This is an open access article published by the IET under the Creative Commons Attribution-NoDerivs License (http://creativecommons.org/licenses/by-nd/3.0/)
Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal.
References
-
-
1)
-
14. Manikandan, M.S., Dandapat, S.: ‘Wavelet energy based diagnostic distortion measure for ECG’, Biomed. Signal Process. Control, Elsevier, 2007, 2, pp. 80–96 (doi: 10.1016/j.bspc.2007.05.001).
-
2)
-
15. Zhang, D.H.: ‘Wavelet approach for ECG baseline wander correction and noise reduction’. Proc. 27th IEEE EMBS, 2005, pp. 1212–1215.
-
3)
-
22. Karagiannis, A., Constantinou, P.: ‘Noise assisted data processing with empirical mode decomposition in biomedical signals’, IEEE Trans. Inf. Technol. Biomed., 2011, 15, (1), pp. 11–18 (doi: 10.1109/TITB.2010.2091648).
-
4)
-
11. Satija, U., Ramkumar, B., Manikandan, M.S.: ‘A unified sparse signal decomposition and reconstruction framework for elimination of muscle artifacts from ECG signal’. IEEE ICASSP, 2016, pp. 779–783.
-
5)
-
9. Weng, B., Blanco-Velasco, M., Barner, K.E.: ‘ECG denoising based on the empirical mode decomposition’. 28th Int. Conf. IEEE EMBS, 2006, pp. 1–4.
-
6)
-
5. Lahmiri, S.: ‘A comparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains’, IET Healthc. Technol. Lett., 2014, 1, (3), pp. 104–109 (doi: 10.1049/htl.2014.0073).
-
7)
-
10. Sanei, S., Lee, T., Abolghasemi, V.: ‘A new adaptive line enhancer based on singular spectrum analysis’, IEEE Trans. Biomed. Eng., 2012, 59, (2), pp. 428–434 (doi: 10.1109/TBME.2011.2173936).
-
8)
-
2. Ji, T.Y., Lu, Z., Wu, Q.H., et al: ‘Baseline normalization of ECG signals using empirical mode decomposition and mathematical morphology’, Electron. Lett., 2008, 44, (2), pp. 82–83 (doi: 10.1049/el:20082709).
-
9)
-
10. Gao, J.: ‘Denoising nonlinear time series by adaptive filtering and wavelet shrinkage: A comparison’, IEEE Signal Process. Lett., 2010, 17, (3), pp. 237–240 (doi: 10.1109/LSP.2009.2037773).
-
10)
-
2. Roonizi, E.K., Sassi, R.: ‘A signal decomposition model-based Bayesian framework for ECG components separation’, IEEE Trans. Signal Process., 2016, 64, (3), pp. 665–674 (doi: 10.1109/TSP.2015.2489598).
-
11)
-
4. Candes, E., Romberg, J., Tao, T.: ‘Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information’, IEEE Trans. Inf. Theory, 2006, 52, pp. 489–509 (doi: 10.1109/TIT.2005.862083).
-
12)
-
20. Rangayyan, R.M.: ‘Biomedical signal analysis: a case study approach’ (Wiley, 2009).
-
13)
-
24. Moody, G.B., Mark, R.G.: ‘The impact of the MIT-BIH Arrhythmia Database’, IEEE Eng. Med. Biol., 2001, 20, (3), pp. 45–50 (doi: 10.1109/51.932724).
-
14)
-
10. Sameni, R., Shamsollahi, M.B., Jutten, C., et al: ‘A nonlinear Bayesian filtering framework for ECG denoising’, IEEE Trans. Biomed. Eng., 2007, 54, (12), pp. 2172–2185 (doi: 10.1109/TBME.2007.897817).
-
15)
-
3. Vullings, R., De Vries, B., Bergmans, J.W.: ‘An adaptive Kalman filter for ECG signal enhancement’, IEEE Trans. Biomed. Eng., 2011, 58, (4), pp. 1094–1103 (doi: 10.1109/TBME.2010.2099229).
-
16)
-
4. Yadav, S.K., Sinha, R., Bora, P.K.: ‘Electrocardiogram signal denoising using non-local wavelet transform domain filtering’, IET Signal Process., 2015, 9, (1), pp. 88–96 (doi: 10.1049/iet-spr.2014.0005).
-
17)
-
23. McSharry, P.E., Clifford, G.D.: ‘ECGSYN-waveform generator’, .
-
18)
-
12. Kabir, A., Shahnaz, C.: ‘Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains’, Biomed. Sig. Process. Control, 2012, 7, (5), pp. 481–489 (doi: 10.1016/j.bspc.2011.11.003).
-
19)
-
16. Leski, J.M., Henzel, N.: ‘ECG baseline wander and power line interference reduction using nonlinear filter bank’, Signal Process., 2005, 85, pp. 781–793 (doi: 10.1016/j.sigpro.2004.12.001).
-
20)
-
17. Havmoller, R., Carlson, J., Holmqvist, F., et al: ‘Age-related changes in P wave morphology in healthy subjects’, BMC Cardiovasc. Disord., 2007, 7, (1), p. 22 (doi: 10.1186/1471-2261-7-22).
-
21)
-
13. Zivanovic, M., González-Izal, M.: ‘Simultaneous powerline interference and baseline wander removal from ECG and EMG signals by sinusoidal modeling’, Med. Eng. Phys., 2013, 35, (10), pp. 1431–1441 (doi: 10.1016/j.medengphy.2013.03.015).
-
22)
-
6. Hesar, H., Mohebbi, M.: ‘ECG denoising using marginalized particle extended kalman filter with an automatic particle weighting strategy’, IEEE J. Biomed. Health Informatics, 2016, .
-
23)
-
15. Manikandan, M.S., Ramkumar, B.: ‘Straightforward and robust QRS detection algorithm for wearable cardiac monitor’, Healthc. Technol. Lett., 2014, 1, (1), pp. 40–44 (doi: 10.1049/htl.2013.0019).
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