Signal Detrending Using Least Squares Method with MATLAB Implementation

This article demonstrates the application of least squares method for removing trend components from signals. The implementation includes MATLAB code that reads data directly from Excel files, accompanied by actual measurement datasets. The detrending process involves fitting a polynomial trend line to the signal data using least squares regression, then subtracting this trend from the original signal to obtain the detrended version. Key MATLAB functions used include xlsread() for data import and polyfit() for polynomial fitting. These procedures help reveal the underlying characteristics of signals by eliminating slow-varying trends, providing an effective preprocessing method for signal analysis. Furthermore, this methodology can be extended to various domains such as finance and medical data processing, enabling more accurate analysis and interpretation of time-series data. Through these techniques, we can uncover meaningful patterns in data and extract valuable insights that might otherwise be obscured by dominant trends.