ECG Signal Power Spectral Analysis Simulation

In MATLAB, power spectral analysis of ECG signals can be performed using two different spectrum estimation algorithms. First, we can implement the Welch method, which is a commonly used spectrum estimation technique that effectively analyzes signal frequency characteristics through windowed periodogram averaging. The implementation typically involves using MATLAB's `pwelch` function with parameters like window size, overlap percentage, and FFT length to optimize spectral resolution and variance reduction. Second, we can utilize the Yule-Walker method, which is an autoregressive (AR) spectrum estimation approach based on autocorrelation functions. This method provides more accurate analysis of signal spectral distribution by modeling the signal as an AR process. In MATLAB implementation, this can be achieved using the `pyulear` function where we specify the AR model order and properly handle the autocorrelation matrix computation for optimal spectral estimation. Through comparative analysis of these two algorithms, we can gain comprehensive understanding of ECG signal spectral characteristics, providing valuable references for related research and clinical applications. The comparative analysis can include code implementations that evaluate spectral resolution, estimation variance, and computational efficiency under different signal conditions. Additionally, we can further explore other spectrum estimation methods such as the Multitaper method or Burg's method using corresponding MATLAB functions (`pmtm` and `pburg`) to broaden our understanding and analysis capabilities for ECG signals.