Wavelet Transform Implementation for ECG Signal Filtering Using Thresholding Techniques

This implementation utilizes wavelet transform methods for ECG signal filtering, incorporating hard thresholding, soft thresholding, and enhanced thresholding techniques. The filtering performance is quantitatively evaluated using two key metrics: mean square error (MSE) and signal-to-noise ratio (SNR). From a coding perspective, the implementation typically involves: - Wavelet decomposition using functions like wavedec() to break down ECG signals into approximation and detail coefficients - Threshold application through customized functions that modify coefficients based on selected thresholding rules - Signal reconstruction using waverec() to generate filtered ECG waveforms - Performance calculation routines implementing MSE = mean((original_signal - filtered_signal).^2) and SNR = 10*log10(var(original_signal)/MSE) Further research can explore different filtering methodologies in ECG signal processing, examining their respective advantages, limitations, and optimal application scenarios. Through comprehensive investigation and experimental validation, the effectiveness of ECG signal filtering can be significantly enhanced, providing a more accurate and reliable data analysis foundation for medical applications. This approach enables researchers to systematically compare thresholding strategies and optimize parameters for specific ECG characteristics.