Multi-channel classification of respiratory sounds.
In this study, respiratory sounds of pathological and healthy subjects were analyzed via frequency spectrum and AR model parameters with a view to construct a diagnostic aid based on auscultation. Each subject is represented by 14 channels of respiratory sound data of a single respiration cycle. Two reference libraries, pathological and healthy, were built based on multi-channel respiratory sound data for each channel and for each respiration phase, inspiration and expiration, separately. A multi-channel classification algorithm using K nearest neighbor (k-NN) classification method was designed. Performances of the two classifiers using spectral feature set corresponding to quantile frequencies and 6th order AR model coefficients on inspiration and expiration phases are compared.
Yilmaz
C Asli
CA
Syst. & Control Eng., Bogazici Univ., Istanbul, Turkey.
Kahya
Yasemin P
YP
D000465:Algorithms; D001314:Auscultation / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012120:Respiration Disorders / Q000175:diagnosis* / Q000503:physiopathology*; D012135:Respiratory Sounds / Q000145:classification*; D012680:Sensitivity and Specificity; D013018:Sound Spectrography / Q000379:methods*