Ischemia classification via ECG using MLP neural networks

DOI

10.1080/18756891.2014.889498How to use a DOI?

Keywords

Classification, ECG, ischemia, MLP, DWT

Abstract

This paper proposes a two stage system based in neural network models to classify ischemia via ECG analysis. Two systems based on artificial neural network (ANN) models have been developed in order to discriminate inferolateral and anteroposterior ischemia from normal electrocardiogram (ECG) and other heart diseases. This method includes pre-processing and classification modules. ECG segmentation and wavelet transform were used as pre-processing stage to improve classical multilayer perceptron (MLP) network. A new set of about 800 ECG were collected from different clinics in order to create a new ECG Database to train ANN models. The best specificity of all models in the test phases was found as 88.49%, and the best sensitivity was obtained as 80.75%.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - J.I. Peláez
AU  - J.M. Doña
AU  - J.F. Fornari
AU  - G Serra
PY  - 2014
DA  - 2014/04/01
TI  - Ischemia classification via ECG using MLP neural networks
JO  - International Journal of Computational Intelligence Systems
SP  - 344
EP  - 352
VL  - 7
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2014.889498
DO  - 10.1080/18756891.2014.889498
ID  - Peláez2014
ER  -