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

Year 2019, , 928 - 943, 01.09.2019

Mary Mekala Antony Dhas , Srimathi Chandrasekaran

https://doi.org/10.35378/gujs.501114

Cited By: 1

Abstract

Heart Sound Signal (HSS) is considered as one of the important bio-signals. It carries vital information about the heart functions. For bio-acoustic observations, the HSS is diagnosed and recorded with auscultatory procedures. During auscultation, the noisy components gets added along with the reading. The physician’s individual diagnostic experience, ecological noise and the intersection of heart and lung sound signal (LSS) are considered as the major noisy components in HSS diagnosis. Suppression of LSS from the HSS is a challenging task. Due to its quasi stationary nature, adaptive filtering techniques are used for the noise removal. In this paper, Recursive Least Square (RLS) adaptive algorithm is proposed to obtain the HSS from the noisy mixture. Faster convergence is a benefit in selecting RLS algorithm over other adaptive algorithms. The forgetting factor is one of the important parameters of RLS which defines the convergence. The RLS performance is improved by choosing an optimal forgetting factor. A Particle Swarm Optimization (PSO) based search algorithms are deployed for optimization. To enhance the implementation time, a Dynamic Neighbourhood Learning Particle Swarm Optimizer (DNL-PSO) is analysed. In DNL-PSO, each particle studies from its knowledge in dynamically varying neighbourhood that prevents early convergence. The normal HSS 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) adaptive algorithms. Various metrics are used to compare the performance of both RLS and optimization algorithms.

Keywords

Adaptive noise cancellation, Dynamic Neighbourhood Learning based PSO, Forgetting factor, Heart sound signals, Recursive Least Square

References

• Debbal, S. M., Tani, A. M., “Heart sounds analysis and murmurs”, International Journal of Medical Engineering and Informatics, 8 (1):49-62, (2016).
• Ari, S., Hembram, K., 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.,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).
• Evans W. “The use of the phonocardiograph in clinical medicine”, The Lancet, 257(6664):1083-5, (1951).
• Kao, W.C., Wei, C.C.,” Automatic phonocardiograph signal analysis for detecting heart valve disorders", Expert systems with applications, 38(6):6458-68, (2011).
• Chowdhury, S.K., Majumder, A.K., “Digital spectrum analysis of respiratory sound”, IEEE Transactions on Biomedical Engineering, 11:784-8, (1981).
• Welsby, P.D., Parry, G., Smith, D., “The stethoscope: some preliminary investigations”, Postgraduate medical journal, 79(938): 695-8, (2003).
• Mary Mekala A and Srimathi Chandrasekaran (in press),” 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.
• Yip L, Zhang YT.,” Reduction of heart sounds from lung sound recordings by automated gain control and adaptive filtering techniques”. In Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE, 3: 2154-6, (2001).
• Hossain I, 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, 3:458-461, (2003).
• Ayari F, Ksouri M, Alouani AT.,” Lung sound extraction from mixed lung and heart sounds FASTICA algorithm”, In Electro Technical Conference (MELECON), 339-342, (2012).
• Ghaderi, F., Mohseni, H.R., Sanei, S.,” Localizing heart sounds in respiratory signals using singular spectrum analysis”, IEEE Transactions on Biomedical Engineering, 58(12):3360-67, (2011).
• Nersisson R, Noel MM.,” Hybrid Nelder-Mead search based optimal Least Mean Square algorithms for heart and lung sound separation”, Engineering Science and Technology, an international journal, 20 (3):1054-65, (2017).
• Gnitecki, J., Moussavi, Z.M., “Separating heart sounds from lung sounds-Accurate Diagnosis of Respiratory Disease Depends on Understanding Noises”, IEEE Engineering in medicine and biology magazine, 26(1):20, (2007).
• Pourazad MT, Moussavi Z, Farahmand F, Ward RK.,” Heart sounds separation from lung sounds using independent component analysis”, In Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference, 2736-2739, (2006).
• Ramos JP, Carvalho P, Paiva RP, Henriques J.,” Modulation filtering for noise detection in heart sound signals”, In Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of IEEE, 6013-6016 (2011).
• Xiu-min Z, 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 2009, 65-68, (2009).
• Tang, H., Li, T., Park, Y., 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., Fuertes-Garcia, J.,” A non-negative matrix factorization approach based on spectro-temporal clustering to extract heart sounds”, Applied Acoustics, 125:7-19,(2017).
• Lu YS, Liu WH, Qin GX., “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., 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., & 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., & 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., & 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., Suganthan, P.N.,” A dynamic neighbourhood learning based particle swarm optimizer for global numerical optimization”, Information Sciences, 209:16-36, (2012)
• Gavriely, N., Nissan, M., Rubin, A.H., Cugell, D.W, “Spectral characteristics of chest wall breath sounds in normal subjects”, Thorax, 50(12):1292-1300, (1995).
• Pasterkamp, H., Kraman, S.S., Wodicka, G.R.,” Respiratory sounds: advances beyond the stethoscope”, American journal of respiratory and critical care medicine, 156(3): 974-87, (1997).
• Widrow B, Glover JR, McCool JM, Kaunitz J, Williams CS, Hearn RH, Zeidler JR, Dong JE, Goodlin RC., “Adaptive noise cancelling: Principles and applications”, Proceedings of the IEEE, 63(12):1692-716, (1975).