ECG Signal Processing
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In the ECG signal preprocessing workflow, we first perform filtering and denoising operations to eliminate interference components from the signal. This typically involves implementing bandpass filters (e.g., 0.5-40 Hz range) to remove baseline wander and high-frequency noise, followed by advanced techniques like wavelet denoising for optimal signal cleaning. Subsequently, we conduct QRS complex detection using algorithms such as Pan-Tompkins method, which applies derivative filters and moving average integration to precisely identify QRS wave locations. Simultaneously, we perform P-wave and T-wave detection through morphological analysis and template matching approaches, enabling comprehensive analysis of other critical ECG features. These processing steps, when implemented through appropriate signal processing libraries (e.g., MATLAB's Signal Processing Toolbox or Python's BioSPPy), allow for more accurate analysis and interpretation of electrocardiographic signals.
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