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
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