Total views : 106
Improved Classification of Phonocardiography Signal Using Optimised Feature Selection
Objectives: To propose hybrid feature selection technique based on Particle Swarm Optimization and Genetic Algorithm for phonocardiography. Methods/Statistical Analysis: The system estimated using heart sounds corresponding to different heart conditions like 320 signals out of which 150-normal, 70-Mitral Valve Prolapse, 50-Ventricular Septal Defect and 50-Pulmonary Stenosis. Features are extracted using DWT. Finding: A phonocardiographic signal reflects the health status of the heart. Generally there exists two heart sounds, but further sounds indicate disease. Phonocardiography is non-invasive, low-cost and accurate method to detect heart disease. This work proposes a framework to extract information from phonocardiography signal to classify whether it is proper or improper. Discrete Wavelet Transform method is implemented to extract features followed by Singular value decomposition for feature selection. Applications/Improvements: pplications/Improvements: An experimental result gives the improvement of the proposed method by increasing the efficacy of the corresponding feature selection technique.
Binary Particle Swarm Optimization, Feature Selection, Phonocardiography, DWT, GA Introduction
- Javed F, Venkatachalam PA. A signal processing module for the analysis of heart sounds and heart murmurs. Journal of Physics: Conference Series 2006 April;34(1):1098. Crossref.
- Varady P. Wavelet-based adaptive denoising of phonocardiographic records. Proceedings of the 23rd AnnualInternational Conference of the IEEE Engineering in Medicine and Biology Society. Turkey. 2001. 2 p.1846–9.Crossref.
- Singh M, Cheema A. Heart Sounds Classification using Feature Extraction of Phonocardiography Signal.
- International Journal of Computer Applications.2013;77(4):13–17.
- Ahlström C. (2006). Processing of the Phonocardiographic Signal: Methods for the intelligent stethoscope [PhD thesis].Sweden: Linkoping University; May 2006.
- Cheema A, Singh M. Steps Involved in Heart Sound Analysis-A Review of Existing Trends. International Journal of Engineering Trends and Technology. 2013 July; 4(7):2921–5.
- Reynolds DA, Rose RC. Robust text-independent speaker identification using Gaussian mixture speaker models.IEEE Transactions on Speech and Audio Processing.1995;3(1):72–83. Crossref.
- Badghare SS. Analysis of (Doctoral dissertation, National Institute of Technology, Rourkela).
- Pahuja H, Chhabra J, Khokhar A. Ant colony optimizationbased selected features for Text-independent speaker verification.
- Balasubramaniam D, Nedumaran D. Design and Development of Digital Signal Processor based Phonocardiogram System. IEEE International Conference on Signal Acquisition and Processing, Bangalore.2010. p.366–70. Crossref.
- Quiceno-Manrique AF, Godino-Llorente JI, BlancoVelasco M, Castellanos-Dominguez G. (2010). Selection of dynamic features based on time–frequency representations for heart murmur detection from phonocardiographic signals. Annals of biomedical engineering, 2010; 38(1):118–37.Crossref. PMid:19921435
- Ahlstrom C, Hult P, Rask P, Karlsson JE, Nylander E, Dahlström U, Ask P. Feature extraction for systolic heart murmur classification. Annals of biomedical engineering, 2016; 34(11): 1666–77. Crossref. PMid:17019618
- Chourasia VS, Tiwari AK, Gangopadhyay R. A novel approach for PCG signals processing to make possible fetal heart rate evaluations. Digital Signal Processing, 2014; 30: 165–83. Crossref.
- Cesarelli M, Ruffo M., Romano M, Bifulco P. Simulation of foetal phonocardiographic recordings for testing of FHR extraction algorithms. Computer methods and programs in biomedicine. 2012; 107(3): 513–23. Crossref.PMid:22178069
- Gradolewski D, Redlarski G. Wavelet-based denoising method for real PCG signal recorded by mobile devices in noisy environment. Computers in biology and medicine, 2014; 52: 119–29. Crossref. PMid:25038586
- Premalatha K. Hybrid PSO and GA for Global Maximization. International Journal of Open Problems Computational Mathematics.2009 Dec; 2(4): 597–608.
- Medha VW. Image Registration Techniques: An overview.International Journal of Signal Processing, Image Processing and Pattern Recognition. 2009 Sep; 2(3):11–28.
- Fahd MA Mohsen. A new Optimization-Based Image Segmentation method By Particle Swarm Optimization, International Journal of Advanced Computer Science Application, Special Issue on Image Processing and Analysis.11–18.
- Kai-Shiuan Shih. Observer-Based Adaptive Fuzzy Robust Controller with Self-Adjusted Membership Functions For A Class of Uncertain Mimo Nonlinear Systems: A PSO-SA Method, International Journal of Innovative Computing, Information and Control. 2012 Feb; 8(2):1419–37.
- Van Den Bergh, F. An analysis of particle swarm optimizers [Doctoral dissertation]. Pretoria:University of Pretoria.2006.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.