ECG Signal Filtering with Peak and Phase Detection Algorithms

This algorithm is widely applied in ECG signal processing for filtering, peak detection, phase detection, and median value detection. The implementation typically involves digital filtering techniques (such as Butterworth or Chebyshev filters) to effectively remove noise from ECG signals while preserving essential morphological features. For peak detection, the algorithm employs threshold-based methods or derivative analysis to accurately identify R-peaks and other characteristic points in the cardiac cycle. Phase detection utilizes wavelet transforms or Hilbert transform methods to precisely measure signal phases, while median detection incorporates sliding window techniques for robust baseline estimation. This comprehensive algorithm plays a crucial role in ECG signal processing and is extensively used in medical diagnostics and health monitoring applications, providing reliable analysis through efficient MATLAB or Python implementations that include functions for signal preprocessing, feature extraction, and quality assessment.