The paper proposes a two-layer pattern recognition system architecture for asthma wheezing detection in recorded children's respiratory sounds. The first layer consists of two SVM classifiers specifically designed as a cascade stacked in parallel to emphasize the differences among signals with similar acoustic properties, such as wheezes and inspiratory stridors. The second layer is realized using a digital detection threshold, which further upgrades the proposed structure with the aim of improving the process of wheezing detection. The results were experimentally evaluated on the data acquired from the General Hospital of Dubrovnik, Croatia. Classification results obtained on the test data sets revealed that the central frequency of wheezes included in the training data is important for the success of classification. 
Two-level coarse-to-fine classification algorithm for asthma wheezing recognition in children's respiratory sounds