Discrimination between healthy subjects and patients using lung sounds from multiple auscultation points

In this paper, we propose a robust classification method to distinguish between a healthy subject and a patient with pulmonary emphysema using lung sound samples recorded from multiple auscultation points. Although the symptom of pulmonary emphysema can be determined from lung sounds that frequently include abnormal (i.e., adventitious) sounds, these are not observed in every auscultation point. Furthermore, noise pollution during auscultation makes high-accuracy detection difficult. To overcome these difficulties, our proposed method took into account lung sound samples from multiple auscultation points in diagnosing a patient. After the calculation of the acoustic likelihood for each respiratory phase based on the maximum likelihood approach using hidden Markov models and a segmental bigram, patient diagnosis was carried out based on the comparison of the average likelihood of all auscultation points between a patient and a healthy subject. Our classification method significantly increased the classification performance to 90.5% (using samples from four auscultation points) from the 82.7% classification performance of the conventional method (using a sample from one auscultation point), validating the usefulness of our proposed method.