Neural classification of lung sounds using wavelet packet coefficients energy

Authors:
Yi Liu;Caiming Zhang;Yuhua Peng
Affiliations:
School of Computer Science and Technology, Shandong Univ., Jinan, China;School of Computer Science and Technology, Shandong Univ., Jinan, China;School of Computer Sci. and Eng. , Shandong Univ., Jinan, China
Venue:
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Year:
2006

Citing 4
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Abstract

A novel method for recognition two kinds of lung sounds is presented. The proposed scheme is based on the analysis of a wavelet packet decomposition (WPD). Normal and abnormal lung sounds data were sampled from various subjects. Each signal is segmented to inspiration and expiration. From their high dimension WPD coefficients, we build the compact and meaningful energy feature vectors, then use them as the input vectors of the artificial neural network(ANN) to classify the lung sound types. Extensive experimental results show that this feature extraction method has convincing recognition efficiency although not yet good enough for clinical use.