-
Technol Health Care · Jun 1998
Classification of respiratory sounds based on wavelet packet decomposition and learning vector quantization.
- L Pesu, P Helistö, E Ademovic, J C Pesquet, A Saarinen, and A R Sovijärvi.
- Laboratory of Biomedical Engineering, Helsinki University of Technology, Finland.
- Technol Health Care. 1998 Jun 1;6(1):65-74.
AbstractIn this paper, a wavelet packet-based method is used for detection of abnormal respiratory sounds. The sound signal is divided into segments, and a feature vector for classification is formed using the results of the search for the best wavelet packet decomposition. The segments are classified as containing crackles, wheezes or normal lung sounds, using Learning Vector Quantization. The method is tested using a small set of real patient data which was also analysed by an expert observer. The preliminary results are promising, although not yet good enough for clinical use.
Notes
Knowledge, pearl, summary or comment to share?You can also include formatting, links, images and footnotes in your notes
- Simple formatting can be added to notes, such as
*italics*,_underline_or**bold**. - Superscript can be denoted by
<sup>text</sup>and subscript<sub>text</sub>. - Numbered or bulleted lists can be created using either numbered lines
1. 2. 3., hyphens-or asterisks*. - Links can be included with:
[my link to pubmed](http://pubmed.com) - Images can be included with:
 - For footnotes use
[^1](This is a footnote.)inline. - Or use an inline reference
[^1]to refer to a longer footnote elseweher in the document[^1]: This is a long footnote..