ECG Signal Processing with Multiple Filtering Techniques and Analysis

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Resource Overview

This program implements ECG signal processing using three filtering methods: adaptive filtering, smoothing filter, and wavelet filtering. It also includes R-peak detection and energy analysis functionality. The MATLAB-based solution features a comprehensive GUI interface for intuitive operation and visualization of signal processing results.

Detailed Documentation

This program implements comprehensive ECG signal processing using three distinct filtering approaches: adaptive filtering for noise reduction in non-stationary environments, smoothing filters for baseline wander removal, and wavelet filtering for multi-resolution analysis. The implementation includes robust R-peak detection algorithms using threshold-based methods or Pan-Tompkins algorithm for accurate QRS complex identification. Energy analysis is performed through spectral density calculations and signal power measurements. The MATLAB-based graphical user interface (GUI) provides interactive controls for parameter adjustment, real-time visualization of filtered signals, and comparative analysis of different filtering techniques. Key functions include ecg_adaptive_filter() for LMS-based adaptation, wavelet_denoise() for multi-level decomposition, and r_peak_detector() with adjustable thresholds for precise cardiac event detection.

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