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

Key Points

• The primary aim is to develop an automated framework for processing EEG signals to diagnose neurological diseases early and accurately.
• Designed an automated diagnostic support architecture based on EEG signal processing.
• Integrated advanced deep learning models for analyzing EEG signals.
• Utilized graph-based feature extraction techniques.
• The EEG-DSS-SMCNN model efficiently processes noisy EEG signals.
• Enhanced reliability in disease classification was achieved.
• The system shows potential for real-time clinical decision support and monitoring of neurological diseases.