Removal Method of Baseline Drift from ECG Signals Based on Morphology Filter

Abstract:

Baseline drift is the main noise of ECG signals which affects the detection accuracy so its removal plays a significantrole in the ECG signal preprocessing. Complex calculation and non-optimal signal processing cause problems of ineffective results and low real-time effects in traditional methods. This paper designs a new filter to remove baseline drift based on the theory of mathematical morphology, which is created by the geometric parameters of the ECG signal. Experiments show that the method can effectively remove the noise of baseline drift by simple computation and is helpful to improve the detection accuracy.

• Periodical: Applied Mechanics and Materials (Volumes 427-429)
• Pages: 1691-1695
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.427-429.1691
• Online since: September 2013
• Authors: Yu Pang*, Lu Deng, Jin Zhao Lin, Zhang Yong Li, Guo Quan Li, Qian Neng Zhou
• Keywords: Baseline Drift, ECG Signal, Mathematical Morphology, Noise

References

[1] Mneimneh MA, Yaz EE, Johnson MT, Povinell RJ. An adaptive Kalman filter for removing baseline wandering in ECG signals. In: Computers in Cardiology, Sept, 2006. Valencia, pp.253-256.

[2] Yang, Lin. Removal of Pulse Waveform Baseline Drift Using Cubic Spline Interpolation. 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE), 2010. p.1~3.

DOI: 10.1109/icbbe.2010.5516523

[3] Lisheng Xu. Wavelet-based cascaded adaptive filter for removing baseline drift in pulse waveforms. Biomedical Engineering, IEEE Transactions on  (Volume: 52 ,  Issue: 11). p.1973~1975. (2005).

DOI: 10.1109/tbme.2005.856296

[4] Dianguo Cao. Removing Baseline Drift in Pulse Waveforms by a Wavelet Adaptive Filter. Bioinformatics and Biomedical Engineering, 2008. ICBBE 2008. p.2135~2137.

DOI: 10.1109/icbbe.2008.863

[5] Ezenwa, B.N. A baseline tracking algorithm for drift reduction in electrocardiography. Engineering in Medicine and Biology Society, 1988. Proceedings of the Annual International Conference of the IEEE. p.1230~1232 vol. 3.

DOI: 10.1109/iembs.1988.94889

[6] Gritzali, F. An automatic baseline drift correction method. Computers in Cardiology 1989, Proceedings. p.265~267.

DOI: 10.1109/cic.1989.130539

[7] Pandit, S.V. ECG baseline drift removal through STFT. Engineering in Medicine and Biology Society, 1996. Bridging Disciplines for Biomedicine. Proceedings of the 18th Annual International Conference of the IEEE  (Volume: 4). p.1405~1406 vol. 4.

DOI: 10.1109/iembs.1996.647477

[8] Chouhan, V.S. Total Removal of Baseline Drift from ECG Signal. Computing: Theory and Applications, 2007. ICCTA '07. International Conference on. p.512~ 515.

DOI: 10.1109/iccta.2007.126

[9] P. Marago, R. W. Schafer. Morphological filter [J]. IEEE Trans Acoustics, speech, and Signal processing, ASSP, 1987, 35(8): 1153~1184.

[10] Sun Y, Chan K L, Krishnan S M. ECG signal conditioning by morphological filtering. Computers in Biology and Medicine, 2002, 32(6): 465~479.

DOI: 10.1016/s0010-4825(02)00034-3

[11] Verma, Rishendra. An integration of improved median and morphological filtering techniques for electrocardiogram signal processing. Advance Computing Conference (IACC), 2013. IEEE 3rd International. p.1223~1228.

DOI: 10.1109/iadcc.2013.6514402