Charleston-Villalobos Sonia, Castañeda-Villa Norma, González-Camarena Ramón, Mejía-Ávila M, Aljama-Corrales Tomás
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:4126-9. doi: 10.1109/EMBC.2015.7319302.
Discontinuous lung sounds (DLS), also known as crackles, are abnormal sounds produced by different pulmonary pathologies (PP) whose thoracic spatial distribution and prevalence are relevant for diagnosis purpose. Recently, DLS imaging has been proposed to help diagnose and follow-up PP where automated recognition of DLS is meaningful. The present study focuses on the automated selection of independent components (ICs) associated with DLS. Extraction of ICs information for clustering by k-means is achieved in two ways: (1) forming features vectors (FVs) containing the kurtosis, entropy and sparsity of each IC or (2) by applying mutual information (MI) or Euclidean distance (ED) to all ICs. Next, silhouette index is computed to estimate the number of necessary clusters (C). Afterward, to detect just the clusters containing ICs of DLS a selection index is proposed. Finally, to estimate the number of DLS per ICs in each selected cluster a time-variant AR modeling is applied; the estimated number is shown in conjunction with the 2D-ICs spatial distribution. The methodology is applied to simulated and real cases; DLS imaging results are also compared against clinical auscultation. The results showed that the automated selection via FVs is promising to imaging DLS.
不连续肺音（DLS），也称为啰音，是由不同肺部疾病（PP）产生的异常声音，其胸部空间分布和发生率对诊断具有重要意义。最近，有人提出DLS成像有助于诊断和随访PP，其中DLS的自动识别很有意义。本研究的重点是自动选择与DLS相关的独立成分（IC）。通过两种方式实现用于k均值聚类的IC信息提取：（1）形成包含每个IC的峰度、熵和稀疏性的特征向量（FV），或（2）对所有IC应用互信息（MI）或欧几里得距离（ED）。接下来，计算轮廓系数以估计所需聚类（C）的数量。之后，为了仅检测包含DLS的IC的聚类，提出了一个选择指数。最后，为了估计每个选定聚类中每个IC的DLS数量，应用时变自回归建模；估计数量与二维IC空间分布一起显示。该方法应用于模拟和实际病例；DLS成像结果也与临床听诊进行了比较。结果表明，通过FV进行自动选择对DLS成像很有前景。
