Muscle Artifacts Cancellation Framework for ECG Signals Combining Convolution Auto-encoder and Average Beat Subtraction
Abstract
As the basic tool for the diagnosis of cardiac diseases, electrocardiogram (ECG) is often contaminated by muscle artifacts, which can cause unreliable interpretation and measurement for ECG. To adequately remove muscle artifacts which contaminate ECG signals, we propose a novel computation framework combining the convolution auto-encoder (CAE) and average beat subtraction in this paper. Firstly, the framework subtracts from the original ECG signal based on an initial average beat, which preserves the characteristics of a heart beat; the average beat is updated according to the original ECG signal to incorporate inter-beat variations. Then, the framework filters the residual ECG signal by a convolution auto-encoder (CAE), which filters out the contaminated parts and keeps the specific information related to the ECG signal. Finally, we combine the filtered residual ECG signal and updated average beat to obtain an enhanced ECG signal. Our framework is evaluated on ECG records from the MIT-BIH Arrhythmia Database, and results show that our framework outperforms existing methods in muscle artifacts removal.
References
[1]
U. Satija, B. Ramkumar, and M. S. Manikandan, A review of signal processing techniques for electrocardiogram signal quality assessment, IEEE Reviews in Biomedical Engineering, vol. PP, no. 99, pp. 1--1, 2018.
[2]
V. D. Pinto, Filters for the reduction of baseline wander and muscle artifact in the ecg, Journal of Electrocardiology, vol. 25 Suppl, no. 1, p. 40, 1992.
[3]
P. Lander and E. J. Berbari, Time-frequency plane wiener filtering of the high-resolution ecg: development and application, IEEE transactions on bio-medical engineering, vol. 44, no. 4, p. 256, 1997.
[4]
N. V. Thakor and Y. S. Zhu, Applications of adaptive filtering to ecg analysis: noise cancellation and arrhythmia detection, IEEE Trans Biomed Eng., vol. 38, no. 8, pp. 785--794, 1991.
[5]
A. Gotchev, N. Nikolaev, and K. Egiazarian, Improving the transform domain ecg denoising performance by applying interbeat and intra-beat decorrelating transforms, in IEEE International Symposium on Circuits and Systems, pp. 17--20 vol. 2, 2001.
[6]
E. Ercelebi, Electrocardiogram signals de-noising using lifting-based discrete wavelet transform, Computers in Biology & Medicine, vol. 34, no. 6, pp. 479--493, 2004.
[7]
V. V. K. D. V. Prasad, P. Siddaiah, and B. P. Rao, A new wavelet based method for denoising of biological signals, International Journal of Computer Science & Network Security, vol. 8, no. 1, pp. 238--244, 2014.
[8]
B. N. Singh and A. K. Tiwari, Optimal selection of wavelet basis function applied to ecg signal denoising, Digital Signal Processing, vol. 16, no. 3, pp. 275--287, 2006.
[9]
S. Ari, M. K. Das, and A. Chacko, Ecg signal enhancement using s-transform, Computers in Biology & Medicine, vol. 43, no. 6, p. 649, 2013.
[10]
L. Smital, M. Vitek, J. Kozumplik, and I. Provaznik, Adaptive wavelet wiener filtering of ecg signals., IEEE Transactions on Biomedical Engineering, vol. 60, no. 2, p. 437, 2013.
[11]
R. Vullings, V. B. De, and J. W. Bergmans, An adaptive kalman filter for ecg signal enhancement, IEEE Trans Biomed Eng, vol. 58, no. 4, pp. 1094--1103, 2011.
[12]
R. Sameni, M. B. Shamsollahi, C. Jutten, and G. D. Clifford, A nonlinear bayesian filtering framework for ecg denoising, IEEE Transactions on Biomedical Engineering, vol. 54, no. 12, pp. 2172--2185, 2007.
[13]
L. N. Sharma, S. Dandapat, and A. Mahanta, Multiscale principal component analysis to denoise multichannel ecg signals, in Biomedical Engineering Conference, pp. 17--20, 2010.
[14]
U. Satija, B. Ramkumar, and M. S. Manikandan, A unified sparse signal decomposition and reconstruction framework for elimination of muscle artifacts from ecg signal, in IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 779--783, 2016.
[15]
P. Xiong, H. Wang, M. Liu, S. Zhou, Z. Hou, and X. Liu, Ecg signal enhancement based on improved denoising auto-encoder, Engineering Applications of Artificial Intelligence, vol. 52, no. C, pp. 194--202, 2016.
[16]
P. Xiong, H. Wang, M. Liu, F. Lin, Z. Hou, and X. Liu, A stacked contractive denoising auto-encoder for ecg signal denoising., Physiological Measurement, vol. 37, no. 12, pp. 2214--2230, 2016.
[17]
R. Rui and P. Couto, A neural network approach to ecg denoising, Computer Science, 2012.
[18]
O. Yildirim, R. San Tan, U.R. Acharya, An efficient compression of ECG signals using deep convolutional autoencoders, Cognit. Syst. Res., 52 (2018), pp. 198--211
[19]
G. B. Moody and R. G. Mark, The impact of the mit-bih arrhythmia database., IEEE Engineering in Medicine & Biology Magazine, vol. 20, no. 3, pp. 45--50, 2002.
[20]
Moody G.B., Muldrow W.E. and Mark R.G., 1984, A noise stress test for arrhythmia detectors Comput. Cardiol. 11 381--4.
Index Terms
- Muscle Artifacts Cancellation Framework for ECG Signals Combining Convolution Auto-encoder and Average Beat Subtraction
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August 2019
149 pages
ISBN:9781450372244
DOI:10.1145/3354031
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- Graduate School of Library, Information, and Media Studies, University of Tsukuba, Japan: Graduate School of Library, Information, and Media Studies, University of Tsukuba, Japan
- Sichuan University
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Association for Computing Machinery
New York, NY, United States
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Published: 13 August 2019
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ICBIP '19
ICBIP '19: 2019 4th International Conference on Biomedical Signal and Image Processing
August 13 - 15, 2019
Chengdu, China
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