Affiliations: [a] School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
| [b] Hebei Province Bigdata Computation Key Laboratory, Tianjin, China
Correspondence:
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Corresponding author. Lukui Shi, School of Artificial Intelligence, Hebei University of Technology, 300401, Tianjin. E-mail: [email protected].
Abstract: 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.
Keywords: Lung sound, random forest, Welch power spectrum, Mel cepstrum coefficient, linear prediction cepstral coefficient
