Detection of wheezes using their signature in the spectrogram space and musical features
Authors
Luís Mendes
Ioanna Chouvarda
Nicos Maglaveras
César Teixeira
Paulo de Carvalho
Jorge Henriques
Rui Pedro Paiva
et al.
Ioanna Chouvarda
Nicos Maglaveras
César Teixeira
Paulo de Carvalho
Jorge Henriques
Rui Pedro Paiva
et al.
Abstract
In this work thirty features were tested in order to identify the best feature set for the robust detection of wheezes. The features include the detection of the wheezes signature in the spectrogram space (WS-SS) and twenty-nine musical features usually used in the context of Music Information Retrieval. The method proposed to detect the signature of wheezes imposes a temporal Gaussian regularization and a reduction of the false positives based on the (geodesic) morphological opening by reconstruction operator. Our dataset contains wheezes, crackles and normal breath sounds.Four selection algorithms were used to rank the features. The performance of the features was asserted having into account the Matthews correlation coefficient (MCC). All the selection algorithms ranked the WS-SS feature as the most important. A significant boost in performance was obtained by using around ten features. This improvement was independent of the selection algorithm. The use of more than ten features only allows for a small increase of the MCC value.
Subject
Clinical InformaticsRelated Project
WELCOME - Wearable Sensing and Smart Cloud Computing for Integrated Care to COPD Patients with ComorbiditiesConference
37th Int. Conf. of the IEEE Engineering in Medicine and Biology Society – EMBC’2015, August 2015PDF File
Cited by
Year 2016 : 1 citations
Bokov, P., Mahut, B., Flaud, P. and Delclaux, C., 2016. Wheezing recognition algorithm using recordings of respiratory sounds at the mouth in a pediatric population. Computers in biology and medicine, 70, pp.40-50.