Abstract
A novel method is presented to determine the features of heart sounds. Feature vectors are formed by using the wavelet detail and approximation coefficients at the second and the sixth decomposition levels, respectively. Decision making is performed in four stages: Segmentation of the first and second heart sounds, normalization process, feature extraction, and classification by the artificial neural network. In this study, nine different heart sounds are classified.
| Original language | English |
|---|---|
| Pages (from-to) | 128-129 |
| Number of pages | 2 |
| Journal | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings |
| Volume | 1 |
| Publication status | Published - 2002 |
| Event | Proceedings of the 2002 IEEE Engineering in Medicine and Biology 24th Annual Conference and the 2002 Fall Meeting of the Biomedical Engineering Society (BMES / EMBS) - Houston, TX, United States Duration: 23 Oct 2002 → 26 Oct 2002 |
Keywords
- Classification
- Heart sounds
- Wavelet transform