title: Classification of wheeze sounds using cepstral analysis and neural networks.
abstract: Wheezes are abnormal, continuous sounds heard over large airways and chest. They are divided to two groups based on relative intensity of airway obstruction. They are usually heard in asthma, pneumonia, emphysema and chronic obstructive pulmonary diseases (COPD). We present a classification method to discriminate between polyphonic and monophonic wheeze sounds using multilayer perceptron (MLP) neural network and mel-frequency cepstral coefficients (MFCC). Wheeze signals are divided to segments with 50% overlap. MFCC features are then extracted. Groups with different numbers of MFCC powerful features are compared by receiver operating characteristic (ROC) curves. The test results show an accuracy of 92.8%.
authors.affiliation: Research Center for Science and Technology in Medicine (RCSTIM), Tehran University of Medical Sciences, Tehran, Iran.
authors.forename: Amjad
authors.initials: A
authors.lastname: Hashemi
authors.forename: Hossein
authors.initials: H
authors.lastname: Arabalibeik
authors.forename: Khosrow
authors.initials: K
authors.lastname: Agin
mesh_terms: D006801:Humans; D007492:Iran; D016571:Neural Networks, Computer; D012135:Respiratory Sounds / Q000145:classification* / Q000175:diagnosis*; D012815:Signal Processing, Computer-Assisted