An Adaptive Kalman Filter Bank for ECG Denoising

Model-based Bayesian frameworks proved their effectiveness in the field of ECG processing. However, their performances rely heavily on the pre-defined models extracted from ECG signals. Furthermore, their performances decrease substantially when ECG signals do not comply with their models- a situation generally occurs in the case of arrhythmia-. In this paper, we propose a novel Bayesian framework based on Kalman filter, which does not need a predefined model and can adapt itself to different ECG morphologies. Compared with the previous Bayesian techniques, the proposed method requires much less preprocessing and it only needs to know the location of R-peaks to start ECG processing. Our method uses a filter bank comprised of two adaptive Kalman filters, one for denoising QRS complex (high frequency section) and another one for denoising P and T waves (low frequency section). The parameters of these filters are estimated and iteratively updated using expectation maximization (EM) algorithm. In order to deal with nonstationary noises such as muscle artifact (MA) noise, we used Bryson and Henrikson's technique for the prediction and update steps inside the Kalman filter bank. We evaluated the performance of the proposed method on different ECG databases containing signals having morphological changes and abnormalities such as atrial premature complex (APC), premature ventricular contractions (PVC), Ventricular Tachyarrhythmia (VT) and sudden cardiac death (SCD). The proposed algorithm was compared with several popular ECG denoising methods such as wavelet transform (WD) and empirical mode decomposition (EMD). The comparison results showed that the proposed method performs well in the presence of various ECG morphologies in both stationary and non-stationary environments especially at low input SNRs.