A Diagnostic Model for Identification of Myocardial Infarction from Electrocardiography Signals

Yıl 2017, , 132 - 139, 26.12.2017

Aykut Diker , Zafer Cömert Engin Avcı

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

Cited By: 15

Öz

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.

Anahtar Kelimeler

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

Kaynakça

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