Methodology of wavelet packet selection for event detection

Chendeb, Marwa; Khalil, Mohamad; Duchêne, Jacques

doi:10.1016/j.sigpro.2006.03.029

Cited by 16 publications

(15 citation statements)

References 21 publications

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“…In detection case, the decomposition level is limited to 3 because the goal was to choose the best packets for detection whatever the events [15]. To decide which packets were able to classify the events, the values Rv of the discrimination criterion were computed on each packet using equation 8 (there are 30 packets).…”

Section: Results On Classificationmentioning

confidence: 99%

“…The mother wavelet choice in the current work was based on the minimum delay induced by applying detection algorithms directly on the wavelet packet coefficients. The choice was made using all available EMG events, leading to the selection of the Symlet 5 wavelet [15]. This choice is probably due to the symmetrical shape of the associated filters.…”

Section: Discussionmentioning

confidence: 99%

“…The superimposed signals correspond to short potentials or artifacts which appear randomly throughout the signal, such as foetus motions, Alvarez waves and other superimposed events [2,3,15]. Alvarez waves appear during the first 30 weeks of the human pregnancy.…”

Section: Signal Compositionmentioning

confidence: 99%

“…In our last work [15] an algorithm of best basis for detection is proposed. It is based on the Kullback Leibler distance as a criterion to select the best WPs which show clearly the events.…”

Section: Detectionmentioning

confidence: 99%

“…After choosing the best basis, the detection algorithm DCS (Dynamic Cumulative Sum) is applied on every selected wavelet packet coefficients. After the delay correction and the change time fusion [15], the uterine events of the real signals are obtained.…”

Section: Detectionmentioning

confidence: 99%

See 4 more Smart Citations

Classification of non stationary signals using multiscale decomposition

Chendeb¹,

Khalil²,

Hewson³

et al. 2010

JBiSE

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The aim of this article is to develop an automatic algorithm for the classification of non stationary signals. The application context is to classify uterine electromyogram (EMG) events to prevent the onset of preterm birth. The idea is to discriminate between the events by allocating them to the physiological classes: contractions, foetus motions, Alvarez or Long Duration Low Frequency waves. Our method is based on the Wavelet Packet (WP) decomposition and the choice of a best basis for classification purpose. Before classification, there is a need to detect events in the recorded signals. The discrimination criterion is based on the calculation of the ratio between intra-class variance and total variance (sum of the intra-class and inter-class variances), calculated directly from the coefficients of the selected WP. We evaluated the performance of the algorithm on real signals by using the classification methods Neural Networks (NN) and Support Vector Machines (SVM). Subband energies of the best selected WP are used as effective features. The determined best basis is applicable to a wide range of uterine EMG signals from large range of patients. In most cases, more than 85% of events are well classified whatever the term of gestation.

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Section: Results On Classificationmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Signal Compositionmentioning

confidence: 99%

Section: Detectionmentioning

confidence: 99%

Section: Detectionmentioning

confidence: 99%

See 3 more Smart Citations

Classification of non stationary signals using multiscale decomposition

Chendeb¹,

Khalil²,

Hewson³

et al. 2010

JBiSE

Self Cite

View full text Add to dashboard Cite

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References

2018

Hybridization, Diagnostic and Prognostic of Proton Exchange Membrane Fuel Cells

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Ohmic Contacts on p‐GaN

Chen

Brewer

2015

Adv Elect Materials

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With the development of GaN-based optoelectronic devices, the need for p-GaN contacts with low resistivity, good thermal stability, and high transparency or refl ectivity has become more pressing. Various contact schemes to satisfy these requirements have been investigated in the past two decades. In this progress report, the main developments of contacts on p-GaN throughout these years are summarized. The primary focus is on the materials aspects of the contacts and the functional mechanisms related to their relevant properties.

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Methodology of wavelet packet selection for event detection

Cited by 16 publications

References 21 publications

Classification of non stationary signals using multiscale decomposition

Classification of non stationary signals using multiscale decomposition

References

Ohmic Contacts on p‐GaN

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