Mendes L, Vogiatzis I M, Perantoni E, Kaimakamis E, Chouvarda I, Maglaveras N, Henriques J, Carvalho P, Paiva R P
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:3679-3683. doi: 10.1109/EMBC.2016.7591526.
The automatic detection of adventitious lung sounds is a valuable tool to monitor respiratory diseases like chronic obstructive pulmonary disease. Crackles are adventitious and explosive respiratory sounds that are usually associated with the inflammation or infection of the small bronchi, bronchioles and alveoli. In this study a multi-feature approach is proposed for the detection of events, in the frame space, that contain one or more crackles. The performance of thirty-five features was tested. These features include thirty-one features usually used in the context of Music Information Retrieval, a wavelet based feature as well as the Teager energy and the entropy. The classification was done using a logistic regression classifier. Data from seventeen patients with manifestations of adventitious sounds and three healthy volunteers were used to evaluate the performance of the proposed method. The dataset includes crackles, wheezes and normal lung sounds. The optimal detection parameters, such as the number of features, were chosen based on a grid search. The performance of the detection was studied taking into account the sensitivity and the positive predictive value. For the conditions tested, the best results were obtained for the frame size equal to 128 ms and twenty-seven features.
自动检测肺部异常声音是监测慢性阻塞性肺疾病等呼吸系统疾病的一项重要工具。爆裂音是一种异常的、具有爆发性的呼吸音，通常与小支气管、细支气管和肺泡的炎症或感染有关。在本研究中，提出了一种多特征方法，用于在包含一个或多个爆裂音的帧空间中检测相关事件。对35种特征的性能进行了测试。这些特征包括通常在音乐信息检索中使用的31种特征、一种基于小波的特征以及Teager能量和熵。使用逻辑回归分类器进行分类。来自17名有异常声音表现的患者和3名健康志愿者的数据用于评估所提方法的性能。该数据集包括爆裂音、哮鸣音和正常肺音。基于网格搜索选择了最优检测参数，如特征数量。从灵敏度和阳性预测值方面研究了检测性能。对于所测试的条件，当帧大小等于128毫秒且使用27种特征时，获得了最佳结果。
