Abstract
The early and objective detection of neurodevelopmental conditions in children remains a critical challenge due to the reliance on subjective behavioral assessments and clinician-dependent interpretation. This study proposes a hybrid deep learning framework for the automated analysis of electroencephalographic (EEG) signals aimed at identifying neurodevelopmental patterns associated with attention and cognitive regulation disorders. The methodology integrates convolutional neural networks (CNN), gated recurrent units (GRU), and long short-term memory (LSTM) layers to extract both spatial and temporal EEG features. Preprocessing procedures include band-pass filtering, independent component analysis, segmentation into epochs, and z-score normalization. Experimental evaluation demonstrates high classification accuracy and robust generalization ability. Additionally, explainable artificial intelligence techniques are employed to interpret feature importance at the electrode level, increasing clinical transparency and trust. The obtained results confirm that hybrid deep learning architectures combined with interpretability tools can serve as reliable decision-support mechanisms in pediatric neurodiagnostics.