| Autor: |
Sodre B, Rosa M, Dassie-Leite A |
| Jazyk: |
angličtina |
| Zdroj: |
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2017 Jul; Vol. 2017, pp. 4590-4593. |
| DOI: |
10.1109/EMBC.2017.8037878 |
| Abstrakt: |
Nineteen acoustical measurements were related to 23 larynx conditions by artificial neural networks (ANNs) and principal component analysis. An exhaustive analysis (combining all possible sets of acoustical measurements as ANN inputs) showed a performance of 99.4% for accuracy and 90.3% for sensitivity and specificity in classifying voice signals into normal and non-normal larynx conditions. In the case of individual larynx condition identification, the general sensitivity drop significantly (6.4%), although some conditions were better identified (including "vocal nodules" and "cysts") then others, reaching 99.8% of sensitivity. We also identified the acoustical measurements that produced the best classification results. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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