Name
Abstract | ||
|---|---|---|
Biosignals analysis has become widespread, upstaging their typical use in clinical settings. Electrocardiography (ECG) plays a central role in patient monitoring as a diagnosis tool in today's medicine and as an emerging biometric trait. In this paper we adopt a consensus clustering approach for the unsupervised analysis of an ECG-based biometric records. This type of analysis highlights natural groups within the population under investigation, which can be correlated with ground truth information in order to gain more insights about the data. Preliminary results are promising, for meaningful clusters are extracted from the population under analysis. |
Year | Venue | Keywords |
|---|---|---|
2014 | Signal Processing Conference | electrocardiography,medical signal processing,patient monitoring,pattern clustering,ECG analysis,ECG-based biometric records,biometric trait,biosignal analysis,consensus clustering approach,diagnosis tool,electrocardiography,extracted clusters,patient monitoring,ECG analysis,ECG-based biometrics,consensus clustering,evidence accumulation |
DocType | ISSN | Citations |
Conference | 2076-1465 | 0 |
PageRank | References | Authors |
0.34 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| André Lourenço | 1 | 312 | 45.33 |
| Carlos Carreiras | 2 | 41 | 6.96 |
| Samuel Rota Bulò | 3 | 0 | 0.34 |
| Ana L. N. Fred | 4 | 1317 | 195.30 |