Resonance based respiratory sound decomposition aiming at localization of crackles in noisy measurements

Abstract

In this work, resonance based decomposition of lung sounds that aims to separate wheeze, crackle and vesicular sounds into three individual channels while automatically localizing crackles for both synthetic and real data is presented. Previous works focus on stationary-non stationary discrimination to separate crackles and vesicular sounds disregarding wheezes which are stationary as compared to crackles. However, wheeze sounds include important cues about the underlying pathology. Using two different threshold methods and synthetic sound generation scenarios in the presence of wheezes, resonance based decomposition performs 89.5 % crackle localization recall rate for white Gaussian noise and 98.6 % crackle localization recall rate for healthy vesicular sound treated as noise at low signal-to-noise ratios. Besides, an adaptive threshold determination which is independent from the channel at which it will be applied is used and is found to be robust to noise.