Name
Abstract | ||
|---|---|---|
Biosignal recordings are widely used in the medical environment to support the evaluation and the diagnosis of pathologies. Nevertheless, the main difficulty lies in the non-stationary behavior of the biosignals, difficulting the obtention of patterns characterizing the changes in physiological or pathological states. Thus, the obtention of the stationary and non-stationary components of a biosignal is still an open issue. This work proposes a methodology to detect time-homogeneities based on time-frequency analysis with aim to extract the non-stationary behavior of the biosignal. Results show an increase in the stationarity and in the distance between classes of the reconstructions from the enhanced time-frequency representations. The stationary components extracted with the proposed approach can be used to solve biosignal classification problems. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1155/2012/951213 | 2012 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Keywords | Field | DocType |
Time-evolving Latent Variable Decomposition, Multivariate locally stationary time series | Visibility,Computer science,Computer network,Sonar,Point of interest,Artificial neural network,Wireless sensor network,Doors,Actuator | Journal |
Volume | ISSN | Citations |
2012 | 1557-170X | 13 |
PageRank | References | Authors |
0.72 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| José V. Martí | 1 | 18 | 2.64 |
| J. Sales | 2 | 44 | 7.28 |
| Raúl Marín | 3 | 93 | 14.55 |
| Ernesto Jiménez-Ruiz | 4 | 1120 | 84.14 |