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Features extraction for signal classification based on Wigner-Ville distribution and mutual information criterion

Abstract : The presented method deals with the extraction of features for the classification of non-stationary signals, when the process is only described through training data. The features are determined using the Wigner-Ville distribution (WVD). Three kinds of features are researched: the energy, the temporal expectation and the frequential expectation of the WVD restricted to specific regions. The restriction of the WVD is obtained by applying on the WVD a bidimensional Gaussian window. Given a feature type and a center position of the window in the time-frequency plane, the window parameters are optimized to provide the most discriminant feature. The discriminant nature is measured using a mutual information criterion. This provides a measure of the class separability suitable with any distribution law, and assuming no specific structure of the final classifier. The procedure has been validated with a classification problem of sleep EEG signals.
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https://hal-utt.archives-ouvertes.fr/hal-02317787
Contributor : Jean-Baptiste Vu Van <>
Submitted on : Wednesday, October 16, 2019 - 12:34:57 PM
Last modification on : Thursday, October 17, 2019 - 1:27:39 AM

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Edith Grall-Maës, Pierre Beauseroy. Features extraction for signal classification based on Wigner-Ville distribution and mutual information criterion. International Symposium on Time-Frequency and Time-Scale Analysis, Oct 1998, Pittsburgh, United States. pp.589-592, ⟨10.1109/TFSA.1998.721493⟩. ⟨hal-02317787⟩

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