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Saudi ADHD Research

Below is a curated list of published studies related to different aspects of Attention Deficit / Hyperactivity Disorder (ADHD) in Saudi Arabia, including prevalence, awareness, diagnosis, language and communication, and review articles.

Additional studies from Saudi Arabia, including topics not featured on this page, are available in the research library. We also maintain a list of ADHD-related theses that have been made available online. If you would like your research featured here, or know of any publications in or about ADHD in KSA that you believe should be included here, please get in touch by email at research @ adhd.org.sa.

Note: Unless specifically stated, these studies were neither conducted by nor supported by the Saudi ADHD Society, and are provided as a resource for researchers only.

Review Articles

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  4. 10.3934/math.2024517

Diagnosis of attention deficit hyperactivity disorder: A deep learning approach

Diagnosis of attention deficit hyperactivity disorder: A deep learning approach
Open Access | CC BY 4.0 | |
Authors:

AIMS Mathematics

10.3934/math.2024517

Abstract

In recent years, there has been significant interest in the analysis and classification of brain dis-orders using electroencephalography (EEG). We presented machine learning and deep learning (DL) frameworks that integrate an EEG-based brain network with various DL models to diagnose attention deficit hyperactivity disorder (ADHD). By incorporating an objective biomarker into the diagnostic process, the accuracy and effectiveness of diagnosis could be enhanced. We used public EEG datasets from 61 ADHD youngsters and 60 normally developing children. The raw EEG data underwent preprocessing, including the application of filters in clinically relevant frequency bands and notch filters. From the preprocessed EEG segments, statistical features (e.g., standard deviation, kurtosis) and spectral features (e.g., entropy) were extracted. Principal component analysis (PCA) and chi-square with PCA were used as feature selection methods to obtain the most useful features and keep them. The machine learning models achieved the highest accuracy result of 94.86% by utilizing support vector machines (SVM) with PCA features. Furthermore, integrating models combining a convolutional neural network (CNN) with bidirectional long short-term memory (BiLSTM) networks, and gated recurrent unit-Transformer (GRU-Transformer block) with Chi-square and PCA features achieved accuracies of 94.50% and 95.59%, respectively. The suggested framework demonstrated a wide range of applicability in addressing the identification of ADHD. To evaluate the performance of the proposed models, comparisons were made with existing models, and the proposed system exhibited superior performance. We enhanced EEG-based analysis and categorization of ADHD by demonstrating the capabilities of advanced artificial intelligence models in enhancing diagnostic accuracy and efficacy.

Keywords

deep learning, machine learning, attention deficit hyperactivity disorder, electroencephalogram, diagnosis

Citation

[research_citation style="APA" pubtype="journal" authors="Nizar Alsharif,Mosleh Hmoud Al-Adhaileh,Mohammed Al-Yaari," year="2024" title="Diagnosis of attention deficit hyperactivity disorder: A deep learning approach" volume="9" issue="5" journal="AIMS Mathematics" shortjournal="MATH" startpage="10580" endpage="10608" articlenum="" doi="10.3934/math.2024517"]

Prevalence

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  4. 10.3934/math.2024517

Diagnosis of attention deficit hyperactivity disorder: A deep learning approach

Diagnosis of attention deficit hyperactivity disorder: A deep learning approach
Open Access | CC BY 4.0 | |
Authors:

AIMS Mathematics

10.3934/math.2024517

Abstract

In recent years, there has been significant interest in the analysis and classification of brain dis-orders using electroencephalography (EEG). We presented machine learning and deep learning (DL) frameworks that integrate an EEG-based brain network with various DL models to diagnose attention deficit hyperactivity disorder (ADHD). By incorporating an objective biomarker into the diagnostic process, the accuracy and effectiveness of diagnosis could be enhanced. We used public EEG datasets from 61 ADHD youngsters and 60 normally developing children. The raw EEG data underwent preprocessing, including the application of filters in clinically relevant frequency bands and notch filters. From the preprocessed EEG segments, statistical features (e.g., standard deviation, kurtosis) and spectral features (e.g., entropy) were extracted. Principal component analysis (PCA) and chi-square with PCA were used as feature selection methods to obtain the most useful features and keep them. The machine learning models achieved the highest accuracy result of 94.86% by utilizing support vector machines (SVM) with PCA features. Furthermore, integrating models combining a convolutional neural network (CNN) with bidirectional long short-term memory (BiLSTM) networks, and gated recurrent unit-Transformer (GRU-Transformer block) with Chi-square and PCA features achieved accuracies of 94.50% and 95.59%, respectively. The suggested framework demonstrated a wide range of applicability in addressing the identification of ADHD. To evaluate the performance of the proposed models, comparisons were made with existing models, and the proposed system exhibited superior performance. We enhanced EEG-based analysis and categorization of ADHD by demonstrating the capabilities of advanced artificial intelligence models in enhancing diagnostic accuracy and efficacy.

