EEG-Based ADHD Classification Using Autoencoder Feature Extraction and ResNet with Double Augmented Attention Mechanism
Background: Attention-Deficit/Hyperactivity Disorder (ADHD) represents a widely prevalent and heterogeneous neurodevelopmental condition in pediatric populations, often exhibiting a substantial propensity to persist into adulthood. ADHD is a multifaceted disorder that resists straightforward diagnos...
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2025-01-01
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| author | Jayoti Bansal Gaurav Gangwar Mohammad Aljaidi Ali Alkoradees Gagandeep Singh |
| author_facet | Jayoti Bansal Gaurav Gangwar Mohammad Aljaidi Ali Alkoradees Gagandeep Singh |
| author_sort | Jayoti Bansal |
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| description | Background: Attention-Deficit/Hyperactivity Disorder (ADHD) represents a widely prevalent and heterogeneous neurodevelopmental condition in pediatric populations, often exhibiting a substantial propensity to persist into adulthood. ADHD is a multifaceted disorder that resists straightforward diagnostic tests. Clinicians must invest substantial time and effort to secure an accurate diagnosis and implement effective treatment. ADHD diagnosis is primarily based on psychiatric tests, as there is currently no clinically utilized objective diagnostic tool. Nonetheless, several studies in have documented endeavors to create objective instruments designed to assist in the diagnostic process of ADHD, aiming to enhance diagnostic accuracy and reduce subjectivity. Method: This research endeavor sought to establish an objective diagnostic modality for ADHD through the utilization of electroencephalography (EEG) signal analysis. With the use of innovative deep learning techniques, this research seeks to improve the diagnosis of ADHD using EEG data. To capture complex patterns in EEG data, this study proposes a double-augmented attention mechanism ResNet-based model. Using an autoencoder for feature extraction, the Reptile Search Algorithm for feature selection, and a modified ResNet architecture for model training comprise the technique. Results: AUC, F1-score, accuracy, precision, recall, and other standard classifiers like Random Forest and AdaBoost were utilized to compare the model’s performance. By a wide margin, the proposed ResNet model outperforms the traditional models with a 99.42% accuracy, 99.03% precision, 99.82% recall, and 99.42% F1-score. Conclusions: ROC AUC score of 0.99 for the model underscores its remarkable capability to differentiate between children with and without ADHD, thereby minimizing misclassification errors and improving diagnostic precision. |
| format | Article |
| id | doaj-art-07b61f881585486db34ee52a83fa139d |
| institution | Kabale University |
| issn | 2076-3425 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Brain Sciences |
| spelling | doaj-art-07b61f881585486db34ee52a83fa139d2025-01-24T13:25:57ZengMDPI AGBrain Sciences2076-34252025-01-011519510.3390/brainsci15010095EEG-Based ADHD Classification Using Autoencoder Feature Extraction and ResNet with Double Augmented Attention MechanismJayoti Bansal0Gaurav Gangwar1Mohammad Aljaidi2Ali Alkoradees3Gagandeep Singh4Department of Computer Science Engineering, Baba Farid College of Engineering & Technology, Bathinda 151001, Punjab, IndiaDepartment of Computer Science Engineering, Baba Farid College of Engineering & Technology, Bathinda 151001, Punjab, IndiaDepartment of Computer Science, Zarqa University, Zarqa 13110, JordanUnit of Scientific Research, Applied College, Qassim University, Buraydah 52571, Saudi ArabiaDepartment of Mechanical Engineering, Baba Farid College of Engineering & Technology, Bathinda 151001, Punjab, IndiaBackground: Attention-Deficit/Hyperactivity Disorder (ADHD) represents a widely prevalent and heterogeneous neurodevelopmental condition in pediatric populations, often exhibiting a substantial propensity to persist into adulthood. ADHD is a multifaceted disorder that resists straightforward diagnostic tests. Clinicians must invest substantial time and effort to secure an accurate diagnosis and implement effective treatment. ADHD diagnosis is primarily based on psychiatric tests, as there is currently no clinically utilized objective diagnostic tool. Nonetheless, several studies in have documented endeavors to create objective instruments designed to assist in the diagnostic process of ADHD, aiming to enhance diagnostic accuracy and reduce subjectivity. Method: This research endeavor sought to establish an objective diagnostic modality for ADHD through the utilization of electroencephalography (EEG) signal analysis. With the use of innovative deep learning techniques, this research seeks to improve the diagnosis of ADHD using EEG data. To capture complex patterns in EEG data, this study proposes a double-augmented attention mechanism ResNet-based model. Using an autoencoder for feature extraction, the Reptile Search Algorithm for feature selection, and a modified ResNet architecture for model training comprise the technique. Results: AUC, F1-score, accuracy, precision, recall, and other standard classifiers like Random Forest and AdaBoost were utilized to compare the model’s performance. By a wide margin, the proposed ResNet model outperforms the traditional models with a 99.42% accuracy, 99.03% precision, 99.82% recall, and 99.42% F1-score. Conclusions: ROC AUC score of 0.99 for the model underscores its remarkable capability to differentiate between children with and without ADHD, thereby minimizing misclassification errors and improving diagnostic precision.https://www.mdpi.com/2076-3425/15/1/95ADHDResNetdouble augmented attention mechanismEEGauto encoderreptile search algorithm |
| spellingShingle | Jayoti Bansal Gaurav Gangwar Mohammad Aljaidi Ali Alkoradees Gagandeep Singh EEG-Based ADHD Classification Using Autoencoder Feature Extraction and ResNet with Double Augmented Attention Mechanism Brain Sciences ADHD ResNet double augmented attention mechanism EEG auto encoder reptile search algorithm |
| title | EEG-Based ADHD Classification Using Autoencoder Feature Extraction and ResNet with Double Augmented Attention Mechanism |
| title_full | EEG-Based ADHD Classification Using Autoencoder Feature Extraction and ResNet with Double Augmented Attention Mechanism |
| title_fullStr | EEG-Based ADHD Classification Using Autoencoder Feature Extraction and ResNet with Double Augmented Attention Mechanism |
| title_full_unstemmed | EEG-Based ADHD Classification Using Autoencoder Feature Extraction and ResNet with Double Augmented Attention Mechanism |
| title_short | EEG-Based ADHD Classification Using Autoencoder Feature Extraction and ResNet with Double Augmented Attention Mechanism |
| title_sort | eeg based adhd classification using autoencoder feature extraction and resnet with double augmented attention mechanism |
| topic | ADHD ResNet double augmented attention mechanism EEG auto encoder reptile search algorithm |
| url | https://www.mdpi.com/2076-3425/15/1/95 |
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