Classification of respiratory signals by linear analysis
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, f50/ f90 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.
2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society