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Mendeley readers
Attention Score in Context
| 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
The data shown below were compiled from readership statistics for 128 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 | 124 | 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 | 21 | 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 | 8 | 6% |
| Unknown | 33 | 26% |
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
#8,544,090
of 25,394,764 outputs
Outputs from Computers in Biology & Medicine
#696
of 2,771 outputs
Outputs of similar age
#70,574
of 206,673 outputs
Outputs of similar age from Computers in Biology & Medicine
#8
of 17 outputs
Altmetric has tracked 25,394,764 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 2,771 research outputs from this source. They receive a mean Attention Score of 3.6. This one has gotten more attention than average, scoring higher than 62% 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 206,673 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% 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.