Vol. 7, Issue 1, Part B (2025)

Biomedical signal processing (BSP) is a vital interdisciplinary field that integrates engineering, computer science, and biology to analyze physiological signals for improved healthcare outcomes. It encompasses the acquisition, analysis, and interpretation of signals such as electrocardiograms (ECG), electroencephalograms (EEG), and electromyograms (EMG), which provide critical insights into human health. BSP methodologies, including time and frequency domain analysis, wavelet transforms, and machine learning algorithms, enable the extraction of significant data for medical diagnosis and continuous health monitoring. The applications of BSP extend to telemedicine, where wearable devices equipped with biosensors facilitate remote patient monitoring, particularly in chronic disease management. Additionally, BSP plays a crucial role in medical imaging technologies like MRI and CT, enhancing image reconstruction and automating pathology identification. Despite its advancements, BSP faces challenges such as data heterogeneity, real-time processing requirements, and algorithmic complexity. Future research aims to optimize existing techniques, improve computational efficiency, and address patient variability to maximize BSP's potential in clinical settings. This paper explores the fundamentals, applications, and challenges of BSP, highlighting its transformative role in modern medicine and its potential to redefine patient care practices through innovative health monitoring solutions.