Heart Rate Calculation Using the Pan-Tompkins Algorithm

In the medical field, heart rate monitoring is critically important. The Pan-Tompkins algorithm represents a widely-used technique for calculating heart rate, achieving accurate results by analyzing QRS complexes in electrocardiogram (ECG) signals. This algorithm operates through several key processing stages, including signal preprocessing, QRS complex detection, QRS localization, and heart rate computation. During the preprocessing stage, digital filters are implemented to remove noise and interference from the raw ECG signal. This typically involves bandpass filtering to eliminate baseline wander and high-frequency noise, followed by derivative and squaring operations to enhance QRS complex characteristics. The QRS detection phase employs threshold-based techniques to identify potential QRS complexes. This process uses adaptive thresholds that dynamically adjust based on signal characteristics, ensuring robust detection across varying signal qualities. In the QRS localization stage, moving average filters and dynamic threshold techniques are applied to precisely pinpoint QRS complex positions. The algorithm maintains running estimates of signal and noise levels to optimize detection accuracy. Finally, heart rate is calculated by measuring the time intervals between consecutive QRS complexes. The algorithm typically uses a buffer of recent RR intervals to compute average heart rate while filtering out physiologically implausible values. The Pan-Tompkins algorithm provides a straightforward yet effective approach for implementing accurate heart rate monitoring systems, making it suitable for both clinical applications and wearable health devices.