Digital Modelling of Low-Frequency ECG Signals Denoising

The problem of low-frequency noise (baseline wander) in long-duration digital electrocardiogram (ECG) signals, which can distort critical diagnostic features such as the ST-segment and T-wave morphology, is considered. Digital filtering methods are studied with an emphasis on low-frequency noise extraction and correction using Chebyshev type II and Butterworth filters synthesized in Python. The results show that a 7th-order high-pass filter with a cutoff frequency of 1 Hz effectively isolates the zero-potential line, whereas the filtfilt function is essential to avoid phase distortions. The success of the filtering method depends on the rate of change of the zero-potential line, and further work is required to develop quantitative criteria for evaluating and correcting filter-induced distortions. The proposed approach aims to improve automated ECG analysis and reduce false alarms in cardiac-monitoring systems.