Real-Time Embedded Processing of Cardiac Bioelectric Signals: Microprocessor-Based Algorithm Design and Clinical Decision Support Applications

Cardiovascular diseases remain among the leading causes of mortality worldwide, necessitating the development of efficient diagnostic and monitoring technologies. Real-time embedded processing of cardiac bioelectric signals has emerged as a critical area of research, enabling continuous monitoring and rapid clinical intervention. Electrocardiogram (ECG) signals provide valuable information regarding cardiac function; however, their analysis requires robust signal processing algorithms capable of operating on resource-constrained microprocessors. Advances in embedded systems have facilitated the implementation of sophisticated filtering, feature extraction, and classification techniques directly on portable and wearable devices. These systems support real-time detection of arrhythmias, ischemic events, and other cardiac abnormalities while minimizing computational overhead and power consumption. Furthermore, the integration of embedded signal processing with clinical decision support systems enhances diagnostic accuracy and assists healthcare professionals in making timely decisions. This paper examines the design and implementation of microprocessor-based algorithms for cardiac bioelectric signal processing and explores their role in modern clinical decision support applications. The discussion highlights challenges related to signal noise, computational efficiency, and system reliability while emphasizing future opportunities in intelligent healthcare monitoring.