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

该项目将利用电子听诊器记录的呼吸声来检测重症监护病房患者的肺部问题。用传统的听诊器听肺部提供了对气道和肺组织健康的评估，但它提供的信息是主观和定性的。计算机辅助肺音分析（CALSA）使用数字听诊器记录声音进行处理，消除主观性并允许量化其声学特性。来自CALSA的信息有可能帮助早期发现，诊断和监测肺部并发症，这是常见的手术后和重症监护患者。机械通气患者发生呼吸机相关性肺炎（VAP）的风险很高，这是发病率和死亡率的重要原因;其中一个症状是肺音的变化。在这项研究中，重点将是对肺音记录的信号处理和分析，以评估提取的声学特征的有用性，用于预测和监测VAP的发展。信号调理技术的发展，以消除不必要的声音将随后应用特征提取和选择技术，以确定最敏感的VAP发作和进展的预测声学特征的子集。我们的目标是一个强大而可靠的系统，可以很容易地在临床环境中使用的卫生服务专业人员。

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.