Coughing is one of the most frequent presenting symptoms of many diseases affecting the airways and the lungs of humans and animals. The aim of this research is to build an intelligent alarm system that can be used for the early detection of cough sounds in pig houses. Registration of coughs from different pigs in a metallic chamber was done in order to analyze the acoustical signal. A new approach is presented to distinguish cough sounds from other sounds like grunts, metal clanging, and noise using neural network classification methods. Other signals (grunts, metal clanging, etc.) could also be detected. A hybrid classifier is proposed that achieves the highest classification accuracy in both the off-line and the on-line detection of coughs and other sounds. The best correct classification performance was obtained with a hybrid classifier that classified coughs and metal clanging separately from other sounds, giving better results compared to a multi-layer perceptron alone. The hybrid classifier, which consisted of a 2class probabilistic neural network and a 4class multi-layer perceptron, gave high discrimination performance in the case of grunts and noise (91.3 and 91.3 respectively) and a performance of 94.8 for correct classification in the case of coughs. The early detection of coughs can be used for the construction of an intelligent alarm that can signal the presence of a possible viral infection so that early treatment can be implemented.
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