A robust audio classification system for detecting pulmonary edema

In this paper we present a robust audio classification system to efficiently detect pulmonary edema. The system uses a feature learning technique based on (NMF), then classified with logistic regression. A study was done to compare feature engineering approaches with feature selection techniques against NMF. Different NMF schemes were investigated and also compared with Principal Component Analysis. NMF scored 95% F1 score, which was superior to feature engineering techniques that had scores from 83% to 93%. Background noise collected from hospitals and speech from a speech corpus database was used to simulate noisy data. The system was then tested using noisy data. The best NMF scheme scored 74%, while other feature engineering techniques scored lower; from 66% to 71%. NMF was also used as a signal enhancement tool. It improved the F1 score to 77%. Lastly, only inhalations from breath sounds were considered and this further improved classification results to 86%. The proposed robust classification system using NMF thus proved to be an effective method for audio-based detection of pulmonary edema. If implemented in real-time, the proposed system can be used as a screening tool.