QRS Detection Using the Tompkins Algorithm

In this article, we explore the implementation of QRS detection using the Tompkins algorithm. The Tompkins algorithm is a biomedical signal processing technique widely employed in electrocardiogram (ECG) signal analysis. This algorithm combines a filter bank approach with moving average filters to accurately detect QRS complexes in ECG signals. The core implementation involves multiple processing stages: bandpass filtering to eliminate baseline wander and high-frequency noise, derivative computation to emphasize QRS slopes, squaring for signal non-linear amplification, and moving window integration for noise suppression. Key advantages include high detection accuracy, computational efficiency, and adaptability to diverse ECG signal morphologies. The algorithm has been successfully deployed in medical diagnostics and health monitoring systems, making it an essential technique in modern ECG signal processing. Typical implementation involves threshold-based peak detection with adaptive updating mechanisms to handle varying signal amplitudes.