title: Automatic cough episode detection using a vibroacoustic sensor.
abstract: Cough monitoring is an important element of the diagnostics of respiratory diseases. The European Respiratory Society recommends objective assessment of cough episodes and the search for methods of automatic analysis to make obtaining the quantitative parameters possible. The cough "events" could be classified by a microphone and a sensor that measures the vibrations of the chest. Analysis of the recorded signals consists of calculating the features vectors for selected episodes and of performing automatic classification using them. The aim of the study was to assess the accuracy of classification based on an artificial neural networks using vibroacoustic signals collected from chest. Six healthy, young men and eight healthy, young women carried out an imitated cough, hand clapping, speech and shouting. Three methods of parametrization were used to prepare the vectors of episode features - time domain, time-frequency domain and spectral modeling. We obtained the accuracy of 95% using artificial neural networks.
authors.forename: Marcel
authors.initials: M
authors.lastname: Mlynczak
authors.forename: Katarzyna
authors.initials: K
authors.lastname: Pariaszewska
authors.forename: Gerard
authors.initials: G
authors.lastname: Cybulski
mesh_terms: D003371:Cough; D005260:Female; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D013060:Speech; D013909:Thorax; D014732:Vibration