ECG Signal Filtering

ECG signal filtering is a signal processing technique used to remove noise and interference from electrocardiogram signals, enabling more accurate analysis and identification of cardiac electrical activity. As a crucial step in ECG analysis, it improves diagnostic accuracy and reliability by applying appropriate filtering algorithms and techniques to reduce noise caused by muscle movement, power line interference, and respiratory factors. Commonly implemented filtering methods include low-pass filters (typically using Butterworth or Chebyshev designs with cutoff frequencies around 100-150 Hz to eliminate high-frequency noise), high-pass filters (often set at 0.5-1 Hz cutoff to remove baseline wander), and band-pass filters (combining both with typical passband of 0.5-100 Hz). ECG signal filtering finds extensive applications not only in clinical medicine for cardiac disease diagnosis and monitoring but also plays vital roles in scientific research and biomedical engineering fields. Implementation often involves digital filter design using MATLAB's Signal Processing Toolbox functions like `filter()`, `filtfilt()` for zero-phase filtering, or wavelet denoising techniques for non-stationary noise removal.