Abstract
Biomedical signals produced by mobile sensors usually carry various noises. This poses great challenges for the subsequent signal processing and disease analysis. Thus, noise removal becomes an important step of signal processing. This research proposes a noise reduction algorithm which can be applied to noisy ECG (electrocardiogram) signals to obtain a higher signal-to-noise ratio (SNR) for further processing. The proposed algorithm utilises wavelet transform and dynamic thresholding to reduce specific types of noise embedded in raw ECG signals. To prove the efficiency of the proposed algorithm, we employ a half-hour-long real ECG signal and add different types of noise for the evaluation of the proposed algorithm. We also compare the results obtained using different families of wavelets and different decomposition levels. The experimental results show that the proposed algorithm is able to produce a higher SNR in the output signal than that in the raw test signals.
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References
Hall, J.E., Guyton, A.C.: Textbook of medical physiology. J. Chem. Inf. Model. 53, 160 (2011)
Moody, G.B., Mark, R.G.: The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20, 45–50 (2001). doi:10.1109/51.932724
Thalkar, S.: Various techniques for removal of power line interference from ECG signal. Int. J. Sci. Eng. Res. 4, 12–23. http://www.ijser.org (2013). Accessed 15 April 2016
Friesen, G.M., Jannett, T.C., Jadallah, M.A., Yates, S.L., Quint, S.R., Nagle, H.T.: A comparison of the noise sensitivity of nine QRS detection algorithms. IEEE Trans. Biomed. Eng. 37, 85–98 (1990). doi:10.1109/10.43620
Kaur, M., Seema, B.S.: Comparison of different approaches for removal of baseline wander from ECG signal. In: Proceedings of the International Conference & Workshop on Emerging Trends in Technology—ICWET ’11, p. 1290 (2011). doi:10.1145/1980022.1980307
Raphisak, P., Schuckers, S.C., Curry, A.D.J.: An algorithm for EMG noise detection in large ECG data. Comput. Cardiol. 2004(1), 369–372 (2004). doi:10.1109/CIC.2004.1442949
Bai, Y.-W., Chu, W.-Y., Chen, C.-Y., Lee, Y.-T., Tsai, Y.-C., Tsai, C.-H.: Adjustable 60 Hz noise reduction by a notch filter for ECG signals. In: 21st IEEE Instrumentation and Measurement Technology Conference, pp. 1706–1711 (2004). doi:10.1109/IMTC.2004.1351410
Rani, S., Kaur, A., Ubhi, J.S.: Comparative study of FIR and IIR filters for the removal of Baseline noises from ECG signal. Int. J. Comput. Sci. Inf. Technol. 2, 1105–1108 (2011)
An-dong, W., Lan, L., Qin, W.: An adaptive morphologic filter applied to ECG de-noising and extraction of R peak at real-time. In: AASRI Conference on Computational Intelligence and Bioinformatics, pp. 474–479 (2012). doi:10.1016/j.aasri.2012.06.074
Tadejko, P. Rakowski, W.: Mathematical morphology based ECG feature extraction for the purpose of heartbeat classification. In: 6th International Conference on Computer Information Systems and Industrial Management Applications, CISIM 2007, pp. 322–327 (2007). doi:10.1109/CISIM.2007.47
Pan, J., Tompkins, W.J.: A Real-Yime QRS detection algorithm. IEEE Trans. Bio-Med. Eng. Biomed. Eng. BME-32 230–236 (1985). doi:10.1109/TBME.1985.325532
Tamil, E.M., Kamarudin, N.H., Salleh, R., Idris, M.Y.I., Noor, M., Tamil, A.M.: Heartbeat Electrocardiogram (ECG) signal feature extraction using Discrete Wavelet Transforms (DWT). In: CSPA, 2008, pp. 1112–1117 (2008)
Chouhan, V.S., Mehta, S.S.: Detection of QRS complexes in 12-lead ECG using adaptive quantized threshold. Int. J. Comput. Sci. Netw. Secur. 8, 155–163 (2008)
Kadambe, S., Murray, R., Paye, G.: Boudreaux-Bartels Wavelet transform-based QRS complex detector. IEEE Trans. Biomed. Eng. 46, 838–848 (1999). doi:10.1109/10.771194
Kabir, M.A., Shahnaz, C.: Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains. Biomed. Signal Process. Control 7, 481–489 (2012). doi:10.1016/j.bspc.2011.11.003
Garg, G., Gupta, S., Singh, V., Gupta, J.R.P., Mittal, A.P.: Identification of optimal wavelet-based algorithm for removal of power line interferences in ECG signals. In: India International Conference on Power Electronics, IICPE 2010 (2011). doi:10.1109/IICPE.2011.5728090
Khan, M., Aslam, F., Zaidi, T., Khan, S.A.: Wavelet based ECG denoising using signal-noise residue method. In: 5th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2011, pp. 1–4. IEEE (2011). doi:10.1109/icbbe.2011.5780263
Li, Z., Ni, J., Gu, X.: A denoising framework for ECG signal preprocessing. In: 6th International Conference on Internet Computing for Science and Engineering, ICICSE 2012, pp. 176–179. IEEE (2012). doi:10.1109/ICICSE.2012.59
Tiwari, R., Dubey, R.: Analysis of different denoising techniques of ECG signals. Int. J. Emerg. Technol. Adv. Eng. 4, 2–6 (2014)
Sadhukhan, D., Mitra, M.: ECG noise reduction using Fourier coefficient suppression. In: Proceedings of the 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC), pp. 142–146. IEEE (2014). doi:10.1109/CIEC.2014.6959066
Bortolan, G., Christov, I., Simova, I., Dotsinsky, I.: Noise processing in exercise ECG stress test for the analysis and the clinical characterization of QRS and T wave alternans. Biomed. Signal Process. Control 18, 378–385 (2015). doi:10.1016/j.bspc.2015.02.003
Agostinelli, A., Giuliani, C., Burattini, L.: Extracting a clean ECG from a noisy recording: a new method based on segmented-beat modulation. Comput. Cardiol. 2014(41), 49–52 (2014)
Kuzilek, J., Kremen, V., Soucek, F., Lhotska, L.: Independent component analysis and decision trees for ECG holter recording de-noising. PLoS ONE 9, e98450 (2014). doi:10.1371/journal.pone.0098450
Liu, G., Luan, Y.: An adaptive integrated algorithm for noninvasive fetal ECG separation and noise reduction based on ICA-EEMD-WS. Med. Biol. Eng. Comput. 53, 1113–1127 (2015). doi:10.1007/s11517-015-1389-1
Nason, G., Silverman, B.: The stationary wavelet transform and some statistical applications. Wavelets Stat. 281–299 (1995). doi:10.1007/978-1-4612-2544-7_17
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This research is supported by European Union (EU) sponsored (Erasmus Mundus) cLINK (Centre of excellence for Learning, Innovation, Networking and Knowledge) project (EU Grant No. 2645).
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Pandit, D., Zhang, L., Liu, C., Aslam, N., Chattopadhyay, S., Lim, C.P. (2017). Noise Reduction in ECG Signals Using Wavelet Transform and Dynamic Thresholding. In: Bhatti, A., Lee, K., Garmestani, H., Lim, C. (eds) Emerging Trends in Neuro Engineering and Neural Computation. Series in BioEngineering. Springer, Singapore. https://doi.org/10.1007/978-981-10-3957-7_10
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DOI: https://doi.org/10.1007/978-981-10-3957-7_10
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