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تشخیص بیماریهای نیوکاسل، برونشیت و آنفلوانزای پرنده با استفاده از سیگنال صدای قلب و ماشین بردار پشتیبان | ||
مهندسی بیوسیستم ایران | ||
مقاله 1، دوره 47، شماره 4، بهمن 1395، صفحه 587-601 اصل مقاله (998.26 K) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/ijbse.2017.60253 | ||
نویسندگان | ||
محمد صادقی1؛ احمد بناکار* 1؛ عبدالحمید شوشتری2 | ||
1دانشگاه تربیت مدرس | ||
2موسسه تحقیقات واکسن و سرم سازی رازی | ||
چکیده | ||
در این پژوهش روشی هوشمند به منظور تشخیص توامان بیماریهای نیوکاسل، آنفلوانزا و برRBFونشیت پرنده از روی سیگنال صدای قلب پرداخته شده است. در ابتدا جوجهها به 4 دسته تقسیم شدند. یک گروه به عنوان نمونههای شاهد در نظر گرفته شدند و با ویروس هیچگونه تماسی نداشتند و 3 گروه باقیمانده به ترتیب به ویروسهای نیوکاسل، آنفلوانزا و برونشیت آلوده شدند. سیگنالهای حوزه زمان صدای قلب توسط تبدیل فوریه سریع و تبدیل موجک گسسته دابچی نوع اول در دو سطح تجزیه به ترتیب به حوزههای فرکانس و زمان- فرکانس انتقال داده شدند. در مرحله دادهکاوی از سیگنالهای هر سه حوزه 25 ویژگی آماری استخراج شدند و با استفاده از IDE بهترین ویژگیها انتخاب شدند. با استفاده از ماشین بردار پشتیبان و نظریه شواهد دمپستر- شافر سیگنالهای صدای قلب جوجهها طبقهبندی شدند. دقت میانگین، Specificityو Sensitivity تلفیق طبقهبندها به منظور تشخیص بیماریها به ترتیب93/81، 29/93 و 28/82 درصد به دست آمد. | ||
کلیدواژهها | ||
ماشین بردار پشتیبان؛ تبدیل موجک گسسته؛ بیماری طیور؛ نظریه شواهد دمپستر – شافر | ||
مراجع | ||
Acevedo, M. A., Corrada-Bravo, C. J., Corrada-Bravo, H., Villanueva-Rivera, L. J. & Aide, T. M. (2009). Automated classification of bird and amphibian calls using machine learning: A comparison of methods. Ecological Informatics, 4(4): 206-214. Akin, M. (2002). Comparison of wavelet transform and FFT methods in the analysis of EEG signals. Journal of medical systems, 26(3), 241-247. Al-Ani, A. & Deriche, M. (2002). A new technique for combining multiple classifiers using the Dempster-Shafer theory of evidence. Journal of Artificial Intelligence Research: 333-361. Alexander, D. J. (2000). A review of avian influenza in different bird species. Veterinary microbiology, 74(1), 3-13. Balachandran, A., Ganesan, M. & Sumesh, E. (2014). Daubechies algorithm for highly accurate ECG feature extraction. Green Computing Communication and Electrical Engineering (ICGCCEE), 2014 International Conference on, IEEE. Banakar, A. & Azeem, M. F. (2008). Artificial wavelet neuro-fuzzy model based on parallel wavelet network and neural network. Soft Computing, 12(8): 789-808. Bagheri, B., Ahmadi, H., Labbafi, R. (2010). Application of data mining and feature extraction on intelligent fault diagnosis by artificial neural network and k-nearest neighbor, in: XIX International Conference on Electrical Machines - ICEM , Rome, IEEE, pp. 1-7. Boddy, L., Morris, C.W., Wilkins, M.F., Tarran, G.A. & Burkill, P.H. (1994). Neural network analysis of flow citometric data for 40 marine phytoplankton species. Cytometry, 15:283-293. Boroudjerdi, F., Mardjanmehr, S., Shushtari, A., Tavassoli, A., Mirsalimi, S. & Bahmaninejad, M. (2010). An experimental study on histopathological lesions of Iranian isolates of influenza A (H9N2) virus in BALB/C mouse. Journal of Veterinary Research, 65(3): 231-238, 267. Burges, C. (1998). A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery, 2(2): 121-167. Capua, I. & Alexander, D. J. (2009). Avian influenza and