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Multi-level basis selection of wavelet packet decomposition tree for heart sound classification

Overview of attention for article published in Computers in Biology & Medicine, July 2013
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Mentioned by

patent
1 patent

Citations

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133 Dimensions

Readers on

mendeley
127 Mendeley
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Title
Multi-level basis selection of wavelet packet decomposition tree for heart sound classification
Published in
Computers in Biology & Medicine, July 2013
DOI 10.1016/j.compbiomed.2013.06.016
Pubmed ID
Authors

Fatemeh Safara, Shyamala Doraisamy, Azreen Azman, Azrul Jantan, Asri Ranga Abdullah Ramaiah

Abstract

Wavelet packet transform decomposes a signal into a set of orthonormal bases (nodes) and provides opportunities to select an appropriate set of these bases for feature extraction. In this paper, multi-level basis selection (MLBS) is proposed to preserve the most informative bases of a wavelet packet decomposition tree through removing less informative bases by applying three exclusion criteria: frequency range, noise frequency, and energy threshold. MLBS achieved an accuracy of 97.56% for classifying normal heart sound, aortic stenosis, mitral regurgitation, and aortic regurgitation. MLBS is a promising basis selection to be suggested for signals with a small range of frequencies.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 127 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Mexico 1 <1%
Colombia 1 <1%
Serbia 1 <1%
Unknown 123 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 25 20%
Student > Ph. D. Student 22 17%
Student > Bachelor 19 15%
Researcher 10 8%
Lecturer 6 5%
Other 20 16%
Unknown 25 20%
Readers by discipline Count As %
Engineering 59 46%
Computer Science 15 12%
Medicine and Dentistry 5 4%
Physics and Astronomy 4 3%
Neuroscience 4 3%
Other 7 6%
Unknown 33 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 September 2019.
All research outputs
#8,921,111
of 26,311,549 outputs
Outputs from Computers in Biology & Medicine
#753
of 2,933 outputs
Outputs of similar age
#71,879
of 208,365 outputs
Outputs of similar age from Computers in Biology & Medicine
#8
of 17 outputs
Altmetric has tracked 26,311,549 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,933 research outputs from this source. They receive a mean Attention Score of 3.7. This one has gotten more attention than average, scoring higher than 61% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 208,365 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.