Title: Wheeze detecting method based on spectrogram entropy analysis

Author: LI Jiarui; HONG Ying

Affiliation: Institute of Acoustics, Chinese Academy of Sciences, et al.

Abstract: In order to eliminate the subjectivity of wheeze diagnosis and improve the accuracy of objective detecting methods, this paper introduces a wheeze detecting method based on spectrogram entropy analysis. This algorithm mainly comprises three steps which are preprocessing, features extracting and wheeze detecting based on support vector machine (SVM). Herein, the preprocessing consists of the short-time Fourier transform (STFT) decomposition and detrending. The features are extracted from the entropy of spectrograms. The step of detrending makes the difference of the features between wheeze and normal lung sounds more obvious. Moreover, compared with the method whose decision is based on the empirical threshold, there is no uncertain detecting result any more. Results of two testing experiments show that the detecting accuracy (AC) are 97.1{\%} and 95.7{\%}, respectively, which proves that the proposed method could be an efficient way to detect wheeze.