Design and Development of an Automated Electroencephalogram Signal Processing and Diagnostic Support Architecture Employing Advanced Deep Learning Models for Early Accurate and Scalable Detection of Neurological Diseases

The results highlight the effectiveness of integrating advanced signal preprocessing, graph-based feature extraction, and deep learning within a unified diagnostic framework. The proposed EEG-DSS-SMCNN model efficiently handles noisy EEG signals and captures complex neurological patterns, making it suitable for early-stage disease diagnosis in IoMT-enabled healthcare environments. The optimized SMCNN further enhances classification reliability, suggesting strong potential for real-time clinical decision support and scalable neurological disease monitoring systems.