Basel) Article Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental condition worldwide. In this research, we used an ADHD electroencephalography (EEG) dataset containing more than 4000 EEG signals. Moreover, these EEGs are noisy signals. A new hand-modeled EEG classification model has been proposed to separate healthy versus ADHD individuals using the EEG signals. In this model, a new ternary motif pattern (TMP) has been incorporated. We have mimicked deep learning networks to create this hand-modeled classification method. The Tunable Q Wavelet Transform (TQWT) has been utilized to generate wavelet subbands. We applied the proposed TMP and statistics to construct informative features from both raw EEG signals and wavelet bands by generating TQWT. Herein, features have been generated by 18 subbands and the original EEG signal. Thus, this model is named TMP19. The most informative features have been chosen by deploying neighborhood component analysis (NCA), and the selected features have been classified using the k-nearest neighbor (kNN) classifier. The used ADHD EEG dataset has 14 channels. Thus, these three phases—(i) feature extraction with TQWT, TMP, and statistics; (ii) feature selection by deploying NCA; and (iii) classification with kNN—have been applied to each channel. Iterative hard majority voting (IHMV) has been applied to obtain a higher and more general classification response. Our model attained 95.57% and 77.93% classification accuracies by deploying 10-fold and leave one subject out (LOSO) cross-validations, respectively. MDPI 2022-10-20 /pmc/articles/PMC9600696/ /pubmed/36292233 http://dx.doi.org/10.3390/diagnostics12102544 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Barua, Prabal Datta
Dogan, Sengul
Baygin, Mehmet
Tuncer, Turker
Palmer, Elizabeth Emma
Ciaccio, Edward J.
Acharya, U. Rajendra
TMP19: A Novel Ternary Motif Pattern-Based ADHD Detection Model Using EEG Signals
title TMP19: A Novel Ternary Motif Pattern-Based ADHD Detection Model Using EEG Signals
title_full TMP19: A Novel Ternary Motif Pattern-Based ADHD Detection Model Using EEG Signals
title_fullStr TMP19: A Novel Ternary Motif Pattern-Based ADHD Detection Model Using EEG Signals
title_full_unstemmed TMP19: A Novel Ternary Motif Pattern-Based ADHD Detection Model Using EEG Signals
title_short TMP19: A Novel Ternary Motif Pattern-Based ADHD Detection Model Using EEG Signals
title_sort tmp19: a novel ternary motif pattern-based adhd detection model using eeg signals
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9600696/
https://www.ncbi.nlm.nih.gov/pubmed/36292233
http://dx.doi.org/10.3390/diagnostics12102544
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