Abstract
Recent advances in digital processing of biological signals have made it possible to incorporate more extensive signals, generating a large number of features that must be analyzed to carry out the detection, and thereby acting against the performance of the detection methods. This paper introduces a simple feature reduction method based on correlation that allows the incorporation of very extensive signals to the new biological signal detection algorithms. To test the proposed technique, it was applied to the detection of Functional Dyspepsia (FD) from the EGG signal, which is one of the most extensive signals in clinical medicine. After applying the proposed reduction to the wavelet transform coefficients extracted from the EGG signal, a neuronal network was used as a classifier for the wavelet transform coefficients obtained from the EGG traces. The results of the classifier achieved 78.6% sensitivity, and 92.9% specificity for a universe of 56 patients studied.
Keywords
- Hide Layer
- Discrete Wavelet Transformation
- Neuronal Network
- Wavelet Coefficient
- Functional Dyspepsia
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This work was supported by FONDECYT project Nº 1050082.
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Chacón, M., Jara, S., Defilippi, C., Madrid, A.M., Defilippi, C. (2005). A Simple Feature Reduction Method for the Detection of Long Biological Signals. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_45
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DOI: https://doi.org/10.1007/11578079_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
Online ISBN: 978-3-540-32242-9
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