Rich Representation Spaces: Benefits in Digital Auscultation Signal Analysis

When developing automated techniques for analysis of auscultation signals, the choice of a proper representational space that characterizes all attributes of interest in the signal is of paramount importance. In this paper, we investigate different feature representation methods and their benefits in distinguishing auscultation sounds. The importance of choosing an appropriate feature space is explored and validated using trained classifiers that distinguish between normal and abnormal respiratory sounds. Findings of this study are two-fold: i) an increased dimensionality in the feature space can provide a more complete and distinct representation of the delicate breath sounds and ii) dimensionality of the feature space alone is not enough to fully capture discriminative attributes: an informative feature space is even more crucial for extracting accurate, disease-specific characteristics of respiratory sounds.