Particle Swarm Intelligence Based Univariate Parameter Tuning of Recursive Least Square Algorithm for Optimal Heart Sound Signal Filtering

Heart Sound Signal (HSS) is considered as one of the important bio-signals. It carries vitalinformation about the heart functions. For bio-acoustic observations, the HSS is diagnosed andrecorded with auscultatory procedures. During auscultation, the noisy components gets addedalong with the reading. The physician’s individual diagnostic experience, ecological noise andthe intersection of heart and lung sound signal (LSS) are considered as the major noisycomponents in HSS diagnosis. Suppression of LSS from the HSS is a challenging task. Due toits quasi stationary nature, adaptive filtering techniques are used for the noise removal. In thispaper, Recursive Least Square (RLS) adaptive algorithm is proposed to obtain the HSS fromthe noisy mixture. Faster convergence is a benefit in selecting RLS algorithm over otheradaptive algorithms. The forgetting factor is one of the important parameters of RLS whichdefines the convergence. The RLS performance is improved by choosing an optimal forgettingfactor. A Particle Swarm Optimization (PSO) based search algorithms are deployed foroptimization. To enhance the implementation time, a Dynamic Neighbourhood LearningParticle Swarm Optimizer (DNL-PSO) is analysed. In DNL-PSO, each particle studies from itsknowledge in dynamically varying neighbourhood that prevents early convergence. The normalHSS with different LSS interference is taken to assess the RLS filter performance. In this paper,the RLS algorithm performance is compared with Least Mean Square (LMS) adaptivealgorithms. Various metrics are used to compare the performance of both RLS and optimizationalgorithms.

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Sasaoka, N., Shimada, K., Sonobe, S., Itoh, Y., and Fujii, K., “Speech enhancement based on adaptive filter with variable step size for wideband and periodic noise”, In Circuits and Systems, MWSCAS'09, 52nd IEEE International Midwest Symposium on IEEE, 648-652, (2009).
Ahmad, M. S., Kukrer, O., and Hocanin, A., “A 2-D recursive inverse adaptive algorithm”, Signal, Image and Video Processing, 7(2): 221-226, (2013).
Ari, S., Hembram, K., and Saha, G., “Detection of cardiac abnormality from PCG signal using LMS based least square SVM classifier”, Expert Systems with Applications, 37(12): 8019-26, (2010).
Sun, S., Wang, H., Jiang, Z., Fang, Y., and Tao, T., “Segmentation-based heart sound feature extraction combined with classifier models for a VSD diagnosis system”, Expert Systems with Applications, 41(4): 1769-80, (2014).
Debbal, S. M., and Tani, A. M., “Heart sounds analysis and murmurs”, International Journal of Medical Engineering and Informatics, 8(1): 49-62, (2016).
Evans, W., “The use of the phonocardiograph in clinical medicine”, The Lancet, 257(6664): 1083-5, (1951).
Kao, W. C., and Wei, C. C., “Automatic phonocardiograph signal analysis for detecting heart valve disorders”, Expert Systems with Applications, 38(6): 6458-68, (2011).
Chowdhury, S. K., and Majumder, A. K., “Digital spectrum analysis of respiratory sound”, IEEE Transactions on Biomedical Engineering, BME-28(11): 784-8, (1981).
Welsby, P. D., Parry, G., and Smith, D., “The stethoscope: some preliminary investigations”, Postgraduate Medical Journal, 79(938): 695-8, (2003).
Iyer, V. K., Ramamoorthy, P. A., Fan, H., and Ploysongsang, Y., “Reduction of heart sounds from lung sounds by adaptive filtering”, IEEE Transactions on Biomedical Engineering, BME-33(12): 1141-48, (1986).
Widrow, B., Glover, J. R., McCool, J. M., Kaunitz, J., Williams, C. S., Hearn, R. H., Zeidler, J. R., Dong, J. E., and Goodlin, R. C., “Adaptive noise cancelling: Principles and applications”, Proceedings of the IEEE, 63(12): 1692-716, (1975).
Bernard, W., and Hoff, M. E., “Adaptive switching circuits, Technical report”, Stanford Univ CA Stanford Electronics Labs, 96-104, (1960).
Haykin, S.S., Adaptive filter theory, 4 th ed., Pearson Education, India, (2008).
Diniz, P. S., Adaptive Filtering: Algorithms and Practical Implementation, 2 nd ed., Springer, New York, USA, (2008).
Albert, T. R., Abusalem, H., and Juniper, M. D., “Experimental results: Detection and tracking of low SNR sinusoids using real-time LMS and RLS lattice adaptive line enhancers”, In Acoustics, Speech, and Signal Processing, ICASSP-91., International Conference IEEE, 1857-1860, (1991).
Guda, M., Gasser, S., and El Mahallawy, M. S., “Matlab Simulation Comparison for Different Adaptive Noise Cancelation Algorithm”, The International Conference on Digital Information, Networking, and Wireless Communications (Dinwc2014), Czech Republic, 68-73, (2014).
Özbay, Y., and Kavsaoğlu, A. R., “An Optimum Algorithm for Adaptive Filtering on Acoustic Echo Cancellation Using TMS320C6713 DSP”, Digital Signal Processing, 20(1): 133-148, (2010).
Dixit, S., and Nagaria, D., “LMS adaptive filters for Noise Cancellation: A Review”, International Journal of Electrical and Computer Engineering (IJECE), 7(5): 2520-2529, (2017).
Mekala, A. M., and Chandrasekaran, S., “Heart Sound Interference Cancellation from Lung Sound Using Dynamic Neighbourhood Learning-Particle Swarm Optimizer Based Optimal Recursive Least Square Algorithm”, International Journal of Biomedical Engineering and Technology, (in press).
