Modeling occurrence tendency of adventitious sounds and noises for detection of abnormal lung sounds

Abstract

Diagnosis of pulmonary emphysema by using a stethoscope is based on the common knowledge that abnormal respiratory (adventitious) sounds usually appear in patients with pulmonary emphysema. However, the spectral similarity between adventitious sounds and noises at auscultation makes highly accurate automatic detection of adventitious sounds difficult. In this paper, we have proposed a novel method for distinguishing between normal lung sounds in healthy subjects and abnormal sounds, including adventitious sounds in patients, taking into account the occurrence tendency of adventitious sounds and noises. According to our investigation results, adventitious sounds occur repeatedly in successive inspiratory/expiratory phases of patients. On the other hand, noise sounds mix at random in lung sounds of both patients and healthy subjects. In our method, the occurrence tendency of these sounds is described using Gaussian distribution of a random variable obtained by subtracting the acoustic likelihood for abnormal respiration from the likelihood for normal respiration. The spectral likelihood calculated using hidden Markov models and the validity score of the occurrence tendency of the adventitious/noise sounds are combined to derive the classification result. Our method achieved a higher classification rate of 94.1% between normal and abnormal lung sounds than that achieved using the conventional method (87.4%).