Independent Component Analysis and Extended Kalman Filter for ECG signal filtering

Abstract

During acquisition or transmission of the Electrocardiogram, noises generated from the surrounded electrical equipment, the patient’s motion, movement of the electrodes, or contraction of the muscle around the heart usually interfere with the obtained signal. The interference of these noises in the frequency domain may mask the desired signal and obstruct the diagnosis process. Blind Source Separation techniques and Model-based filtering methods have shown promising results in ECG signal processing. This work pointed to assess the performance of Independent Component Analysis and Extended Kalman Filter in removing the most common ECG noise, such as muscle contraction, baseline shift, and electrode motion artifact. Testing has been executed on a formed signal set by adding noises from the MIT noise stress test database to signals from the MIT-BIH arrhythmia database at a different signal to noise ratio. Performance comparison demonstrates that both techniques show satisfying results in muscle artifact filtering, while ICA based filtration is more accurate than EKF in reducing baseline wander and electrode movement artifacts.