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Efficient Cardiac Signal Enhancement Techniques Based on Variable Step Size and Data Normalized Hybrid Signed Adaptive Algorithms
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DOI: https://doi.org/10.15866/irecos.v11i10.10251
Abstract
In remote health care monitoring, the extraction of high resolution cardiac signals is an important task. For this the cardiac signal (CS) needed to be enhanced. Among various filtering techniques, the adaptive noise cancellation (ANC) is a promising methodology. In adaptive filtering least mean square (LMS) algorithm is the fundamental enhancement algorithm. However, it suffers with slow convergence and weight drift problem in non-stationery environment. In order to improve the performance of ANC this paper proposes to implement an ANC methodology based on variable step size on the normalization of fundamental LMS algorithm for CS enhancement. Based on such strategy this research implements ANC using hybrid algorithm called variable normalized LMS (VNLMS) algorithm. Further, to improve the convergence characteristics, to filter the ability and to minimize computational complexity some versions of VNLMS algorithms are implemented too. Finally, the proposed ANCs are tested using real CS obtained from MIT-BIH data base. The performance evaluation is carried based on signal to noise ratio improvement (SNRI) and excess mean square error (EMSE).
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References
Global Report on Non communicable Diseases, 2014, World Health Organization.
http://dx.doi.org/10.3402/gha.v7.24002
“Health in 2015: from Millennium Development Goals to Sustainable Development Goals,” World Health Organization, 2015.
http://dx.doi.org/10.2471/blt.15.165027
“Heart Disease and Stroke Statistics,” American Heart Association, 2015.
http://dx.doi.org/10.1161/circulationaha.105.171600
“World Health Organization Fact Sheets on Cardiovascular Diseases,” World Health Organization, Fact sheet N317, January, 2015.
http://dx.doi.org/10.1111/jch.12402
Rangaraj M Rangayyan, John Wiley and Sons, “Biomedical Signal Analysis- A case study approach”, USA, 2002.
http://dx.doi.org/10.1002/9781119068129
Kyungtae Kang, Kyung-Joon Park, Jae-Jin Song, Chang-Hwan Yoon, and LuiSha, “A Medical-Grade Wireless Architecture for Remote Electrocardiography”, IEEE Transactions on Information Technology in Biomedicine, vol. 15, no. 2, pp.260-266, March, 2011.
http://dx.doi.org/10.1109/titb.2011.2104365
P Langley, E J. Bowers, and A Murray, “Principal Component Analysis as a Tool for Analyzing Beat-to-Beat Changes in ECG Features: Application to ECG-Derived Respiration”, IEEE Transactions on Biomedical Engineering, vol. 57, no. 4, pp. 821-829, April, 2010.
http://dx.doi.org/10.1109/tbme.2009.2018297
Dewar D. Finlay, Chris D. Nugent, Mark P. Donnellyand Robert L. Lux, “Eigenleads: ECG Leads for Maximizing Information Capture and Improving SNR”, IEEE Transactions on Information Technology in Biomedicine, vol. 14, no. 1, pp. 428-438, January, 2010.
http://dx.doi.org/10.1109/titb.2009.2022933
Santosh K.Y, Rohit S, Prabin K.B., “Electrocardiogram signal denoising using non-local wavelet transform domain filtering”, IET Signal Processing, vol. 9, no. 1, pp. 88-96, 2015.
http://dx.doi.org/10.1049/iet-spr.2014.0005
SalimLahmiri, “Comparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains”, Healthcare Technology Letters, vol. 1, no. 3, pp. 104-109, 2014.
http://dx.doi.org/10.1049/htl.2014.0073
N. V. Thakor and Yi-Sheng Zhu, “Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection”, IEEE Transactions on Biomedical Engineering, vol. 38, no. 8, pp.785-794, August, 1991.
http://dx.doi.org/10.1109/10.83591
R. Vullings, B. De Vries, J.W.M. Bergmans, “An adaptive Kalman filter for ECG signal enhancement”, IEEE Trans. Biomed. Eng., vol. 58, no. 4, pp. 1094-1103, April 2011.
http://dx.doi.org/10.1109/tbme.2010.2099229
Kang-Ming Chang andShing-Hong Liu, “Gaussian Noise Filtering from ECG by Wiener Filter and Ensemble Empirical Mode Decomposition”, Journal of Signal Processing Systems,springer, vol. 64, no. 2, pp. 249-264, 2011.
