Elimination of vesicular sounds from pulmonary crackle waveforms

Instantaneous frequency based index to characterize respiratory crackles

BACKGROUND

Crackle is a lung sound widely employed by health staff to identify respiratory diseases. The two-cycle duration (2CD) is a quantitative index pointed out by the American Thoracic Society and the European Respiratory Society to classify respiratory crackles as fine or coarse. However, this index, measured in the time domain, is highly affected by noise and filters of recording systems. Such factors hamper the analysis of data reported by different research groups. This work proposes a new index based on the instantaneous frequency of crackles estimated by means of discrete-time pseudo Wigner-Ville distribution.

METHOD

Comparisons between 2CD and the proposed index were carried out for simulated and actual crackles. Normal breathing sounds were added to simulated crackles; the resulting signals were then applied to a band-pass filter that mimics those belonging to lung sound acquisition systems. Thus, the impact of noise and filtering on these two indices was assessed for simulated crackles. Kruskal-Wallis and Dunn's tests as well as Gaussian mixture model (GMM) were applied to the two indices measured from 382 actual crackles belonging to open databases.

RESULTS

The proposed index is much less susceptible to waveform distortions due to noise and filtering when compared to the 2CD. Thus, the statistical analyses allow the identification of two classes of crackles from actual databases; the same does not occur when using 2CD.

CONCLUSIONS

The new proposed index has the potential to contribute for a better characterization of crackles generated by different respiratory diseases, assisting their diagnosis during clinical exams.

Performance comparison of wavelet based denoising methods on discontinuous adventitious lung sounds

Crackles and their time-domain characteristics provide important clues about different lung diseases. In this paper, we aim to de-noise synthetically produced crackles under various noise levels while preserving their information bearing parts which significantly affect crackle parameters. Classical wavelet based de-noising algorithms are deteriorated by sharp-sudden noise changes and produce Gibbs like fluctuations. On the other hand, total variation based algorithms, which are capable of alleviating the drawbacks of the classical wavelet based algorithms, are failed when dealing with piecewise-smooth signals like crackles and generate unwanted flat regions on the de-noised signals. Proposed wavelet total variation based de-noising is succeed in removing undesired artefacts originating from both classical wavelet and total variation de-noising. The proposed method is compared with classical wavelet based de-noising methods in terms of root mean square error under various white Gaussian noise levels (0 - 20 dB SNR). Moreover, in order to emphasize the de-noising ability of the methods, without deforming crackle waveform, time and frequency domain representation of a noisy and de-noised crackle is validated visually.

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.