Name
Abstract | ||
|---|---|---|
One of the most common reasons for children consulting a general practitioner is respiratory morbidity. For healthcare providers, listening to breath sounds is an important diagnose method for respiration system diseases. However, parents and caregivers dont always have the required knowledge and experience to identify children's various breath sounds. Also, it is extremely hard to obtain feedback from young children about their physical conditions. Therefore, it is necessary to provide a tool to monitor young children healthy condition. In this paper, we propose novel approaches to recognize and classify children's breath sounds. We obtain and analyze audio features of young children's breath sound signals in time and frequency domains, based on these features, classify breath signals to specific breath segments. Nearest neighborhood (NN) and artificial neural network (ANN) were used for pattern recognition and classification. Clinical data were used to design and verify the proposed approaches. Experiments show that the proposed approaches offer accurate results. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICC.2019.8761292 | IEEE International Conference on Communications |
Field | DocType | ISSN |
Breath sound,Computer science,Active listening,Speech recognition,Real-time computing,Extremely hard,Artificial neural network,Respiratory morbidity | Conference | 1550-3607 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lichuan Liu | 1 | 5 | 2.80 |
| Wei Li | 2 | 436 | 140.67 |
| Chao Jiang | 3 | 1 | 0.71 |