تشخیص بیماری‌های نیوکاسل، برونشیت و آنفلوانزای پرنده با استفاده از سیگنال صدای قلب و ماشین بردار پشتیبان

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشگاه تربیت مدرس

2 موسسه 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

موضوعات


عنوان مقاله [English]

Diagnosing avian Newcastle, Bronchitis and Influenza Diseases using heart sound signal and Support Vector Machine

چکیده [English]

This study represents an intelligence procedure for diagnosis simultaneously avian Newcastle Disease Virus, Infection Bronchitis Virus and Influenza using heart sound signal. For this aim, the chickens were divided into four groups. The first group was considered as control samples. The second, third and fourth groups were infected with Newcastle Disease Virus, Infection Bronchitis and Avian Influenza, respectively. The time domain signals were transferred to the frequency and time-frequency domain using Fast Fourier Transform and Discrete Wavelet Transform. In data mining stage, 25 statistical features were extracted from three domains and the best features were selected using improved distance evaluation (IDE) method. The heart sound signals were classified using multiclass support vector machine and Dempster-Shafer evidence theory. The total accuracy, Specificity and Sensitivity of classifiers fusion in diagnosing avian diseases were obtained 81.93, 93.29 and 82.28 percent respectively.

کلیدواژه‌ها [English]

  • Support Vector Machine (SVM)
  • Discrete wavelet transform (DWT)
  • Avian Disease
  • Dempster-Shafer evidence theory
 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.