FPGA Implementation of a ECG-DAC-SPI Interface for Medical Applications

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FPGA Implementation of a ECG-DAC-SPI Interface for Medical Applications | This project presents the design and implementation of an ECG-DAC-SPI interface for medical applications using the Xilinx Spartan-6 FPGA platform and the MCP4921 12-bit SPI DAC. The objective is to process pre-recorded ECG signals from the MIT-BIH database, reconstruct the signal digitally, and output it as an accurate analog waveform suitable for real-time monitoring and simulation. The system is designed to meet the stringent requirements of medical-grade signal fidelity and low-latency processing. The FPGA-based implementation comprises several key modules, including digital ECG data acquisition, optional noise filtering, and a custom SPI communication controller. The ECG signal, preloaded into FPGA memory, is scaled and quantized to match the 12-bit resolution of the MCP4921 DAC. A low-pass FIR filter is implemented on the FPGA to enhance signal quality by removing high-frequency noise, ensuring smooth signal. A Verilog HDL-based SPI controller facilitates precise communication with the DAC, synchronizing data transfer and ensuring real-time signal conversion. The reconstructed analog ECG waveform is visualized on an oscilloscope to validate its fidelity to the original dataset. The DAC, interfaced via the FPGA’s SPI controller, is chosen for its high resolution and compatibility with low-latency applications. The design is synthesized, implemented, and tested on the Xilinx Spartan-6 FPGA platform. The project includes extensive simulation and hardware testing, evaluating parameters such as SPI throughput, waveform accuracy, and system latency. Results demonstrate that the system achieves precise signal reconstruction and reliable analog output, suitable for medical applications. This work highlights the use of FPGA technology and the MCP4921 DAC for scalable and reconfigurable ECG signal processing systems. It provides a robust platform for integration into advanced medical devices, including real-time ECG monitors, simulators, and portable diagnostic tools. Future extensions of the design could include integration of live ECG sensors, advanced noise filtering, or wireless transmission for telemedicine applications.