A two stage recognition method of lung sounds based on multiple features

A two stage recognition method combined multiple kind of features was proposed to overcome the limitation of single kind of feature in the lung sound recognition. The method combines the improved Welch power spectrum, Mel cepstrum coefficients and the linear prediction cepstral coefficients based on...

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Bibliographic Details
Published inJournal of intelligent & fuzzy systems Vol. 37; no. 3; pp. 3581 - 3592
Main Authors Shi, Lukui, Du, Weifang, Li, Zhanru
Format Journal Article
LanguageEnglish
Published Amsterdam IOS Press BV 01.01.2019
Subjects
Accuracy
Acoustics
Asthma
Classifiers
Feature recognition
Linear prediction
Lungs
Pneumonia
Segmentation
Wavelet analysis
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Summary:A two stage recognition method combined multiple kind of features was proposed to overcome the limitation of single kind of feature in the lung sound recognition. The method combines the improved Welch power spectrum, Mel cepstrum coefficients and the linear prediction cepstral coefficients based on the wavelet decomposition. In the first stage, pneumonia samples and asthma samples are firstly taken as the abnormal category. Then a two-class classifier based on random forests is trained to identify the normal samples and the abnormal samples. In the second stage, a classifier based on random forests is trained to recognize pneumonia and asthma from the samples classified as the abnormal samples in the first stage. To further improve the accuracy, a multi granularity cycle segmentation method of lung sounds was presented, which is based on the short time zero crossing rate. It can better segment lung sounds. Experimental results showed that the proposed method greatly improved the recognition accuracy, especially for improving the accuracy of pneumonia and asthma.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-181339