Comparative Study of Baseline Drift Removal Methods

Multiple approaches exist for comparative research in baseline drift removal. These include but are not limited to: baseline drift correction, wavelet transform decomposition, and Kalman filtering algorithms. Implementation typically involves using signal processing libraries (e.g., MATLAB's wavelet toolbox or Python's SciPy) where wavelet-based methods employ thresholding techniques on decomposition coefficients, while Kalman filters require state-space modeling of signal trends. Additionally, these methods can be integrated with other preprocessing techniques like Empirical Mode Decomposition (EMD) or Principal Component Analysis (PCA) to enhance baseline removal effectiveness. For instance, EMD can separate intrinsic mode functions before applying drift correction, while PCA helps identify dominant variation patterns. Therefore, practical applications should select appropriate methods based on specific signal characteristics, sampling rates, and computational requirements.