A robust audio classification system for detecting pulmonary edema. (September 2018)

• Record Type: Journal Article
• Authors: Hong, K.J.; Essid, S.; Ser, W.; Foo, D.C.-G.
• Is Part Of: Biomedical signal processing and control. Volume 46(2018)
• Page Start: 94
• Page End: 103
• Publication Date: 2018-09
• Subjects: Non-negative matrix factorization -- Robust testing -- Pulmonary edema -- Biomedical signal processing -- Feature learning; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2018.07.004
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: 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.