<![CDATA[Biomedical signal processing is essential for modern diagnostics, monitoring, and preventive healthcare in public health and mobile health (mHealth) systems. Signals such as electroencephalography (EEG), electromyography (EMG), and heart rate variability (HRV) offer vital insights into brain, muscle, and cardiovascular health. However, achieving real-time, energy-efficient, and scalable processing remains challenging for conventional hardware such as central-processing units (CPUs), graphics-processing units (GPUs), and application-specific integrated circuits (ASICs). Field-programmable gate arrays (FPGAs) provide a promising alternative through their reconfigurability, parallelism, and adaptability to dynamic biomedical workloads. This review examines FPGA-based implementations for EEG, EMG, and HRV processing, focusing on key metrics including latency, throughput, and power efficiency. It also discusses design strategies such as low-power optimization, hardware–software co-design, and FPGA-based machine learning acceleration, with attention to data integrity and security in medical contexts. Integration with wearable, portable, and telemedicine platforms is explored, alongside comparative analyses with traditional computing architectures. The paper identifies challenges in power–performance trade-offs, design complexity, and clinical validation, and highlights emerging directions such as artificial intelligence (AI)-driven FPGA platforms, neuromorphic design, and sustainable low-cost solutions for large-scale health monitoring. Overall, FPGA-based biomedical signal processing emerges as a foundation for intelligent, efficient, and accessible next-generation public-health technologies.]]>
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