title: Classification of respiratory signals by linear analysis.
abstract: The aim of this study is the classification of wheeze and non-wheeze epochs within respiratory sound signals acquired from patients with asthma and COPD. Since a wheeze signal, having a sinusoidal waveform, has a different behavior in time and frequency domains from that of a non-wheeze signal, the features selected for classification are kurtosis, Renyi entropy, f(50)/ f(90) ratio and mean-crossing irregularity. Upon calculation of these features for each wheeze and non-wheeze portion, the whole data scattered as two classes in four dimensional feature space is projected using Fisher Discriminant Analysis (FDA) onto the single dimensional space that separates the two classes best. Observing that the two classes are visually well separated in this new space, Neyman-Pearson hypothesis testing is applied. Finally, the correct classification rate is %95.1 for the training set, and leave-one-out approach pursuing the above methodology yields a success rate of %93.5 for the test set.
authors: Sergul
authors: S
authors: Aydore
authors: Ipek
authors: I
authors: Sen
authors: Yasemin P
authors: YP
authors: Kahya
authors: M
authors: M
authors: Mihcak
mesh_terms: D000328:Adult; D000368:Aged; D000669:Amplifiers, Electronic; D001249:Asthma / Q000503:physiopathology*; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D029424:Pulmonary Disease, Chronic Obstructive / Q000503:physiopathology*; D015203:Reproducibility of Results; D012135:Respiratory Sounds; D012815:Signal Processing, Computer-Assisted