Classification of Porcine Wasting Diseases Using Sound Analysis

This bio-acoustic study was aimed at classifying the different porcine wasting diseases through sound analysis with emphasis given to differences in the acoustic footprints of coughs in porcine circo virus type 2 (PCV2), porcine reproductive and respiratory syndrome (PRRS) virus and Mycoplasma hyopneumoniae (MH) - infected pigs from a normal cough. A total of 36 pigs (Yorkshire${\times}$Landrace${\times}$Duroc) with average weight ranging between 25-30 kg were studied, and blood samples of the suspected infected pigs were collected and subjected to serological analysis to determine PCV2, PRRS and MH. Sounds emitted by coughing pigs were recorded individually for 30 minutes depending on cough attacks by a digital camcorder placed within a meter distance from the animal. Recorded signals were digitalized in a PC using the Cool Edit Program, classified through labeling method, and analyzed by one-way analysis of variance and discriminant analysis. Input features after classification showed that normal cough had the highest pitch level compared to other infectious diseases (p<0.002) but not statistically different from PRRS and MH. PCV2 differed statistically (p<0.002) from the normal cough and PRRS but not from MH. MH had the highest intensity and all coughs differed statistically from each other (p<0.0001). PCV2 was statistically different from others (p<0.0001) in formants 1, 2, 3 and 4. There was no statistical difference in duration between different porcine diseases and the normal cough (p>0.6863). Mechanisms of cough sound creation in the airway could be used to explain these observed acoustic differences and these findings indicated that the existence of acoustically different cough patterns depend on causes or the animals' respiratory system conditions. Conclusively, differences in the status of lungs results in different cough sounds. Finally, this study could be useful in supporting an early detection method based on the on-line cough counter algorithm for the initial diagnosis of sick animals in breeding farms.