Name
Title | ||
|---|---|---|
Supervised learning methods for pathological arterial pulse wave differentiation: A SVM and neural networks approach. |
Abstract | ||
|---|---|---|
•The arterial pulse pressure waveform (APW) provides an adequate description of the arterial system behaviour..•The development of techniques based on the automatic analysis of biomedical signals could be crucial for a reliable cardiovascular assessment.•An APW database comprising signals from 213 patients acquired with a novel optical system was used here.•Support Vector Machines (SVM) and Neural Networks were compared for differentiating between noisy waveforms, healthy and pathologic APWs.•SVM showed a higher accuracy possibly due to its ability to deal with the non-linearity and high-dimensionality degree of APW signal. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.ijmedinf.2017.10.011 | International Journal of Medical Informatics |
Keywords | Field | DocType |
Arterial pulse waveform,Morphologic features,Support vector machines,Neural network,Support vector machine recursive feature elimination | Structured support vector machine,Data mining,Computer science,Artificial intelligence,Artificial neural network,Wavelet,Time domain,Pattern recognition,Arterial pulse,Waveform,Support vector machine,Supervised learning,Machine learning | Journal |
Volume | ISSN | Citations |
109 | 1386-5056 | 1 |
PageRank | References | Authors |
0.41 | 24 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joana S. Paiva | 1 | 1 | 1.09 |
| João Cardoso | 2 | 10 | 7.92 |
| Tânia Pereira | 3 | 24 | 8.61 |