Machine learning approaches to estimate simulated cardiac ejection fraction from electrical impedance tomography

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 10022 LNAI, Issue , 2016, Pages 235-246

  Fonseca, Tales L ^a   Goliatt, Leonardo ^a   Campos, Luciana C D ^a   Bastos, Flávia S ^a   Barra, Luis Paulo S ^a   Dos Santos, Rodrigo W ^a  

^a FEDERAL UNIVERSITY OF JUIZ DE FORA   (Brazil)

Author keywords

Ejection fraction; Electric impedance tomography; Machine learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; CARDIOLOGY; ELECTRIC IMPEDANCE; ELECTRIC IMPEDANCE MEASUREMENT; HEART; LEARNING SYSTEMS; REGRESSION ANALYSIS;

DIAGNOSTIC TESTS; EJECTION FRACTION; ELECTRICAL IMPE DANCE TOMOGRAPHY (EIT); ELECTRICAL IMPEDANCE TOMOGRAPHY; GAUSSIAN PROCESS; MACHINE LEARNING APPROACHES; MULTIVARIATE ADAPTIVE REGRESSION SPLINES; SUPPORT VECTOR REGRESSION (SVR);

ELECTRIC IMPEDANCE TOMOGRAPHY;

EID: 84994171948     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-47955-2_20     Document Type: Conference Paper

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