Newcastle disease: a field and laboratory manual, Springer Science & Business Media. Chedad, A., Moshou, D., Aerts, J., Van Hirtum, A., Ramon, H. & Berckmans, D. (2001). Recognition System for Pig Cough based on Probabilistic Neural Networks. J. agric. Engng Res, 79 , 449-457. Chesmore, E. D., Femminella, O.P. & Swarbrick, M.D. (1998). Automated analysis of insect soundsusing time-encoded signals and expert systems - a new method for species identification. Information Technology, Plant Pathology and Biodiversity. CAB International,Wallinford, 273-287. Cook, J. K. & Mockett, A. (1995). Epidemiology of infectious bronchitis virus. The coronaviridae, Springer, 317-335. Corman, V., Eickmann, M., Landt, O., Bleicker, T., Brunink, S., Eschbach-Bludau, M., Matrosovich, M., Becker, S. & Drosten, C. (2013). Specific detection by real-time reverse-transcription PCR assays of a novel avian influenza A (H7N9) strain associated with human spillover infections in China. Euro Surveill, 18(16): 20461. Cortes, C. & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3): 273-297. Duhamel, P. & Vetterli, M. (1990). Fast Fourier transforms: a tutorial review and a state of the art. Signal processing, 19(4): 259-299. Fagerlund, S. (2007). Bird species recognition using support vector machines. EURASIP Journal of Advances in Signal Processing, 1-8. Farfani, H., Behnamfar, F. & Fathollahi, A. (2015). Dynamic analysis of soil-structure interaction using the neural networks and the support vector machines, Expert Systems with Applications, 42 (22): 8971-8981. Fu, X., Yan, G., Chen, B. & Li, H. (2005). Application of wavelet transforms to improve prediction precision of near infrared spectra. Journal of Food Engineering, 69(4): 461-466. Ganapathy, K. (2009). Diagnosis of infectious bronchitis in chickens. In Practice, 31(9): 424-431. Gokhale, P. S. (2012). ECG Signal De-noising using Discrete Wavelet Transform for removal of 50Hz PLI noise. International Journal of Emerging Technology and Advanced Engineering, 2(5): 81-85. Gong, W., Obikawa, T. & Shirakashi, T. (1997). Monitoring of tool wear states in turning based on wavelet analysis. JSME international journal. Series C, dynamics, control, robotics, design and manufacturing, 40(3): 447-453. Guan, Y., Shortridge, K., Krauss, S., Chin, P., Dyrting, K., Ellis, T., Webster, R. & Peiris, M. (2000). H9N2 influenza viruses possessing H5N1-like internal genomes continue to circulate in poultry in southeastern China. Journal of virology, 74(20): 9372-9380. Guo, Y., Krauss, S., Senne, D., Mo, I., Lo, K., Xiong, X., Norwood, M., Shortridge, K., Webster, R. & Guan, Y. (2000). Characterization of the pathogenicity of members of the newly established H9N2 influenza virus lineages in Asia. Virology, 267(2): 279-288. Guo, Y., Li, J. & Cheng, X. (1999). [Discovery of men infected by avian influenza A (H9N2) virus]. Zhonghua shi yan he lin chuang bing du xue za zhi= Zhonghua shiyan he linchuang bingduxue zazhi= Chinese journal of experimental and clinical virology, 13(2): 105-108. Gupta, C. N., Palaniappan, R., Swaminathan, S. & Krishnan, S. M. (2007). Neural network classification of homomorphic segmented heart sounds. Applied Soft Computing, 7(1): 286-297. Gutierrez, W., Kim, S., Kim, D., Yeon, S. & Chang, H. (2010). Classification of porcine wasting diseases using sound analysis. Asian-Australasian