Improvement of an extended Kalman filter power line interference suppressor for ECG signals

The powerline interference reduction in ECG records is a challenging problem which is still open for research. The powerline signal, measured directly from the transmission line may have amplitude, phase and frequency variations. These reasons make the classical filtering methods sub-optimal in the powerline interference reduction. We propose a tracking method based on Kalman filtering which uses an state space model for the noisy signal and allows adequate discrimination between the ECG signal and the perturbation, even during non-stationarities. The parameters of this algorithm are optimized via genetic algorithms, obtaining a set of values that give it a mean correlation index on the QT database over 0,99.