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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Dellandrea, E. Makris, P. Boiron, M. Vincent, N. |
| Copyright Year | 2003 |
| Description | Author affiliation: Lab. d'Informatique, Univ. of Tours, France (Dellandrea, E.; Makris, P.) |
| Abstract | We present in this paper a comparison of audio signal codings that we have developed in order to study xiphoidal sounds. These sounds are produced by the lower oesophageal sphincter whose dysfonctionnement can be responsible for the gastro-eosophageal reflux phenomenon. Our goal is to extract pertinent information from audio signals in order to characterize the pathology of the patients and its intensity, which is generally realized by invasive and traumatic methods such as radiological and manometric investigations. Several codings of audio signals are presented and compared. They are based on different representations of signals: temporal, frequential and time-scale representations. Zipf and inverse Zipf analyses are then performed. They allow the extraction of pertinent primitives, not available from standard signal processing methods. Finally, a clustering step is realized in order to bring to the fore clusters, linked to pathology characteristics. Two clustering methods have been used: a linear discriminant analysis, and a neural network clustering method. |
| Starting Page | 9 |
| Ending Page | 12 |
| File Size | 426278 |
| Page Count | 4 |
| File Format | |
| ISBN | 0780376676 |
| DOI | 10.1109/ITAB.2003.1222401 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2003-04-24 |
| Publisher Place | United Kingdom |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Signal analysis Data mining Pathology Medical diagnostic imaging Performance analysis Signal processing Clustering methods Information analysis Medical signal detection Acoustic signal detection |
| Content Type | Text |
| Resource Type | Article |
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