A biomedical system based on artificial neural network and principal component analysis for diagnosis of the heart valve diseases

Journal of Medical Systems

Volumn 36, Issue 1, 2012, Pages 61-72

  Uǧuz, Harun ^a  

^a SELCUK UNIVERSITY   (Turkey)

Author keywords

Artificial neural network; Burg autoregressive method; Discrete fourier transform; Heart sound; Principal component analysis

Indexed keywords

ADULT; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; DECISION SUPPORT SYSTEM; FEMALE; FOURIER TRANSFORMATION; HEART SOUND; HUMAN; MAJOR CLINICAL STUDY; MALE; MEDICAL INFORMATION SYSTEM; MITRAL VALVE STENOSIS; PRINCIPAL COMPONENT ANALYSIS; PULMONARY VALVE STENOSIS; STETHOSCOPE; ADOLESCENT; AGED; CHILD; COMPUTER INTERFACE; FOURIER ANALYSIS; HEART AUSCULTATION; INSTRUMENTATION; METHODOLOGY; MIDDLE AGED; PRESCHOOL CHILD; SIGNAL PROCESSING; VALVULAR HEART DISEASE;

ADOLESCENT; ADULT; AGED; CHILD; CHILD, PRESCHOOL; DECISION SUPPORT SYSTEMS, CLINICAL; DECISION SUPPORT TECHNIQUES; FEMALE; FOURIER ANALYSIS; HEART AUSCULTATION; HEART VALVE DISEASES; HUMANS; MALE; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); PRINCIPAL COMPONENT ANALYSIS; SIGNAL PROCESSING, COMPUTER-ASSISTED; USER-COMPUTER INTERFACE; YOUNG ADULT;

EID: 84860250399     PISSN: 01485598     EISSN: 1573689X     Source Type: Journal    
DOI: 10.1007/s10916-010-9446-7     Document Type: Article

References (39)

