AI-DRIVEN BIOMEDICAL SIGNAL PROCESSING FOR EARLY DISEASE DETECTION

Abstract

Early detection of diseases plays a vital role in improving patient outcomes, reducing treatment costs, and enabling preventive healthcare. Biomedical signals such as electrocardiograms (ECG), electroencephalograms (EEG), electromyograms (EMG), and photoplethysmograms (PPG) contain critical physiological information that can indicate the onset of various medical conditions. However, traditional signal processing and rule-based diagnostic approaches often struggle to detect subtle and nonlinear patterns in noisy biomedical data. This paper presents an AI-driven biomedical signal processing framework for early disease detection. The proposed approach leverages machine learning and deep learning models to automatically extract discriminative features from biomedical signals and accurately classify disease conditions. MATLAB/Simulink-based analysis demonstrates improved detection accuracy and robustness compared to conventional signal processing techniques, validating the effectiveness of AI-driven approaches for intelligent healthcare systems.