A Physiological Signal Denoising Method Using Adaptive Kalman Filter

During the process of physiological signal acquisition, the target signal often encounters various sources of noise interference, including external electromagnetic field interference and internal physiological electrical signal interference, with power line interference being the most severe. These noise interferences pose a significant inconvenience to the analysis and processing of physiological signals. This study proposed a denoising method based on adaptive Kalman filtering, leveraging the advantages of dynamic weight adjustment in adaptive filtering and the state estimation function of Kalman filtering to accurately estimate the signal and noise states, thus eliminating power line interference and other noise interferences mixed into physiological signals. By processing electrocardiogram (ECG) and blink electrooculogram (EOG) collected in a normal experimental environment, the results demonstrate that the adaptive Kalman filter designed in this study can effectively eliminate power line interference and make the target signal clearer and cleaner. The average reduction in spectral amplitude at 50Hz exceeds 52.6 dB. Compared to traditional notch filter processing, this method not only eliminates the fundamental frequency and harmonic components of power line interference but also preserves the useful components of the target signal, providing a more effective solution for the analysis and processing of physiological signals.