Journal of Animal Sciences, 23(8): 1096-1104. Haryanto, A., Irianingsih, S. H., Yudianingtyas, D. W., Wijayanti, N. & Budipitojo, T. (2013). Single step multiplex RT-PCR for detection and differential diagnosis of avian influenza, newcastle disease and infectious bursal disease viruses in chicken. Int Res J Biotechnol, 4: 34-39. Haryanto, A., Purwaningrum, M., Verawati, S., Irianingsih, S. H. & Wijayanti, N. (2015). Pathotyping of Local Isolates Newcastle Disease Virus from Field Specimens by RT-PCR and Restriction Endonuclease Analysis. Procedia Chemistry, 14: 85-90. Hsu, C.-W. & Lin, C.-J. (2002). A comparison of methods for multiclass support vector machines. Neural Networks, IEEE Transactions on, 13(2): 415-425. Ionescu, R. & Llobet, E. (2002). Wavelet transform-based fast feature extraction from temperature modulated semiconductor gas sensors. Sensors and Actuators B: Chemical, 81(2): 289-295. Iyer, S., Sinha, S.K., Tittmann, B.R. & Pedrick, M.K. (2012). Ultrasonic signal processing methods for detection of defects in concrete pipes, Automation in Construction, 22: 135-148. Kasten, E. P., P. K. McKinley, et al (2010). Ensemble extraction for classification and detection of bird species. Ecological Informatics, 5(3): 153-166. Khazaee, M., Ahmadi, H., Omid, M., Banakar, A. & Moosavian, A. (2013). Feature-level fusion based on wavelet transform and artificial neural network for fault diagnosis of planetary gearbox using acoustic and vibration signals. Insight-Non-Destructive Testing and Condition Monitoring, 55(6): 323-330. Klir, G. J. & Wierman, M. J. (1999). Uncertainty-based information: elements of generalized information theory, Springer Science & Business Media. Lee, J., Jin, L., Park, D., Chung, Y. & Chang, H.-H. (2015). Acoustic Features for Pig Wasting Disease Detection. International Journal of Information Processing and Management, 6(1): 37. Lee, J. J., Lee, S. M., Kim, I. Y., Min, H. K. & Hong, S. H. (1999). Comparison between short time Fourier and wavelet transform for feature extraction of heart sound. TENCON 99. Proceedings of the IEEE Region 10 Conference, IEEE. Lei, Y., He, Z. & Zi, Y. (2008). A new approach to intelligent fault diagnosis of rotating machinery. Expert Systems with Applications, 35(4): 1593-1600. Lin, Y., Shaw, M., Gregory, V., Cameron, K., Lim, W., Klimov, A., Subbarao, K., Guan, Y., Krauss, S. & Shortridge, K. (2000). Avian-to-human transmission of H9N2 subtype influenza A viruses: relationship between H9N2 and H5N1 human isolates. Proceedings of the National Academy of Sciences, 97(17): 9654-9658. Malik, Y. S., Patnayak, D. P. & Goyal, S. M. (2004). Detection of three avian respiratory viruses by single-tube multiplex reverse transcription–polymerase chain reaction assay. Journal of veterinary diagnostic investigation, 16(3): 244-248. Marchant, B. (2003). Time–frequency analysis for biosystems engineering. Biosystems engineering, 85(3): 261-281. Misiti, M., Misiti, Y., Oppenheim, G. & Poggi, J.