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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

Machine Learning Approaches to Estimate Simulated Cardiac Ejection Fraction from Electrical Impedance Tomography

verfasst von : Tales L. Fonseca, Leonardo Goliatt, Luciana C. D. Campos, Flávia S. Bastos, Luis Paulo S. Barra, Rodrigo W. dos Santos

Erschienen in: Advances in Artificial Intelligence - IBERAMIA 2016

Verlag: Springer International Publishing

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Abstract

The ejection fraction (EF) is a parameter that represents the amount of blood pumped out of each ventricle in each cardiac cycle and can be used for analyzing the heart failure. There are several diagnostic tests to determine whether the person has heart failure but some are expensive tests and they do not allow obtaining continuous estimations of EF. However, use the Electrical Impedance Tomography (EIT) with Regression Models is an alternative to obtain continuous estimations of EF. The quality of EIT is that it allows a quick diagnosis about the heart’s health, combining low cost and high portability. This paper it proposed four regression models, using the electrical measures from EIT, to estimate the EF : Gaussian Processes (GP), Support Vector Regression (SVR), Elastic Net Regression (ENR) and Multivariate Adaptive Regression Splines (MARS). The overall evaluation of results show that all models achieved competitive results and the method SVR has produced better results than the others tested.

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Vorheriges Kapitel An Empirical Validation of Learning Schemes Using an Automated Genetic Defect Prediction Framework
Nächstes Kapitel Machine Learning Models for Early Dengue Severity Prediction
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Metadaten
Titel
Machine Learning Approaches to Estimate Simulated Cardiac Ejection Fraction from Electrical Impedance Tomography
verfasst von
Tales L. Fonseca
Leonardo Goliatt
Luciana C. D. Campos
Flávia S. Bastos
Luis Paulo S. Barra
Rodrigo W. dos Santos
Copyright-Jahr
2016
Buch
Advances in Artificial Intelligence - IBERAMIA 2016

Print ISBN: 978-3-319-47954-5

Electronic ISBN: 978-3-319-47955-2

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-47955-2_20