Content-based classification of breath sound with enhanced features

Since breath sound (BS) contains important indicators of respiratory health and disease, analysis and detection of BS has become an important topic, with diagnostic and assessment of treatment capabilities. In this paper, the identification and classification of respiratory disorders based on the enhanced perceptual and cepstral feature set (PerCepD) is proposed. The hybrid PerCepD feature can capture the time-frequency characteristics of BS very well. Thus, it is very effective for the exploration and classification of normal and pathological BS related data. The supervised classification models based on support vector machine (SVM) and artificial neural network (ANN) have been adopted to achieve automatic detection from BS data. The high detection accuracy results validate the performance of the proposed feature sets and classification model. The experimental results also demonstrate that the high accuracy of the pathological BS data can provide reliable diagnostic suggestions for breath disorders, such as flu, pneumonia and bronchitis.