Yip, L., and Zhang, Y. T., “Reduction of heart sounds from lung sound recordings by automated gain control and adaptive filtering techniques”. In Engineering in Medicine and Biology Society, Proceedings of the 23rd Annual International Conference of the IEEE, 2154-6, (2001).
Hossain, I., and Moussavi, Z., “An overview of heart-noise reduction of lung sound using wavelet transform based filter”, In Proc. Ann. Int. Conf. IEEE EMBS. Cancún, México, 458-461, (2003).
Ayari, F., Ksouri, M., and Alouani, A. T., “Lung sound extraction from mixed lung and heart sounds FASTICA algorithm”, In Electro Technical Conference (MELECON), 339-342, (2012).
Ghaderi, F., Mohseni, H. R., and Sanei, S., “Localizing heart sounds in respiratory signals using singular spectrum analysis”, IEEE Transactions on Biomedical Engineering, 58(12): 3360-67, (2011).
Nersisson, R., and Noel, M. M., “Hybrid Nelder-Mead search based optimal Least Mean Square algorithms for heart and lung sound separation”, Engineering Science and Technology, an İnternational Journal, 20 (3): 1054-65, (2017).
Gnitecki, J., and Moussavi, Z. M., “Separating heart sounds from lung sounds”, IEEE Engineering in Medicine and Biology Magazine, 26(1): 20, (2007).
Pourazad, M. T., Moussavi, Z., Farahmand, F., and Ward, R. K., “Heart sounds separation from lung sounds using independent component analysis”, In Engineering in Medicine and Biology Society, IEEE-EMBS 2005. 27th Annual International Conference, 2736-39, (2006).
Ramos, J. P., Carvalho, P., Paiva, R.. P., and Henriques, J., “Modulation filtering for noise detection in heart sound signals”, In Engineering in Medicine and Biology Society, EMBC, Annual International Conference of IEEE, 6013-16 (2011).
Xiu-min, Z., and Gui-tao, C., “A novel de-noising method for heart sound signal using improved thresholding function in wavelet domain”, In Bio Medical Information Engineering, International Conference on Future, 65-68, (2009).
Tang, H., Li, T., Park, Y., and Qiu, T., “Separation of heart sound signal from noise in joint cycle frequency–time–frequency domains based on fuzzy detection”, IEEE Transactions on Biomedical Engineering, 57(10): 2438-47, (2010).
Canadas-Quesada, F. J., Ruiz-Reyes, N., Carabias-Orti, J., Vera-Candeas, P., and Fuertes-Garcia, J., “A non-negative matrix factorization approach based on spectro-temporal clustering to extract heart sounds”, Applied Acoustics, 125(1): 7-19, (2017).
Lu, Y. S., Liu, W. H., and Qin, G. X., “Removal of the heart sound noise from the breath sound”, In Engineering in Medicine and Biology Society, Proceedings of the Annual International Conference of the IEEE, 175-176, (1988).
Nersisson, R., and Noel, M. M., “Heart sound and lung sound separation algorithms: a review”,Journal of Medical Engineering & Technology, 41(1): 13-21, (2017a).
Li-Ping, Z., Huan-Jun, Y., and Shang-Xu, H., “Optimal choice of parameters for particle swarm optimization”, Journal of Zhejiang University-Science A, 6(6): 528-534, (2005).
Leung, Y., Gao, Y., and Xu, Z. B., “Degree of population diversity-a perspective on premature convergence in genetic algorithms and its markov chain analysis”, IEEE Transactions on Neural Networks, 8(5): 1165-1176, (1997).
Gao, W. F., Liu, S. Y., and Huang, L. L., “Particle swarm optimization with chaotic opposition based population initialization and stochastic search technique”, Communications in Nonlinear Science and Numerical Simulation, 17(11): 4316-4327, (2012).
Nasir, M., Das, S., Maity, D., Sengupta, S., Halder, U., and Suganthan, P. N., “A dynamic neighbourhood learning based particle swarm optimizer for global numerical optimization”, Information Sciences, 209: 16-36, (2012).
Dr. Prodigious Video Library (2016), Online. https://doctorprodigious.wordpress.com/.
Gavriely, N., Nissan, M., Rubin, A. H., and Cugell, D. W, “Spectral characteristics of chest wall breath sounds in normal subjects”, Thorax, 50(12): 1292-1300, (1995).
Pasterkamp, H., Kraman, S. S., and Wodicka, G. R., “Respiratory sounds: advances beyond the stethoscope”, American Journal of Respiratory and Critical Care Medicine, 156(3): 974-87, (1997).
Douglas, S. C., and Rupp, M., Digital Signal Processing Handbook- Convergence Issues in the LMS Adaptive Filter, 2 nd ed., CRC Press, Atlanta, USA, (1999).
Nakarajan, V. M., and Noel, M. M., “Galactic Swarm Optimization: A new global optimization metaheuristic inspired by galactic motion”, Applied Soft Computing, 38: 771-787, (2016).
Eberhart, R. C., and Shi, Y., “Comparison between Genetic Algorithms and Particle Swarm Optimization”, International Conference on Evolutionary Programming, Springer, 611-616, (1998).
Amali, G. B. D., and Dinakaran, M., “A New Quantum Tunneling Particle Swarm Optimization Algorithm for Training Feedforward Neural Networks”, International Journal of Intelligent Systems and Applications (IJISA), 10(11): 64-75, (2018).
Zielinski, K., Peters, D., and Laur, R., “Run time analysis regarding stopping criteria for differential evolution and particle swarm optimization”, in: Proc. of the 1st International Conference on Experiments/Process/System Modelling/Simulation/Optimization, Athens, Greece, (2005).