Abstract
In this paper, a classification method for respiratory sounds (RSs) in patients with asthma and in healthy subjects is presented. Wavelet transform is applied to a window containing 256 samples. Elements of the feature vectors are obtained from the wavelet coefficients. The best feature elements are selected by using dynamic programming. Grow and Learn (GAL) neural network, Kohonen network and multi-layer perceptron (MLP) are used for the classification. It is observed that RSs of patients (with asthma) and healthy subjects are successfully classified by the GAL network.
| Original language | English |
|---|---|
| Pages (from-to) | 567-580 |
| Number of pages | 14 |
| Journal | International Journal of Pattern Recognition and Artificial Intelligence |
| Volume | 17 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - Jun 2003 |
Keywords
- Artificial neural network
- Classification of biomedical signals
- Pattern recognition
- Respiratory sounds
- Wavelet transform
