Multimodel Integration for FPGA based ECG Signal Processing

Monitoring ECG signals is essential, as it provides critical information about a person’s health. This paper describes an FPGA-based ECG signal processing system designed using a multimodel approach that integrates MATLAB and ModelSim. Raw ECG signals are sourced from PhysioNet, preprocessed through MATLAB to denoise the signals, and then converted into hexadecimal format for input into Verilog modules. The ModelSim environment is used to implement Verilog-based logic for detecting heartbeats. Heartbeat detection is performed using the Pan-Tompkins algorithm, a widely used method for identifying the QRS complex in ECG signals. Key stages such as signal squaring, integration, thresholding, and R-peak detection are modeled to extract heart rate information. The design emphasizes real-time performance and suitability for future hardware deployment in wearable healthcare devices. Simulations are conducted in ModelSim, and the results are validated, demonstrating the potential of FPGAs for biomedical applications.