1. Jiang, Z., and Choi, S., A cardiac sound characteristic waveform method for in-home heart disorder monitoring with electric stethoscope. Expert Syst Appl 31(2):286-298, 2006.
2. Ahlström, C., Processing of the Phonocardiographic Signal-Methods for the intelligent stethoscope, Ms Thesis, Linköping University, Institue of Techonology, Linköping, Sweden, 2006.
3. Kara, S., Classification of mitral stenosis from Doppler signals using short time Fourier transform and artificial neural Networks. Expert Syst Appl 33:468-475, 2007. (Pubitemid 46150049)
4. Güraksin, G.E., Ergün, U., Classification of the Heart Sounds via Artificial Neural Network, International Symposium on Innovations in Intelligent Systems and Applications 507-511, Trabzon Turkey 2009.
5. Say, Ö., Analysis of heart sounds and classification of by using artificial neural networks, Ms Thesis, Institute of Natural and Applied Science, Ýstanbul Technical University, Ýstanbul, Turkey, 2002.
6. Crawford, M. H., Current diagnosis & treatment in cardiology, chapter 23 of congenital heart disease in adults, 2. McGraw-Hill, USA, 2002. 403 p.
7. Leung, T. S., White, P. R., Collis, W. B., Brown, E., and Salmon, A. P., Classification of heart sounds using time-frequency method and artificial neural Networks. Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2:988-991, 2006.
8. Sinha, R. K., Artificial neural network detects changes in electroencephalogram power spectrum of different sleep-wake states in an animal model of heat stress. Med Biol Eng Comput 41:595-600, 2003. (Pubitemid 37258925)
9. Kandaswamy, A., Kumar, C., Ramanathan, R., Jayaraman, S., and Malmurugan, N., Neural classification of lung sounds using wavelet coefficients. Comput Biol Med 34(6):523-537, 2004. (Pubitemid 38950832)
10. O'Rourke, R. A., Cardiovascular disease: foreword. Curr Probl Cardiol 25(11):786-825, 2000.
11. Sharif, Z., Zainal, M. S., Sha'ameri, A. Z., and Salleh, S. H. S., Analysis and classification of heart sounds and murmurs based on the instantaneous energy and frequency estimations. Tencon 41:130-134, 2000.
12. El-Segaier, M., Lilja, O., Lukkarinen, S., Sörnmo, L., Sepponen, R., and Pesonen, E., Computer-based detection and analysis of heart sound and murmur. Ann Biomed Eng 33(7):937-942, 2005. (Pubitemid 40997681)
13. Folland, R., Hines, E. L., Boilot, P., and Morgan, D., Classifying coronary dysfunction using neural networks through cardiovascular auscultation. Med Biol Eng Comput 40:339-343, 2002.
14. Bhatikar, S. R., DeGroff, C., and Mahajan, R. L., classifier based on the artificial neural network approach for cardiologic auscultation in pediatrics. Artif Intell Med 33:251-260, 2005. (Pubitemid 40462459)
15. Reed, T. R., Reed, N. E., and Fritzson, P., Heart sound analysis for symptom detection and computer-aided diagnosis. Simul Model Practice Theory 12(2):129-146, 2004.
16. Sinha, R. K., Aggarwal, Y., and Das, B. N., Backpropagation artificial neural network classifier to detect changes in heart sound due to mitral valve regurgitation. J Med Syst 31:205-209, 2007. (Pubitemid 46985762)
17. Voss, A., Mix, A., and Huebner, T., Diagnosing aortic valve stenosis by parameter extraction of heart sound signals. Ann Biomed Eng 33:1167-1174, 2005. (Pubitemid 41240011)
18. Pavlopoulos, S., Stasis, A., Loukis, E., A decision tree-based method for the differential diagnosis of aortic stenosis from mitral regurgitation using heart sounds. BioMed Eng OnLine (June 3) 1-5, 2004, Available at: http://www.biomedical-engineering-online. com/content/3/1/21.
19. Tetko, I. V., Luik, A. I., and Poda, G. I., Applications of neural networks in structure-activity relationships of a small number of molecules. J Med Chem 36(7):811-814, 1993. (Pubitemid 23118772)
20. Güraksin, G.E., Classification of the heart sounds via artificial neural network, Ms Thesis, Institute of Natural and Applied Science, Afyon Kocatepe University, Afyonkarahisar, Turkey, 2009.
21. Cochran, W. T., Cooley, J. W., Favin, D. L., Helms, H. D., Kaenel, R. A., Lang, W. W., Maling, G. C., Nelson, D. E., Rader, C. M., and Welch, P. D., What is the fast fourier transform. Trans Audio Electroacoust 15:45-55, 1967.
22. Faust, O., Acharya, R. U., Allen, A. R., and Lin, C. M., Analysis of EEG signals during epileptic and alcoholic states using AR modeling techniques. IRBM 29(1):44-52, 2008.
23. Proakis, J. G., and Manolakis, D. G., Digital signal processing principles, algorithms, and applications. Prentice-Hall, New Jersey, 1996.
24. Kay, S. M., Modern spectral estimation: theory and application. Prentice-Hall, New Jersey, 1988.
25. Akaike, H., A new look at the statistical model identification. IEEE Trans Autom Control 19:716-723, 1974.
26. Ölmez, T., and Dokur, Z., Classification of heart sound using an artificial neural network. Pattern Recogn Lett 24:617-629, 2003. (Pubitemid 36080800)
27. Türkoglu, Ý., An Intelligent pattern recognition for nonstationary signals based on the time-frequency entropies, Phd Thesis, Institute of Natural and Applied Science, Firat University, Elazig, Turkey, 2002.
28. Haykin, S., Neural Networks. A Comprehensive Foundation. Macmillan College Publishing Company Inc., New York, 1-60 p., 417 p., 1994.
29. Basheer, I. A., and Hajmeer, M., Artificial neural Networks: Fundementals, computing, design and application. J Microbiol Methods 43:3-31, 2000.
30. Zhang, Y. X., Artificial neural networks based on principal component analysis input selection for clinical pattern recognition analysis. Talanta 73:68-75, 2007. (Pubitemid 47088653)
31. Wang, X., and Paliwal, K. K., Feature extraction and dimensionality reduction algorithms and their applications in vowel recognition. Pattern Recogn 36:2429-2439, 2003. (Pubitemid 36947223)
32. Beksaç, M. S., Başaran, F., Eskiizmirliler, S., Erkmen, A. M., and Yörükan, S., A computerized diagnostic system for the interpretation of umbilical artery blood flow velocity waveforms. Eur J Obstet Gynecol Reprod Biol 64(1):37-42, 1996. (Pubitemid 26062160)
33. Akin, M., and Kiymik, M. K., Application of periodogram and AR spectral analysis to EEG signals. J Med Syst 24(4):247-256, 2000.
34. Seasholtz, M. B., and Kowalski, B., The parsimony principle applied to multivariate calibration. Anal Chim Acta 277:165-177, 1993. (Pubitemid 23157995)
35. Ferr, L., Selection of components in principal component analysis: a comparison of methods. Computat Stat Data Anal 19:669-682, 1995.
36. Valle, S., Li, W., and Qin, S. J., Selection of the number of principal components: The variance of the reconstruction error criterion with a comparison to other methods. Ind Eng Chem Res 38:4389-4401, 1999.
37. Jolliffe, I. T., Principal component analysis, 2nd edition. Springer, New York, 2002.
38. Warne, K., Prasad, G., Rezvani, S., and Maguire, L., Statistical and computational intelligence techniques for inferential model development: a comparative evaluation and a novel proposition for fusion. Eng Appl Artif Intell 17:871-885, 2004.
39. Centor, R. M., Signal detectabilty: The use of ROC curves and their analysis. Med Decis Making 11:102-106, 1991.