Filtering of Human ECG Signals Using Butterworth Low-Pass Filters

In human electrocardiogram (ECG) signal processing, we can effectively eliminate 50Hz power line noise by implementing a Butterworth low-pass filter. This approach involves designing digital filters with specific cutoff frequencies using signal processing algorithms. The implementation typically utilizes filter design functions like butter() in MATLAB or scipy.signal.butter() in Python to generate filter coefficients. We can systematically compare the filtering effectiveness by testing Butterworth filters of different orders (such as 2nd, 4th, and 6th order). Higher-order filters generally provide steeper roll-off characteristics but may introduce more phase distortion. The practical implementation involves applying the filter using convolution operations or recursive filtering techniques, with careful consideration of sampling frequency and cutoff frequency settings. This processing method enables better noise removal, resulting in cleaner ECG signals for accurate medical analysis. Key implementation steps include signal preprocessing, filter design parameter selection, frequency response analysis, and post-filtering signal validation to ensure clinical usability.