Name
Title | ||
|---|---|---|
Dimensionality reduction oriented toward the feature visualization for ischemia detection. |
Abstract | ||
|---|---|---|
An effective data representation methodology on high-dimension feature spaces is presented, which allows a better interpretation of subjacent physiological phenomena (namely, cardiac behavior related to cardiovascular diseases), and is based on search criteria over a feature set resulting in an increase in the detection capability of ischemic pathologies, but also connecting these features with the physiologic representation of the ECG. The proposed dimension reduction scheme consists of three levels: projection, interpretation, and visualization. First, a hybrid algorithm is described that projects the multidimensional data to a lower dimension space, gathering the features that contribute similarly in the meaning of the covariance reconstruction in order to find information of clinical relevance over the initial training space. Next, an algorithm of variable selection is provided that further reduces the dimension, taking into account only the variables that offer greater class separability, and finally, the selected feature set is projected to a 2-D space in order to verify the performance of the suggested dimension reduction algorithm in terms of the discrimination capability for ischemia detection. The ECG recordings used in this study are from the European ST-T database and from the Universidad Nacional de Colombia database. In both cases, over 99% feature reduction was obtained, and classification precision was over 99% using a five-nearest-neighbor classifier (5-NN). |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TITB.2009.2016654 | IEEE Transactions on Information Technology in Biomedicine |
Keywords | DocType | Volume |
selected feature set,feature visualization,suggested dimension reduction algorithm,initial training space,ecg recording,feature reduction,dimensionality reduction,hybrid algorithm,high-dimension feature space,2-d space,ischemia detection,lower dimension space,proposed dimension reduction scheme,relevance,cluster analysis,feature extraction,pathology,multidimensional systems,wavelet transform,data visualisation,feature selection,multidimensional analysis,data visualization,covariance analysis,dimension reduction,feature space,signal reconstruction,cardiovascular system,algorithms,data representation,variable selection | Journal | 13 |
Issue | ISSN | Citations |
4 | 1558-0032 | 4 |
PageRank | References | Authors |
0.50 | 14 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Edilson Delgado-Trejos | 1 | 19 | 6.10 |
| Alexandre Perera-Lluna | 2 | 26 | 5.74 |
| Montserrat Vallverdú-Ferrer | 3 | 4 | 0.50 |
| Peré Caminal-Magrans | 4 | 4 | 0.50 |
| German Castellanos-Dominguez | 5 | 258 | 51.21 |