http://dx.doi.org/10.1007/s11265-009-0447-z
Lukas M, Martin V, Jiri K, and Ivo P, “Adaptive Wavelet Wiener Filtering of ECG signals”, IEEE Trans. Biomed. Eng., vol. 60, no. 2, pp. 437-445, 2013.
http://dx.doi.org/10.1109/tbme.2012.2228482
YuewenTu, Xiuquan Fu, Dingli Li, Chao H, Yawei T, Shuming Ye, And Hang C, “A Novel Method for Automatic Identification of Motion Artifact Beats in ECG Recordings”, Annals of Biomedical Engineering, vol. 40, no. 9, pp. 1917-1928, 2012.
http://dx.doi.org/10.1007/s10439-012-0551-2
Roshan J.M, U. Rajendra A , K.M. Mandana, A.K. Ray, Chandan C, “Application of principal component analysis to ECG signals for automated diagnosis of cardiac health”, Expert Systems with Applications, vol.39, pp. 11792–11800, 2012.
http://dx.doi.org/10.1016/j.eswa.2012.04.072
M.K.Das and S.Ari, “Analysis of ECG signal denoising method based on S-transform”, IRBM, Elsevier, vol. 34, no. 6, pp. 362-367, 2013.
http://dx.doi.org/10.1016/j.irbm.2013.07.012
Jinseok L, David D. McManus, Sneh Merchant and Ki H. Chon, “Automatic Motion and Noise Artifact Detection in Holter ECG Data Using Empirical Mode Decomposition and Statistical Approaches”, IEEE Trans. Biomed. Eng., vol. 59, no. 6, pp. 1499-1506, June 2012.
http://dx.doi.org/10.1109/tbme.2011.2175729
H.Y.Lin, S.Y.Liang, Y.L. Ho, Y.H.Lin, H.P.Ma, “Descrete-Wavelet-transform-based noise removal and feature extraction for ECG signals”, IRBM, Elsevier, vol. 35, no. 6, pp. 351-361, 2014.
http://dx.doi.org/10.1016/j.irbm.2014.10.004
Ke Li, Yun P, Fangjian C, Kwang-Ting C and Ruohong H, “Real-time lossless ECG compression for low-power wearable medical devices based on adaptive region prediction”, Electronics Letters, vol. 50, no. 25, pp. 1904–1906, Dec. 2014.
http://dx.doi.org/10.1049/el.2014.3058
Nauman R, Sheik S.A, M. Salman, T.Zaidi, “An Intelligent Adaptive Filter for Elimination of Power Line Interference from High Resoulution Electrocardiogram”, IEEE Acess, vol. 4, pp. 1676-1688, 2016.
http://dx.doi.org/10.1109/access.2016.2548362
H.Sharma and K.K.Sharma, “Baseline Wander Removal of ECG signals using Hilbert vibration decomposition”, Electronics Letters, vol. 51, no. 6, pp. 447–449, March 2015.
http://dx.doi.org/10.1049/el.2014.4076
Ebadollah K.R and Roberto S, “A Signal Decomposition Model-Based Bayesian Framework for ECG Components Separation”, IEEE Trans.Signal Processing, vol. 64, no. 3, pp. 665-674, Feb. 2016.
http://dx.doi.org/10.1109/tsp.2015.2489598
RikVullings, Bert de Vries, and Jan W. M. Bergmans, “An Adaptive Kalman Filter for ECG Signal Enhancement”, IEEE Transactions on Biomedical Engineering, vol. 58, no. 4, pp.1094-1103, April, 2011.
http://dx.doi.org/10.1109/tbme.2010.2099229
Mohammadreza M, Behboud M, “Introducing new algorithms for realising an FIR filter with less hardware in order to eliminate power line interference from the ECG signal”, IET Signal Processing, vol. 10, no. 7, pp. 709-716, 2016.
http://dx.doi.org/10.1049/iet-spr.2015.0552
C. J. Deepu, and Y. Lian, “A Joint QRS Detection and Data Compression Scheme for Wearable Sensors,” IEEE Transactions on Biomedical Engineering, vol. 62, no. 1, pp. 165-175, January 2015.
http://dx.doi.org/10.1109/tbme.2014.2342879
Mark Ulbrich, Jens Mühlsteff, Daniel Teichmann, Steffen Leonhardt, and Marian Walter, “A Thorax Simulator for Complex Dynamic Bioimpedance Measurements With Textile Electrodes,” IEEE Transactions on Biomedical Circuits And Systems, vol. 9, no. 3, pp. 412-420, June 2015.
