A telemedicine tool to detect pulmonary pathology using computerized pulmonary acoustic signal analysis
Respiratory sound signals are prepossessed and respiratory cycles are segmented. S-transform based statistical features were extracted. The features were classified using KNN, SVM and ELM classifier. Accuracy of 94.99%, 96.85% and 98.52% for KNN, SVM and ELM, respectively. Telemedicine software was developed using ELM classifier.
telemedicine s-transform breath sounds extreme learning machine health care