A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
IEEE Computational Science & Engineering
A reliable probabilistic sleep stager based on a single EEG signal
Artificial Intelligence in Medicine
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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.