Implementation of Pan-Tompkins Algorithm in MATLAB for ECG Signal Analysis

This document presents a detailed implementation of the Pan-Tompkins algorithm in MATLAB for electrocardiogram (ECG) signal analysis. Developed by James Pan and Willis Tompkins in 1985, this algorithm has become the industry standard for QRS complex detection in ECG signals. The MATLAB implementation leverages built-in signal processing functions and custom code to achieve robust QRS detection.

The algorithm follows a multi-stage processing pipeline: bandpass filtering to remove noise and baseline wander, differentiation to highlight QRS slopes, squaring to emphasize R-peaks, moving window integration for signal smoothing, and adaptive thresholding for accurate QRS complex identification. Each stage is implemented using MATLAB's signal processing toolbox functions like filter(), diff(), and moving average techniques, with custom threshold adjustment logic based on signal characteristics.

MATLAB's extensive signal processing capabilities make it particularly suitable for implementing the Pan-Tompkins algorithm. Researchers can utilize functions such as fdesign for filter design, conv for convolution operations, and findpeaks for peak detection enhancement. The implementation includes real-time adaptation mechanisms where threshold values are dynamically updated based on detected signal peaks and noise levels.

The MATLAB implementation provides healthcare professionals and researchers with a powerful tool for ECG analysis, enabling precise extraction of heart rate variability metrics and arrhythmia detection. The code structure typically includes main processing functions, helper functions for signal preprocessing, and visualization modules for result verification using MATLAB's plotting capabilities.