Supervised learning methods for pathological arterial pulse wave differentiation: A SVM and neural networks approach.

Journal: International journal of medical informatics

PMID: 29195703

Abstract

OBJECTIVE: The main goal of this study was to develop an automatic method based on supervised learning methods, able to distinguish healthy from pathologic arterial pulse wave (APW), and those two from noisy waveforms (non-relevant segments of the signal), from the data acquired during a clinical examination with a novel optical system.

Authors

Joana S Paiva

Physics Department, University of Coimbra, Coimbra, Portugal.
João Cardoso

Physics Department, Instrumentation Center, University of Coimbra, Rua Larga, 3004-516, Coimbra, Portugal.
Tânia Pereira

Physics Department, Instrumentation Center, University of Coimbra, Rua Larga, 3004-516, Coimbra, Portugal. taniapereira@lei.fis.uc.pt.

Keywords

Algorithms Arteries Case-Control Studies Equipment Design Humans Neural Networks, Computer Pulsatile Flow Pulse Wave Analysis Signal Processing, Computer-Assisted Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (29195703)

Supervised learning methods for pathological arterial pulse wave differentiation: A SVM and neural networks approach.

Abstract

Authors

Keywords

External Resources

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