A cough sound recognition algorithm based on time-frequency energy distribution.
- Author:
Yongsheng LIU
1
;
Zirong LI
;
Minghui DU
Author Information
1. Department of Electronic Engineer, South-China University of Technology, Guangzhou 510641, China. helloliuyongsheng@gmail.com
- Publication Type:Journal Article
- MeSH:
Algorithms;
Cough;
physiopathology;
Data Interpretation, Statistical;
Discriminant Analysis;
Humans;
Markov Chains;
Monitoring, Physiologic;
methods;
Pattern Recognition, Automated;
methods;
Sound
- From:
Journal of Biomedical Engineering
2009;26(5):953-958
- CountryChina
- Language:Chinese
-
Abstract:
The cough sound is a very important symptom of well over 100 kinds of diseases. Cough sound analysis can provide much information which is useful for diagnosing. Detecting the frequencies and intensity of cough can evaluate the efficiency of therapy quantitatively. In this paper, we put forward an algorithm for cough sound recognition. We first decompose signals with wavelet transform and calculate the normalized energy at each time-frequency point. Then we obtain the normalized energy distribution statistically. After that, we pick out the time-frequency points maximizing a certain discriminant measure of normalized energy distribution between cough sound and non-cough sound, and then we use the normalized energy belonging to these time-frequency points as the inputs of Linear discriminant analysis/Generalized singular value decomposition (LDA/GSVD) classifier. The experimental results show that the classification accuracies achieved by using the algorithm is about 85%, and the computation complexity is low.
