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.