Detection of cough and adventitious respiratory sounds in audio recordings by internal sound analysis

Abstract

We present a multi-feature approach to the detection of cough and adventitious respiratory sounds. After the removal of near-silent segments, a vector of event boundaries is obtained and a proposed set of 126 features is extracted for each event. Evaluation was performed on a data set comprised of internal audio recordings from 18 patients. The best performance (F-measure = 0.69 ± 0.03; specificity = 0.90 ± 0.01) was achieved when merging wheezes and crackles into a single class of adventitious respiratory sounds.