Robust detection of adventitious lung sounds in electronic auscultation signals

We present a sparse representation-based method for detecting adventitious lung sounds in low-quality auscultation signals. Since the noise cannot be represented sparsely by any bases, we can extract clear breath sounds and adventitious sounds from noisy electronic auscultation signals via the sparse representation. Using these clear sound components, we measure the level of abnormality, and robustly detect adventitious sounds with pulsating waveforms, a.k.a crackles. We have experimentally confirmed that our detection achieves an average precision of about 85 percents regardless of noise level.

1. Introduction

Listening to the patient's body with a stethoscope is a simple but effective method for physical diagnosis. Chest auscultation, in particular, has been a fundamental approach to detect heart and pulmonary disorders. As the electronic stethoscope [1] becomes popular, pattern recognition techniques can contribute to deriving medical information from the electronic auscultation signals.