http://dx.doi.org/10.1109/tbcas.2014.2337372
J. Behar, J. Oster, Q. Li and G.D. Clifford ”ECG Signal Quality During Arrhythmia and Its Application to False Alarm Reduction” IEEE Transactions on Biomedical Engineering, vol. 60,no.6, pp. 1660-1666, 2013.
http://dx.doi.org/10.1109/tbme.2013.2240452
K. Luo, J. Li and J. Wu ”A Dynamic Compression Scheme for Energy-Efficient Real-Time Wireless Electrocardiogram Biosensors” IEEE Transactions on instrumentation and Measurement, vol. 63,no. 9,pp.2160-2169,2014.
http://dx.doi.org/10.1109/tim.2014.2308063
Shuang Song, Pieter Harpe, “A low voltage chopper stabilized amplifier for feta; ECG monitoring with a 1.41 power efficiency factor”, IEEE Transactions on Biomedical Circuits and Systems(vol.9, no.2, 2015, pp.237-247.)
http://dx.doi.org/10.1109/tbcas.2015.2417124
Murat A.Yokus and Jesse s Jur, “Fabric Based Dry Electrodes for Body Surface Biopotential Recording”, IEEE Transactions on Biomedical Engineering(volume 63, issue 2, Feb 2016, page(s) 423-430).
http://dx.doi.org/10.1109/tbme.2015.2462312
David Da He, Charles G.Sodini, “A 58nW” ECG ASIC with Motion Tolerant Heart Beat Timing Extraction for Wearable Cardiovascular Monitoring”, IEEE Transactions on Biomedical Circuits and Systems(vol. 9, issue 3, June 2015, page 370-376)..
http://dx.doi.org/10.1109/tbcas.2014.2346761
N.V.Helleputte, K.Mario, “A 345µW” multi sensor Biomedical SoC Bio Impedance, 3-Channel ECG, Motion Artifact Reduction and Integrated DSP”, IEEE Journal of Solid state Circuits(vol.50, no.1, 2015, pp.230-244)
http://dx.doi.org/10.1109/jssc.2014.2359962
B. Kang, J. Yoo and P. Park, "Bias-compensated normalized LMS algorithm with noisy input", Electronics Letters, 2013, vol.49 No.8,
http://dx.doi.org/10.1049/el.2013.0246
Hyeonwoo Cho, Sang Woo Kim, “Variable step-size normalized LMS algorithm by approximating correlation matrix of estimation error", Signal Processing, 2010, pp. 2792-2799.
http://dx.doi.org/10.1016/j.sigpro.2010.03.027
J. Chen, J.C. M. Bermudez and C. Richard, “Steady State Performance of Non Negative Least Mean Square Algorithm and its Variants,” IEEE Signal Processing Letters, vol. 21, no.8, pp. 928-932, 2014.
http://dx.doi.org/10.1109/lsp.2014.2320944
J. Chen, C. Richard, J.C. M. Bermudez and P. Honeine, “Nonnegative least mean square algorithm,” IEEE Transaction on Signal Processing, vol. 59, no.11, pp. 5225-5235, 2011.
http://dx.doi.org/10.1109/tsp.2011.2162508
J. Chen, C. Richard, J.C. M. Bermudez and P. Honeine, “Variants of Non Negative Least Mean Square Algorithm and Convergence Analysis,” IEEE Transaction on Signal Processing, vol. 62, no. 15, pp. 3990-4005, 2014.
http://dx.doi.org/10.1109/tsp.2014.2332440
Md. Zulfiquar Ali Bhottoand Ivan V. Bajić,, Constant Modulus Blind Adaptive Beamforming Based on Unscented Kalman Filtering”, IEEE Signal Processing Letters, vol. 22, vo. 4, April 2015 pp.474-478.
http://dx.doi.org/10.1109/lsp.2014.2362932
JinWooYoo, JaeWook Shin, and PooGyeon Park, “An Improved NLMS Algorithm in Sparse Systems Against Noisy Input Signals”, IEEE Transactions on Circuits and Systems—II: Express Briefs, vol. 62, no. 3, March 2015 pp.271-275.
