@misc{10481/55355,
year = {2018},
month = {11},
url = {http://hdl.handle.net/10481/55355},
abstract = {Heart disease is currently one of the leading causes of death in developed countries. The
electrocardiogram is an important source of information for identifying these conditions, therefore,
becomes necessary to seek an advanced system of diagnosis based on these signals. In this paper
we used samples of electrocardiograms of MIT-related database with ten types of pathologies and
a rate corresponding to normal (healthy patient), which are processed and used for extraction from
its two branches of a wide range of features. Next, various techniques have been applied to feature
selection based on genetic algorithms, principal component analysis and mutual information. To
carry out the task of intelligent classification, 3 different scenarios have been considered. These
techniques allow us to achieve greater efficiency in the classification methods used, namely support
vector machines (SVM) and decision trees (DT) to perform a comparative analysis between them.
Finally, during the development of this contribution, the use of very non-invasive devices (2 channel
ECG) was analyzed, we could practically classify them as wearable, which would not need
interaction by the user, and whose energy consumption is very small to extend the average life of
the user been on it.},
organization = {The publication of this article was supported by projects TIN2015-71873-R and P12-TIC 2082.},
publisher = {MDPI},
keywords = {Heart disease},
keywords = {Electrocardiograms},
keywords = {Intelligent classifier},
title = {Wearable Intelligent System for the Diagnosis of Cardiac Diseases Working in Real Time and with Low Energy Cost},
author = {Valenzuela Cansino, Olga and Prieto Campos, Beatriz and Delgado-Marquez, Elvira and Pomares Cintas, Héctor Emilio and Rojas Ruiz, Ignacio},
}