Quantitative EEG Decomposition and Silver Howl Optimization for Multi-Stage Autism Spectrum Disorder Classification

A complicated neurodevelopmental disorder, autism spectrum disorder (ASD) is represented by difficulties with cognition and behavior. Early and accurate diagnosis is crucial for effective intervention. However, existing machine learning methods for ASD detection face limitations, including inefficiencies in EEG signal noise removal, challenges in feature extraction, and difficulties in stage-wise classification. To address these challenges, the SilverHowl-QDecomp Framework is proposed to enhance EEG-based ASD classification through advanced signal processing and feature extraction techniques. The LaplaZ Filter effectively minimizes noise while preserving critical signal components and normalization techniques ensure data consistency. Furthermore, the proposed feature extraction method captures nonlinear and dynamic EEG characteristics, improving classification accuracy by isolating essential features and reducing computational complexity. To enhance ASD stage classification, the SilverHowl Classifier was introduced, implementing the BCIAUT-P300 dataset and leveraging optimized hyperparameters to achieve better discrimination between ASD stages. With an accuracy of 0.985 and a precision of 0.98572, this method performs better than conventional techniques, thereby offering a more reliable and precise classification framework. The proposed method contributes to personalized ASD interventions by enabling more accurate and stage-specific diagnoses.