http://dx.doi.org/10.1109/tcsii.2014.2369092
Yun-Mei Shi, Lei Huang, Cheng Qian, and H. C. So,” Shrinkage Linear andWidely Linear Complex-Valued Least Mean Squares Algorithms for Adaptive Beamforming”, IEEE Transactions on Signal Processing, vol. 63, no. 1, January 1, 2015 pp.119-131.
http://dx.doi.org/10.1109/tsp.2014.2367452
Ji Zhao, Xiaofeng Liao Shiyuan Wang, and Chi K. Tse, “Kernel Least Mean Square with Single Feedback”, IEEE Signal Processing Letters, vol. 22, no. 7, July 2015,pp.953-957.
http://dx.doi.org/10.1109/lsp.2014.2377726
Ming Wu, and Jun Yang,” A Step Size Control Method for Deficient Length FBLMS Algorithm”, IEEE Signal Processing Letters, vol. 22, no. 9, September, 2015, pp.1448-1451.
http://dx.doi.org/10.1109/lsp.2014.2382113
RajibLochan Das and MrityunjoyChakraborty,” On Convergence of Proportionate-Type Normalized Least Mean Square Algorithms”, IEEE Transactions on Circuits and Systems—II: Express Briefs, vol. 62, no. 5, May 2015 pp.491-495.
http://dx.doi.org/10.1109/tcsii.2014.2386261
Yun Tan, Zhiqiang He, and BaoyuTian” A Novel Generalization of ModifiedLMS Algorithm to Fractional Order”, IEEE Signal Processing Letters, vol. 22, no. 9, September, 2015, pp.1244-1248.
http://dx.doi.org/10.1109/lsp.2015.2394301
Han-Sol Lee, Seong-Eun Kim, Jae-Woo Lee, and Woo-Jin Song,” A Variable Step-Size Diffusion LMS Algorithm for Distributed Estimation”, IEEE Transactions on Signal Processing, vol. 63, No. 7, April 1, 2015, pp.1808-1820.
http://dx.doi.org/10.1109/tsp.2015.2401533
Md. Zia Ur Rahman, G.V.K.S.Karthik, S.Y.Fathima, A.L-Ekukaille, “An efficient cardiac signal enhancement using time-frequency realization of leaky adaptive noise cancellers for remote health monitoring systems”, Measurements(vol. 46, 2013, pp.3815-3835)
http://dx.doi.org/10.1016/j.measurement.2013.07.009
Md.Zia Ur Rahman, Rafi Ahmed Shaik, D.V.RamaKoti Reddy, “Efficient and simplified Adaptive Noise Cancellers for ECG sensor Based Remote Health Monitoring”, IEEE Sensors Journal(vol.91, no.3, 2012, pp.566-573).
http://dx.doi.org/10.1109/jsen.2011.2111453
Md. Zia Ur Rahman, Rafi AhamedShaik, D.V.Ramakoti Reddy, Efficient sign based normalized adaptive filtering techniques for cancelation of artifacts in ECG signals: Application to wireless biotelemetry”, Signal processing (vol.91, 2011, pp.225-239)
http://dx.doi.org/10.1016/j.sigpro.2010.07.002
Md. Zia Ur Rahman, Rafi AhamedShaik, D.V.RamaKoti Reddy, “Cancellation of Artifacts in the ECG signals using Block adaptive filtering techniques”, Research book entitled “ Software Tools and Algorithms for Biological Systems”, Springer, Advances in Experimental Medicine and Biology, Vol- 696, pp.505-513, 2010.
http://dx.doi.org/10.1007/978-1-4419-7046-6_51
T. Gowri, P. Rajesh, Md.Zia Ur Rahman, D.V.R.K.Reddy, “Efficient ECG Signal Enhancement Techniques using Block Processed Noise Cancellers”, Journal of Medical Imaging and Health Informatics, Vol-6, No-3, pp.739-745, 2016.
http://dx.doi.org/10.1166/jmihi.2016.1744
Md Zia Ur Rahman, ShafiShahsavarMirza,“Process Techniques For Human Thoracic Electrical Bio-Impedance Signal In Remote Healthcare Systems,” IET Healthcare Technology Letters, pp. 1–5, 2016.
http://dx.doi.org/10.1049/htl.2015.0061
Venkatasrikanth, Md Zia Ur Rahman, “Efficient ECG Signal Conditioning Techniques using Variable Step Size LMF Algorithms,” International Journal of Engineering and Technology, Vol. 8 , No 2 , pp.660-668, 2016.
