Robust classification between normal and abnormal lung sounds using adventitious-sound and heart-sound models

Abstract

In this paper, we propose a robust classification method to distinguish between normal lung sounds from healthy subjects and abnormal lung sounds containing adventitious sounds from patients b y using lung sounds contaminated with heart sounds. Heart sounds make it difficult to perform the aforementioned classification with high accuracy. To address this problem, we propose the use of stochastic models to represent the acoustic feature of heart sounds in the classification method based on the maximum likelihood approach b y using hidden Markov models (HMMs) for the calculation of a more exact acoustic likelihood of lung sounds. Our method distinguishes between periods of adventitious sounds and heart sounds by yielding the most likely acoustic segment sequence for each respiration. For the test set of lung sounds contaminated with heart sounds, our classification method achieved a higher classification rate between normal and abnormal lung sounds compared to the conventional method without heart-sound models (88.6% vs. 87.1%, respectively). The classification of healthy and patient subjects b y using the proposed method also achieved a higher classification rate of 83.0% when two samples from different auscultation points were used.