title: Automated recognition of spontaneous versus voluntary cough.
abstract: Cough or cough epochs may be an important and persistent symptom in many respiratory diseases requiring both a continuous and objective observation. The research presented in this paper is aimed at assessing a blind data-based classification between 'spontaneous' and 'voluntary' human cough on individual sound samples. Cough sounds were registered in the free acoustic field on 3 pathological and 9 healthy non-smoking subjects, all aged between 20 and 30. Each sound is represented by the normalized power spectral density (PSD). Different transformations of the cough PSD-vector are chosen as input features to the classification algorithm. An experimental error rate comparison between different neural and fuzzy classification networks is performed. All evaluated algorithms used the Euclidean metric. This resulted in a correct class-discrimination between 'spontaneous' and 'voluntary' cough for 96% of the cough database.
authors.affiliation: Department of Agro-Engineering and Economics, K.U. Leuven, Kasteelpark Arenberg 30, 3001 Leuven, Belgium.
authors.forename: Annemie
authors.initials: A
authors.lastname: Van Hirtum
authors.forename: Daniel
authors.initials: D
authors.lastname: Berckmans
mesh_terms: D000162:Acoustics; D000328:Adult; D000465:Algorithms; D016000:Cluster Analysis; D003371:Cough / Q000145:classification* / Q000175:diagnosis* / Q000503:physiopathology; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D017143:Fuzzy Logic; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013018:Sound Spectrography / Q000379:methods