Keywords

deep learning, machine learning, attention deficit hyperactivity disorder, electroencephalogram, diagnosis

Citation

[research_citation style="APA" pubtype="journal" authors="Nizar Alsharif,Mosleh Hmoud Al-Adhaileh,Mohammed Al-Yaari," year="2024" title="Diagnosis of attention deficit hyperactivity disorder: A deep learning approach" volume="9" issue="5" journal="AIMS Mathematics" shortjournal="MATH" startpage="10580" endpage="10608" articlenum="" doi="10.3934/math.2024517"]

Awareness and Attitudes

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  4. 10.3934/math.2024517

Diagnosis of attention deficit hyperactivity disorder: A deep learning approach

Diagnosis of attention deficit hyperactivity disorder: A deep learning approach
Open Access | CC BY 4.0 | |
Authors:

AIMS Mathematics

10.3934/math.2024517

Abstract

In recent years, there has been significant interest in the analysis and classification of brain dis-orders using electroencephalography (EEG). We presented machine learning and deep learning (DL) frameworks that integrate an EEG-based brain network with various DL models to diagnose attention deficit hyperactivity disorder (ADHD). By incorporating an objective biomarker into the diagnostic process, the accuracy and effectiveness of diagnosis could be enhanced. We used public EEG datasets from 61 ADHD youngsters and 60 normally developing children. The raw EEG data underwent preprocessing, including the application of filters in clinically relevant frequency bands and notch filters. From the preprocessed EEG segments, statistical features (e.g., standard deviation, kurtosis) and spectral features (e.g., entropy) were extracted. Principal component analysis (PCA) and chi-square with PCA were used as feature selection methods to obtain the most useful features and keep them. The machine learning models achieved the highest accuracy result of 94.86% by utilizing support vector machines (SVM) with PCA features. Furthermore, integrating models combining a convolutional neural network (CNN) with bidirectional long short-term memory (BiLSTM) networks, and gated recurrent unit-Transformer (GRU-Transformer block) with Chi-square and PCA features achieved accuracies of 94.50% and 95.59%, respectively. The suggested framework demonstrated a wide range of applicability in addressing the identification of ADHD. To evaluate the performance of the proposed models, comparisons were made with existing models, and the proposed system exhibited superior performance. We enhanced EEG-based analysis and categorization of ADHD by demonstrating the capabilities of advanced artificial intelligence models in enhancing diagnostic accuracy and efficacy.

Keywords

deep learning, machine learning, attention deficit hyperactivity disorder, electroencephalogram, diagnosis

Citation

[research_citation style="APA" pubtype="journal" authors="Nizar Alsharif,Mosleh Hmoud Al-Adhaileh,Mohammed Al-Yaari," year="2024" title="Diagnosis of attention deficit hyperactivity disorder: A deep learning approach" volume="9" issue="5" journal="AIMS Mathematics" shortjournal="MATH" startpage="10580" endpage="10608" articlenum="" doi="10.3934/math.2024517"]

Diagnosis

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  4. 10.3934/math.2024517

Diagnosis of attention deficit hyperactivity disorder: A deep learning approach

Diagnosis of attention deficit hyperactivity disorder: A deep learning approach
Open Access | CC BY 4.0 | |
Authors:

