Automated Analysis of Lung Sounds as a Predictor of Ventilator Associated Pneumonia (VAP)

This project will make use of breath sounds, recorded using an electronic stethoscope, to detect lung problems in patients on intensive care units. Listening to the lungs with a traditional stethoscope provides an assessment of airway and lung tissue health, but the information it gives is subjective and qualitative. Computer aided lung sound analysis (CALSA) uses a digital stethoscope to record sounds for processing, removing the subjectivity and allowing quantification of their acoustic characteristics. Information derived from CALSA has the potential to aid the early detection, diagnosis and monitoring of lung complications, which are common in post-operative and intensive care patients. Mechanically ventilated patients are at high risk for developing ventilator-associated pneumonia (VAP), which is a significant cause of morbidity and mortality; one symptom of this is a change to the lung sounds.

In this study the focus will be on signal processing and analysis of lung sound recordings to evaluate the usefulness of extracted acoustics features for predicting and monitoring development of VAP. Development of signal conditioning techniques to remove unwanted sounds will be followed by application of feature extraction and selection techniques to identify the subset of acoustic features most sensitively predictive of VAP onset and progression. The goal is a robust and reliable system that can easily be used by health service professionals in clinical surroundings.