http://dx.doi.org/10.1109/wispnet.2016.7566392
Md Zia Ur Rahman, Asiya Sultana, BurraVenkataSrikanth, “Design and Implementation of Efficient Low Complexity Biomedical Artifact Canceller for Nano Devices,” Leonardo Electronic Journal of Practices and Technologies, Issue 28, pp. 197-210, 2016.
http://dx.doi.org/10.17485/ijst/2016/v9i37/92836
I. Soumya, Md. Zia Ur Rahman, D.V.R.K.Reddy A. Lay-Ekuakille, “Efficient Block Processing of Long duration Biotelemetric Brain data for Health Care Monitoring”, Review of Scientific Instruments, Vol-86, pp.035003-1-10, 2015.
http://dx.doi.org/10.1063/1.4913658
G.V.S.Karthik, S. Y. Fathima, Md. Zia Ur Rahman, Sk.RafiAhamed, A. Lay-Ekuakille, “Efficient Signal conditioning techniques for Brain activity in Remote Health Monitoring Network”, IEEE Sensors Journal, Vol-13, No-9, pp. 3276-3283, 2013.
http://dx.doi.org/10.1109/jsen.2013.2271042
Physio Net, TheMassachusetts Institute of Technology - Boston's Beth Israel Hospital (MIT-BIH) Arrhythmia Database,Available: http://www.physionet.org/physiobank/database/mitdb/ (Online).
http://dx.doi.org/10.1109/cic.1990.144205
The MIT-BIH Normal Sinus Rhythm DatabseAvailable at http://www.physionet.org/physiobank/database/nsrdb/(Online).
http://dx.doi.org/10.5120/7845-0912
Mark RG and Moody GB, “The Impact of the MIT Arrhythmia Database”, IEEE Engineering in Medicine and Biology, vol. 20,no. 3, June, 2001, pp. 45-50.
http://dx.doi.org/10.1109/51.932724
Moody GB, Mark RG. The MIT-BIH Arrhythmia Database on CD-ROM and software for usewith it. Computers in Cardiology, (17:185-188, 1990).
http://dx.doi.org/10.1109/cic.1990.144205
Beydoun, A., Sharafeddine, Y., Alaeddine, H., Rachini, A., Khalil, F., Beydoun, B., Optimization and Implementation of Acoustic Echo Canceller Based on LMS Algorithm Using FPGA, (2014) International Journal on Communications Antenna and Propagation (IRECAP), 4 (6), pp. 234-243.
http://dx.doi.org/10.15866/irecap.v4i6.4792
Kothandaraman, M., Pachaiyappan, A., Wavelet Based Adaptive Filtering Algorithms for Acoustic Noise Cancellation, (2014) International Review on Computers and Software (IRECOS), 9 (10), pp. 1675-1681.
http://dx.doi.org/10.15866/irecos.v9i10.4308
Adewole, A., Tzoneva, R., Distribution Network Fault Detection and Diagnosis Using Wavelet Energy Spectrum Entropy and Neural Networks, (2014) International Review of Electrical Engineering (IREE), 9 (1), pp. 165-173.
Bazi, S., Nait Said, M., Extreme Learning Machines and Particle Swarm Optimization for Induction Motor Faults Detection and Classification, (2015) International Review of Electrical Engineering (IREE), 10 (4), pp. 501-509.
http://dx.doi.org/10.15866/iree.v10i4.7048
Arıkan, Ç., Özdemir, M., Classification of Power Quality Disturbances Using Support Vector Machines and Comparing Classification Performance, (2013) International Review of Electrical Engineering (IREE), 8 (2), pp. 776-784.
Jagadeesh, B., Kumar, P., Reddy, P., Fuzzy Inference System Based Robust Digital Image Watermarking in DWT-DCT Domain Using Human Visual System, (2016) International Review on Modelling and Simulations (IREMOS), 9 (4), pp. 265-270.
http://dx.doi.org/10.15866/iremos.v9i4.8534
El Yousfi Alaoui, M., Jilbab, A., El Hani, S., A New Approach to FPGA-Implementation of DWT Applied to Real Time Denoising of Vibration Signals Related to Bearing Defects, (2016) International Review on Modelling and Simulations (IREMOS), 9 (3), pp. 181-190.
http://dx.doi.org/10.15866/iremos.v9i3.8753
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