A Diagnostic Model for Identification of Myocardial Infarction from Electrocardiography Signals

Year 2017, Volume: 7 Issue: 2, 132 - 139, 26.12.2017

Aykut Diker , Zafer Cömert Engin Avcı

https://doi.org/10.17678/beuscitech.344953

Cited By: 15

Abstract

Electrocardiography (ECG) is a
useful test used commonly to observe the electrical activity of a heart.
Recently, a growing relationship has been observed between diagnosis of a
disease and using of machine learning techniques. In this scope, a diagnostic application
model designed based on a combination of Recursive Feature Eliminator (RFE) and
two different machine learning algorithms called as -nearest
neighbors (-NN)
and artificial neural network (ANN) is proposed for classification of ECG
signals in this study. The experiments performed on an open-access ECG
database. Firstly, the signals were passed a pre-processing step. Then, several
diagnostic features from morphological and statistical domains were extracted
from the signals. In the last stage of the analysis, RFE algorithm covering
10-fold cross-validation and the mentioned machine learning techniques were
employed to separate abnormal Myocardial Infarction (MI) samples from normal.
The promising results as accuracy of 80.60%, sensitivity of 86.58% and
specificity of 64.71% were achieved. The validation of the contribution was
checked by comparing the performances of both -NN
and ANN to related works. Consequently, the proposed diagnostic model ensured
an automatic and robust ECG signal classification model.

Keywords

Biomedical Signal Processing, Decision Support System, Machine Learning, Electrocardiography

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