A Pole Radius Varying Notch Filter with Transient Suppression for Electrocardiogram

Authors: Ramesh Rajagopalan, Adam Dahlstrom

Abstract:

Noise removal techniques play a vital role in the performance of electrocardiographic (ECG) signal processing systems. ECG signals can be corrupted by various kinds of noise such as baseline wander noise, electromyographic interference, and powerline interference. One of the significant challenges in ECG signal processing is the degradation caused by additive 50 or 60 Hz powerline interference. This work investigates the removal of power line interference and suppression of transient response for filtering noise corrupted ECG signals. We demonstrate the effectiveness of infinite impulse response (IIR) notch filter with time varying pole radius for improving the transient behavior. The temporary change in the pole radius of the filter diminishes the transient behavior. Simulation results show that the proposed IIR filter with time varying pole radius outperforms traditional IIR notch filters in terms of mean square error and transient suppression.

Keywords: Notch filter, ECG, transient, pole radius.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1091162

References:

[1] V. Almenar and A. Albiol, A New Adaptive Scheme for ECG Enhancement. Signal Processing, 75(3), 1999, pp. 253 –263.
[2] J. M. Leski, and N. Henzel, ECG Baseline Wander and Powerline Interference Reduction Using Nonlinear Filter Bank. Signal Processing, 85(4), 2005, pp.781 – 793.
[3] M. Kotas, Projective Filtering of Time-Aligned ECG Beats for Repolarization Duration Measurement. Computer Methods and Programs in Biomedicine, 85(2), 2007, pp.115 – 123.
[4] M. Korrek and A. Nizam, Clustering Mit-Bih Arrhythmias with Ant Colony Optimization Using Time Domain and PCA Compressed Wavelet Coefficients. Digital Signal Processing, 20(4), 2010, pp.1050 – 1060.
[5] D. Bansal, M. Khan, and A. K. Salhan, A Computer Based Wireless System for Online Acquisition, Monitoring and Digital Processing of ECG Waveforms. Computers in Biology and Medicine, 39(4), 2009, pp. 361 – 367.
[6] J. Yan, Y. Lu, J. Liu, X. Wu, and Y. Xu, Selfadaptive Model-Based ECG Denoising Using Features Extracted by Mean Shift Algorithm. Biomedical Signal Processing and Control, 5(2), 2010, pp.103 – 113.
[7] M. Ferdjallah, R.E. Barr, Adaptive Digital Notch Filter Design on the Unit Circle for the Removal of Powerline Noise from Biomedical Signals, IEEE Trans. Biomed. Eng. 41 (4), 1994, pp. 529–536.
[8] S.-C. Pei, C.-C. Tseng, Elimination of AC Interference in Electrocardiogram Using IIR Notch Filter with Transient Suppression, IEEE Trans. Biomed. Eng. 42 (7), 1995, pp. 1128–1132.
[9] N.V. Thakor, Y.-S. Zhu, Application of Adaptive Filtering to ECG Analysis: Noise Cancellation and Arrhythmia Detection, IEEE Trans. Biomed. Eng. 38 (8), 1991, pp.785–794.
[10] C.-H.H. Chu, E.J. Delp, Impulsive noise Suppression and Background Normalization of Electrocardiogram Signals Using Morphological Operators, IEEE Trans. Biomed. Eng. 36 (2), 1989, pp. 262–273.
[11] A.S Khaing and Z. M. Naing, Quantitative Investigation of Digital Filters in Electrocardiogram with Simulated Noises, International Journal of Information and Electronics Engineering, 1 (3), 2011.
[12] J. Piskorowski, Digital Q-Varying Notch IIR Filter with Transient Suppression, IEEE Trans. Instrum. Meas. 59 (4), 2010, pp. 866–872.
[13] L. Tan, Pole-Radius-Varying IIR Notch Filter With Transient Suppression‖, in IEEE Transactions on Instrumentation and Measurement, Vol. 61, 2012, pp. 1684 - 1691.
[14] A.L. Goldberger, L.A.N. Amaral, L. Glass, J.M. Hausdorff, P.C. Ivanov, R.G. Mark, J.E. Mietus, G.B. Moody, C.-K. Peng, H.E. Stanley, PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals, Circulation 101.