-M. (1996). Wavelet toolbox. The MathWorks Inc., Natick, MA. Morgan, H. (1946). Newcastle Disease Of Poultry. Iowa State University Veterinarian, 9(1): 4. Naeem, K., Ullah, A., Manvell, R. & Alexander, D. (1999). Avian influenza A subtype H9N2 in poultry in Pakistan. Veterinary Record, 145(19): 560-560. Nidzworski, D., Wasilewska, E., Smietanka, K., Szewczyk, B. & Minta, Z. (2013). Detection and differentiation of Newcastle disease virus and influenza virus by using duplex real-time PCR. Acta Biochimica Polonica, 60(3): 475-480. Parsons, S. & Jones, G. (2000). Acoustic identification of twelve species of echolocating bat by discriminant function analysis and artificial neural networks. Experimental Biology, 203(17): 2641-2656. Peiris, M., Yuen, K., Leung, C., Chan, K., Ip, P., Lai, R., Orr, W. & Shortridge, K. (1999). Human infection with influenza H9N2. The Lancet, 354(9182): 916-917. Ruhm, K. H. (2007). Sensor fusion and data fusion–Mapping and reconstruction. Measurement, 40(2): 145-157. Saravanan, N. & Ramachandran, K. (2010). Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN). Expert Systems with Applications, 37(6): 4168-4181. Schalk, A. & Hawn, M. (1931). An apparently new respiratory disease of baby chicks. J. Am. Vet. Med. Assoc, 78(413-422): 19. Sentz, K. & Ferson, S. (2002). Combination of evidence in Dempster-Shafer theory, Citeseer. Shafer, G. (1976). mathematical theory of evidence, Princeton university press Princeton. Shafer, G. (2013). Probability Judgement in Artificial Intelligence. arXiv preprint arXiv:1304.3429. Simmonds, E. J., Armstrong, F. & Copland, P.J. (1996). Species identification using wideband backscatter with neural network and discriminant analysis. ICES Journal of Marine Science, 53: 189-195. Turkoglu, I., Arslan, A. & Ilkay, E. (2003). An intelligent system for diagnosis of the heart valve diseases with wavelet packet neural networks. Computers in Biology and Medicine, 33(4): 319-331. Vasfi Marandi, M. & Bozorgmehri Fard, M. H. (2002). Isolation of H9N2 subtype of avian influenza viruses during an outbreak in chickens in Iran. Iranian Biomedical Journal, 6(1): 13-17. Wang, X., Makis, V. & Yang, M. (2010). A wavelet approach to fault diagnosis of a gearbox under varying load conditions. Journal of Sound and Vibration, 329(9): 1570-1585. Wu, J. D. & Liu, C. H. (2009). An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network. Expert Systems with Applications, 36(3): 4278-4286. Xia, J. F., Li, X. Y., Li, P. W., Qian, M. & Ding, X. X. (2007). Application of wavelet transform in the prediction of navel orange vitamin C content by near-infrared spectroscopy. Agricultural Sciences in China, 6(9): 1067-1073. Xu, C., Zhang, H., Peng, D. & Yu, Y. (2012). Study of fault diagnosis of integrate of DS evidence theory based on neural network for turbine. Energy Procedia, 16: 2027-2032. Yager, R. R. (1987). On the Dempster-Shafer framework and new combination rules. Information Sciences, 41(2): 93-137. Zhan, Y. & Makis, V. (2006). A robust diagnostic model for gearboxes subject to vibration monitoring. Journal of Sound and Vibration , 290(3): 928-955. Zhu, D.-q. (2002). Data fusion algorithm based on DS evidential theory and its application for circuit fault diagnosis. Acta electronica sinica, 30(2): 221-223. Zhu, D., Ji, B., Meng, C., Shi, B., Tu, Z. & Qing, Z. (2007). Study of wavelet denoising in apple's charge-coupled device near-infrared spectroscopy. Journal of agricultural and food chemistry, 55(14): 5423-5428. Zhu, K., Wong, Y. S. & Hong, G. S. (2009). Wavelet analysis of sensor signals for tool condition monitoring: a review and some new results. International Journal of Machine Tools and Manufacture, 49(7): 537-553.
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