AIMS Mathematics

10.3934/math.2024517

Abstract

In recent years, there has been significant interest in the analysis and classification of brain dis-orders using electroencephalography (EEG). We presented machine learning and deep learning (DL) frameworks that integrate an EEG-based brain network with various DL models to diagnose attention deficit hyperactivity disorder (ADHD). By incorporating an objective biomarker into the diagnostic process, the accuracy and effectiveness of diagnosis could be enhanced. We used public EEG datasets from 61 ADHD youngsters and 60 normally developing children. The raw EEG data underwent preprocessing, including the application of filters in clinically relevant frequency bands and notch filters. From the preprocessed EEG segments, statistical features (e.g., standard deviation, kurtosis) and spectral features (e.g., entropy) were extracted. Principal component analysis (PCA) and chi-square with PCA were used as feature selection methods to obtain the most useful features and keep them. The machine learning models achieved the highest accuracy result of 94.86% by utilizing support vector machines (SVM) with PCA features. Furthermore, integrating models combining a convolutional neural network (CNN) with bidirectional long short-term memory (BiLSTM) networks, and gated recurrent unit-Transformer (GRU-Transformer block) with Chi-square and PCA features achieved accuracies of 94.50% and 95.59%, respectively. The suggested framework demonstrated a wide range of applicability in addressing the identification of ADHD. To evaluate the performance of the proposed models, comparisons were made with existing models, and the proposed system exhibited superior performance. We enhanced EEG-based analysis and categorization of ADHD by demonstrating the capabilities of advanced artificial intelligence models in enhancing diagnostic accuracy and efficacy.

Keywords

deep learning, machine learning, attention deficit hyperactivity disorder, electroencephalogram, diagnosis

Citation

[research_citation style="APA" pubtype="journal" authors="Nizar Alsharif,Mosleh Hmoud Al-Adhaileh,Mohammed Al-Yaari," year="2024" title="Diagnosis of attention deficit hyperactivity disorder: A deep learning approach" volume="9" issue="5" journal="AIMS Mathematics" shortjournal="MATH" startpage="10580" endpage="10608" articlenum="" doi="10.3934/math.2024517"]

Language & Communication

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  4. 10.3934/math.2024517

Diagnosis of attention deficit hyperactivity disorder: A deep learning approach

Diagnosis of attention deficit hyperactivity disorder: A deep learning approach
Open Access | CC BY 4.0 | |
Authors:

AIMS Mathematics

10.3934/math.2024517

Abstract

In recent years, there has been significant interest in the analysis and classification of brain dis-orders using electroencephalography (EEG). We presented machine learning and deep learning (DL) frameworks that integrate an EEG-based brain network with various DL models to diagnose attention deficit hyperactivity disorder (ADHD). By incorporating an objective biomarker into the diagnostic process, the accuracy and effectiveness of diagnosis could be enhanced. We used public EEG datasets from 61 ADHD youngsters and 60 normally developing children. The raw EEG data underwent preprocessing, including the application of filters in clinically relevant frequency bands and notch filters. From the preprocessed EEG segments, statistical features (e.g., standard deviation, kurtosis) and spectral features (e.g., entropy) were extracted. Principal component analysis (PCA) and chi-square with PCA were used as feature selection methods to obtain the most useful features and keep them. The machine learning models achieved the highest accuracy result of 94.86% by utilizing support vector machines (SVM) with PCA features. Furthermore, integrating models combining a convolutional neural network (CNN) with bidirectional long short-term memory (BiLSTM) networks, and gated recurrent unit-Transformer (GRU-Transformer block) with Chi-square and PCA features achieved accuracies of 94.50% and 95.59%, respectively. The suggested framework demonstrated a wide range of applicability in addressing the identification of ADHD. To evaluate the performance of the proposed models, comparisons were made with existing models, and the proposed system exhibited superior performance. We enhanced EEG-based analysis and categorization of ADHD by demonstrating the capabilities of advanced artificial intelligence models in enhancing diagnostic accuracy and efficacy.

Keywords

deep learning, machine learning, attention deficit hyperactivity disorder, electroencephalogram, diagnosis

Citation

[research_citation style="APA" pubtype="journal" authors="Nizar Alsharif,Mosleh Hmoud Al-Adhaileh,Mohammed Al-Yaari," year="2024" title="Diagnosis of attention deficit hyperactivity disorder: A deep learning approach" volume="9" issue="5" journal="AIMS Mathematics" shortjournal="MATH" startpage="10580" endpage="10608" articlenum="" doi="10.3934/math.2024517"]