فيما يلي قائمة منسقة للدراسات المنشورة المتعلقة بالجوانب المختلفة لاضطراب فرط الحركة وتشتت الانتباه في المملكة العربية السعودية. إذا كنت تعرف أي دراسات أخرى عن اضطراب فرط الحركة وتشتت الانتباه في المملكة العربية السعودية وترغب بإدراجها ضمن هذه القائمة، نرجو التواصل معنا هنا.
يمكنك أيضًا العثور على مزيد من الدراسات من المملكة العربية السعودية في هذه القائمة الكاملة للمنشورات ذات الصلة بترتيب زمني ، بالإضافة إلى قائمة بالأطروحات ذات الصلة التي تم توفيرها عبر الإنترنت.
ملاحظة: ما لم يُذكر على وجه التحديد ، فإن الدراسات أدناه لم يتم إجراؤها أو دعمها من قِبل الجمعية السعودية لاضطراب فرط الحركة وتشتت الانتباه، وتم توفيرها هنا كمصدر للباحثين فقط.
دراسات شمولية
Diagnosis of attention deficit hyperactivity disorder: A deep learning approach
- Nizar Alsharif
- Mosleh Hmoud Al-Adhaileh
- Mohammed Al-Yaari
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, diagnosisCitation
[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"]نسبة الشيوع
Diagnosis of attention deficit hyperactivity disorder: A deep learning approach
- Nizar Alsharif
- Mosleh Hmoud Al-Adhaileh
- Mohammed Al-Yaari
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, diagnosisCitation
[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"]المعرفة والوعي
Diagnosis of attention deficit hyperactivity disorder: A deep learning approach
- Nizar Alsharif
- Mosleh Hmoud Al-Adhaileh
- Mohammed Al-Yaari
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, diagnosisCitation
[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"]التشخيص
Diagnosis of attention deficit hyperactivity disorder: A deep learning approach
- Nizar Alsharif
- Mosleh Hmoud Al-Adhaileh
- Mohammed Al-Yaari
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, diagnosisCitation
[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"]دراسات شمولية
Diagnosis of attention deficit hyperactivity disorder: A deep learning approach
- Nizar Alsharif
- Mosleh Hmoud Al-Adhaileh
- Mohammed Al-Yaari
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.