HARNESSING FRACTAL THEORY FOR BIOMEDICAL SIGNAL ANALYSIS: A COMPREHENSIVE REVIEW

Abstract

Fractal theory has become an essential tool for analyzing complex biomedical signals, offering novel methods to interpret physiological data that traditional approaches struggle to decipher. This review explores how fractal analysis has transformed clinical diagnostics, disease monitoring, and personalized treatment planning across various biomedical domains, including Electrocardiography (ECG), Electroencephalography (EEG), Electromyography (EMG), Phonocardiogram (PCG), Magnetoencephalography (MEG), and Galvanic Skin Response (GSR). Key advancements, such as early detection of cardiac anomalies, differentiation of neurological disorders, and improved neuromuscular rehabilitation, underscore the clinical relevance of fractal-based methods. By identifying subtle patterns in physiological signals, fractal analysis enhances the precision of diagnosis, and supports real-time monitoring in clinical practice. The review also examines the challenges of implementing fractal techniques in healthcare, including data noise, nonstationarity, and algorithm optimization; Future innovations, particularly the integration of machine learning and real-time monitoring systems, hold significant potential for advancing biomedical research and improving patient outcomes. This work highlights fractal analysis as a vital asset in modern medicine, bridging the gap between complex data interpretation and effective clinical application.