Artificial neural networks improve diagnosis of acute myocardial infarction

Abstract Our paper introduces a new technique for diagnosis of various heart diseases without the need of highly experts to investigate the electrocardiogram (ECG). Using the same electrodes of the ECG machine, it will be able to transmit directly the electrical activity inside the heart to a moving picture. Our technique is based on artificial intelligence algorithm using artificial neural networks (ANN). Finding the trans-membrane potential (TMP) inside the heart from the body surface potential (BSP) is known as the inverse problem of ECG. To have a unique solution for the inverse problem the data used should be obtained from a forward model. A three dimensional (3-D) model of cellular activation whole heart embedded in torso is simulated and solved using COMSOL Multiphysics software. In our previous paper, one ANN succeeded in displaying the wave propagation on the surface of a normal heart. In this paper, we used a configuration of ANNs to display different cases of heart with myocardial infarction (MI). To check the system accuracy, eight MI cases with different sizes and locations in the heart are simulated in the forward model. This configuration proved to be highly accurate in displaying each MI case -size and location- presenting the infarction as an area with no electrical activity.

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Myocardial Infarction Prediction by Artificial Neural Networks

Annals of Internal Medicine ◽

10.7326/0003-4819-116-8-701 ◽

1992 ◽

Vol 116 (8) ◽

pp. 701 ◽

Cited By ~ 3

Author(s):

Alan P. Zelicoff

Keyword(s):

Myocardial Infarction ◽

Neural Networks ◽

Artificial Neural Networks ◽

Artificial Neural

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Follow-Up and Risk Assessment in Patients with Myocardial Infarction Using Artificial Neural Networks

Complexity ◽

10.1155/2017/8953083 ◽

2017 ◽

Vol 2017 ◽

pp. 1-8 ◽

Cited By ~ 7

Author(s):

Tatjana Gligorijević ◽

Zoran Ševarac ◽

Branislav Milovanović ◽

Vlado Đajić ◽

Marija Zdravković ◽

...

Keyword(s):

Myocardial Infarction ◽

Neural Networks ◽

Artificial Neural Networks ◽

Baroreflex Sensitivity ◽

Training Dataset ◽

Survival Prediction ◽

Classification Problems ◽

Risk Of Death ◽

Increased Risk ◽

Artificial Neural

Artificial neural networks (ANNs) are machine learning technique, inspired by the principles found in biological neurons. This technique has been used for prediction and classification problems in many areas of medical signal processing. The aim of this paper was to identify individuals with high risk of death after acute myocardial infarction using ANN. A training dataset for ANN was 1705 consecutive patients who underwent 24-hour ECG monitoring, short ECG analysis, noninvasive beat-to-beat heart-rate variability, and baroreflex sensitivity that were followed for 3 years. The proposed neural network classifier showed good performance for survival prediction: 88% accuracy, 81% sensitivity, 93% specificity, 0.85 F-measure, and area under the curve value of 0.77. These findings support the theory that patients with high sympathetic activity (reduced baroreflex sensitivity) have an increased risk of mortality independent of other risk factors and that artificial neural networks can indicate the individuals with a higher risk.

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Research on the use of artificial neural networks for the myocardial infarction diagnosis

Information Technology and Nanotechnology ◽

10.18287/1613-0073-2019-2416-158-164 ◽

2019 ◽

pp. 158-164

Author(s):

P I Katkov ◽

N S Davydov ◽

A G Khramov ◽

A N Nikonorov

Keyword(s):

Neural Network ◽

Myocardial Infarction ◽

Neural Networks ◽

Artificial Neural Networks ◽

Convolutional Neural Network ◽

The Neural Network ◽

University Of Technology ◽

Artificial Neural ◽

Leave One Out

In this paper, the use of artificial neural networks for the myocardial infarction diagnosis is investigated. For the analysis, 169 ECG records were taken from the database of the Massachusetts University of Technology, of which 80 correspond to healthy patients and 89 correspond to patients who have a myocardial infarction. Each signal has been preprocessed. The result of preprocessing each signal is a common segment consisting of 1000 samples. To detect myocardial infarction, a convolutional neural network consisting of two convolutional layers was used. For accuracy of the neural network leave-one-out crossvalidation was used. The best results of the experiments are obtained with the neural network for leads V1, V2, AVF.

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