Noise detection in phonocardiograms by exploring similarities in spectral features

The described approach was compared with two existent methodologies developed for the same purpose. Our algorithm was found to return the best performance regarding the different types of interferences, resulting in an average sensitivity and specificity of 88.4% and 85.6%, respectively, for healthy dataset and 84.3% and 85.8%, respectively, for the dataset with pathology. These results correspond to an increase of up to 4.7% in SE and 39.0% in SP when comparing to the two referred methodologies in healthy dataset. Regarding the pathological dataset our approach improved noise detection by up to 27.0% in SE and 34.1% in SP.