A simulation-based ECG arrhythmia detection system using the Pan-Tompkins algorithm successfully detected R-peaks and classified heart rhythms into normal, bradycardia, and tachycardia.
A simulation-based system using the Pan-Tompkins algorithm and Verilog HDL can effectively process ECG signals to detect R-peaks and classify basic arrhythmias.
Electrocardiogram (ECG) signals play a vital role in monitoring heart activity and identifying irregular cardiac conditions such as arrhythmia. Early and accurate detection of these abnormalities is important for effective diagnosis and treatment. This work presents a simulation-based approach for ECG arrhythmia detection using the Pan–Tompkins algorithm and Verilog HDL. Standard ECG recordings are taken from the MIT-BIH Arrhythmia Database to ensure consistency and reliability in analysis. The acquired signals are first pre-processed using digital filtering techniques to reduce noise and improve signal clarity. The Pan–Tompkins algorithm is then applied to identify QRS complexes and detect R-peaks. Based on the extracted features, heart rate is calculated and used to classify cardiac conditions into normal rhythm, bradycardia, and tachycardia. The complete system is designed and verified using simulation tools, demonstrating accurate detection performance. This approach provides a simple, cost-effective, and efficient solution for ECG signal analysis and validation of arrhythmia detection methods.
Niharika et al. (Wed,) conducted a other in Arrhythmia. Pan-Tompkins Algorithm and Verilog HDL simulation was evaluated on ECG Arrhythmia Detection (R-peak detection and heart rate calculation). A simulation-based ECG arrhythmia detection system using the Pan-Tompkins algorithm successfully detected R-peaks and classified heart rhythms into normal, bradycardia, and tachycardia.