Probing the existence of medium pulmonary crackles via model-based clustering.

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.