An acoustical respiratory phase segmentation algorithm using genetic approach.
This paper proposes a robust and fully automated respiratory phase segmentation method using single channel tracheal breath sounds (TBS) recordings of different types. The estimated number of respiratory segments in a TBS signal is firstly obtained based on noise estimation and nonlinear mapping. Re...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 47; no. 9; pp. 941 - 954 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Sep2009
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| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | This paper proposes a robust and fully automated respiratory phase segmentation method using single channel tracheal breath sounds (TBS) recordings of different types. The estimated number of respiratory segments in a TBS signal is firstly obtained based on noise estimation and nonlinear mapping. Respiratory phase boundaries are then located through the generations of multi-population genetic algorithm by introducing a new evaluation function based on sample entropy (SampEn) and a heterogeneity measure. The performance of the proposed method is analyzed for single channel TBS recordings of various types. An overall respiratory phase segmentation accuracy is found to be 12 +/- 5 ms for normal TBS and 21 +/- 9 ms for adventitious sounds. The results show the robustness and effectiveness of the proposed segmentation method. The proposed method has been a successful attempt to solve the clinical application challenge faced by the existing phase segmentation methods in terms of respiratory dysfunctions. |
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