Predictions of blood flow varations based on artificial neural network and doppler signal

• Type: Article in International Conference Proceedings Book
• Date: 2012

Authors

Carlos C. António (Author) FEUP; Catarina F. Castro (Author) FEUP; Luísa C. Sousa (Author) FEUP; Rui Chaves (Author) Other

Conference proceedings International

Title: Proceedings ICEM15 - 15th International Conference on Experimental Mechanics

Pages: 1089-1090

ICEM15 - 15th International Conference on Experimental Mechanics

Porto, 22-27 July 2012

Indexing

ISI Proceedings

ISI Web of Knowledge - 0 Citations

Scientific classification

FOS: Engineering and technology > Other engineering and technologies

CORDIS: Technological sciences ; Health sciences

Other information

Authenticus ID: P-005-K2E

Abstract (EN): In this approach a surrogate model based on Artificial Neural Networks (ANN), Doppler signal and Finite Element Method (FEM) is developed aiming to investigate the carotid arterial conditions. Doppler measurements of blood flow velocities are used as input values in Artificial Neural Network developments based on supervised learning. The network is trained using a Genetic Algorithm (GA) supported by an elitist strategy to obtain the optimal ANN topology. The ANN is cross validated and tested with carotid arterial Doppler signal and FEM analysis. The use of this ANN surrogate model for classification of carotid arterial diagnosis problems is proposed.

Language: English

Type (Professor's evaluation): Scientific

Notes: Livro de resumos: Symp_17 pp.1089-1090; CD-ROM: paper ref 3797 com 9 páginas

No. of pages: 2