Several Methods for Baseline Drift Removal

This article introduces several methods for baseline drift removal and provides detailed MATLAB implementation procedures. These techniques are primarily applied in ECG signal processing to enhance signal accuracy and reliability. Notably, these methods are not limited to ECG signals but can also be effectively utilized in other biomedical signal processing domains such as EEG and EMG analysis, as well as any signal processing tasks requiring baseline correction. The MATLAB implementations typically involve signal filtering algorithms, polynomial fitting techniques, and wavelet transform methods, where key functions like filtfilt for zero-phase filtering, polyfit for polynomial regression, and wden for wavelet denoising are commonly employed. Therefore, this content serves as valuable reference material for researchers in signal processing fields, offering practical code examples and algorithmic explanations for effective baseline drift elimination.