Innovative Framework for Precise Peripheral Arterial Disease Diagnostics Through Multi-Modal Digital Signal Processing on Xilinx FPGA Platform

The mortality rate due to the Arterial disease is increasing every day. Because of this need for the accurate and early detection of the Peripheral Arterial Disease (PAD) increases day to day to avoid the consequences due to the health issues. This is addressed in this research work by a introducing an innovative framework centered on multi-modal Digital Signal Processing (DSP) filtering. Specifically targeting Electrocardiogram (ECG) signals, our methodology intricately weaves together various DSP filters, such as low-pass, high-pass, and adaptive filters. Integrating AI with the medical signal processing paves the way for accurate detection of PAD in its early stages. The unique contribution of this research lies in its implementation using Xilinx FPGA. The proposed approach not only advances the accuracy of PAD diagnostics but also showcases the adaptability and scalability of the hardware implementation. The inherent configurability of Xilinx FPGA provides a foundation for potential enhancements and adjustments to the multi-modal DSP filtering paradigm, ensuring a sustainable and evolving solution for cardiovascular healthcare. This tool chain, seamlessly integrating Xilinx and Verilog, stands as a formidable technological advancement poised to revolutionize the landscape of cardiovascular diagnostics.