A Machine Learning Framework for Accurate Diagnosis of ADHD from Human EEG Signals

Attention Deficit Hyperactivity Disorder (ADHD) presents significant neurodevelopmental challenges, particularly affecting children. This paper introduces an advanced machine learning framework for precise ADHD diagnosis using human EEG signals, emphasizing early intervention. Our novel approach advances beyond traditional diagnostic methods by implementing a proprietary machine learning model that significantly improves the accuracy of ADHD detection. This innovation is achieved through the strategic extraction of EEG features, commonly utilized in the detection of other neurological disorders, enhancing the discriminatory power of our model. Our methodology integrates a meticulous examination of recent breakthroughs in EEG signal processing and feature extraction, enabling the identification of unique neurophysiological signatures associated with ADHD. We explore the integration of EEG spectral power with multimodal serotonergic data, employing these insights to enhance machine learning-based classification. While evaluating various neural network models, we focus on the superior performance of Support Vector Machines (SVM) and k-Nearest Neighbors (KNN), which achieved exceptional classification accuracies of ۹۹.۵۱% and ۹۹.۰۲%, respectively. The paper presents a comprehensive comparative analysis of machine learning methods, highlighting the effectiveness of our feature selection strategy over conventional connectivity-based approaches. This systematic exploration not only validates our model but also extends its applicability to broader neurodevelopmental disorder detection. We conclude with a discussion on current trends and future perspectives in automated ADHD detection, underscoring the transformative potential of our EEG-based machine learning framework. The remarkable accuracies achieved by SVM and KNN underscore the utility of our approach, setting a new benchmark for early and precise ADHD diagnostics in clinical settings.

Keywords:

attention deficit hyperactivity disorder (ADHD) , machine learning (ML) , support vector machine (SVM) , K-nearest neighbors (KNN) , electroencephalography (EEG)