Classification of healthy subjects and patients with pulmonary emphysema using continuous respiratory sounds

In this paper, we propose a new method for classifying patients with pulmonary emphysema and healthy subjects using lung sounds. Using conventional classification methods, every boundary between inspiratory and expiratory phases in successive respiratory sounds are detected manually prior to automatic classification. However, manual segmentation must be performed accurately and has therefore created significant obstacles in achieving automatic classification. In our proposed method, adequate boundaries are detected automatically in the classification process, based on the criterion of maximizing the difference between the acoustic likelihoods for a candidate with abnormal respiration and one with normal respiration. The proposed method achieved a classification rate of 83.9% between healthy subjects and patients. The reported rate was 1.3% greater than the rate achieved using the conventional method, which required manual phase-wise segmentation. Furthermore, the resulting rate was 2.2% higher than the rate obtained by the classification in which a lung sound sample was divided into phases of equal duration, indicating the effectiveness of the proposed method.