Institute of Biomedical Engineering, Bogazici University, 34342 Istanbul, Turkey.
Comput Biol Med. 2010 Sep;40(9):765-74. doi: 10.1016/j.compbiomed.2010.07.004. Epub 2010 Aug 21.
The objective of this study is to probe the existence of a third crackle type, medium, besides the traditionally accepted types, namely, fine and coarse crackles and, furthermore, to explore the representative parameter values for each crackle type. A set of clustering experiments have been conducted on pulmonary crackles to this end. A model-based clustering algorithm, the Expectation-Maximization algorithm, is used and the resulting cluster numbers are validated with Bayesian Inference Criterion. Four different feature sets are extracted from the preprocessed crackle samples, the first of which consists of conventional parameters derived from the zero-crossings of crackle waveforms. The second feature set corresponds to the spectral components of the crackles, whereas the remaining two sets are derived from a single- and double-nodes wavelet network modeling. The results of the clustering experiments demonstrate strong evidence for the existence of a third crackle type. Moreover the labels yielded by clustering experiments, using different feature sets match for roughly 80% or more of the crackle samples, resulting in similar representative crackle parameter values of the three clusters for all feature sets.
本研究旨在探究是否存在除传统的细、粗之外的第三种爆裂音类型——中，并且探索每种爆裂音类型的代表性参数值。为此，我们对肺部爆裂音进行了一系列聚类实验。使用基于模型的聚类算法——期望最大化算法，并使用贝叶斯信息准则验证得到的聚类数。从预处理的爆裂音样本中提取了四个不同的特征集，第一个特征集由爆裂音波形过零点得出的常规参数组成。第二个特征集对应于爆裂音的频谱分量，而其余两个特征集则来自单节点和双节点小波网络建模。聚类实验的结果强烈证明了存在第三种爆裂音类型。此外，使用不同特征集进行聚类实验的标签在大约 80%或更多的爆裂音样本中匹配，这导致所有特征集的三个聚类的代表性爆裂音参数值相似。
