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ECG Prediction Based on Classification via Neural Networks and Linguistic Fuzzy Logic Forecaster.

Volna E, Kotyrba M, Habiballa H - ScientificWorldJournal (2015)

Bottom Line: All experimental results from both of the proposed classifiers are mutually compared in the conclusion.We also experimented with the new method of time series transparent prediction based on fuzzy transform with linguistic IF-THEN rules.Preliminary results show interesting results based on the unique capability of this approach bringing natural language interpretation of particular prediction, that is, the properties of time series.

View Article: PubMed Central - PubMed

Affiliation: University of Ostrava, 30 Dubna 22, 70103 Ostrava, Czech Republic.

ABSTRACT
The paper deals with ECG prediction based on neural networks classification of different types of time courses of ECG signals. The main objective is to recognise normal cycles and arrhythmias and perform further diagnosis. We proposed two detection systems that have been created with usage of neural networks. The experimental part makes it possible to load ECG signals, preprocess them, and classify them into given classes. Outputs from the classifiers carry a predictive character. All experimental results from both of the proposed classifiers are mutually compared in the conclusion. We also experimented with the new method of time series transparent prediction based on fuzzy transform with linguistic IF-THEN rules. Preliminary results show interesting results based on the unique capability of this approach bringing natural language interpretation of particular prediction, that is, the properties of time series.

No MeSH data available.


Related in: MedlinePlus

The cardiac action potentials.
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fig2: The cardiac action potentials.

Mentions: ECG scanning has its own rules, which are in accordance with the laws of physics. The heart irritation spreads in all directions. In the case that the depolarisation spreads towards the electrode, which is placed on the body surface, a positive deflection is recorded on an ECG monitor. A negative deflection is recorded at the opposite end of the body. The ECG waveform is written with a chart speed of 25 mm·s−1. An algorithm describing the curve goes in the following steps. First, we evaluate the shape and rhythm of ventricular complexes or atrial, which can be either regular or irregular. Then we evaluate the frequency of ventricular complexes and atrial fibrillations. Contraction of each muscle of the human body (and thus the heart as well) is associated with electrical changes called depolarization, which can be detected by electrodes. The heart contains two basic types of cells: myocardial cells, which are responsible for generating the pressure necessary to pump blood throughout the body, and conduction cells, which are responsible for rapidly spreading electrical signals to the myocardial cells in order to coordinate pumping. A graph of an action potential of a muscle of cardiac cells is shown in Figure 2.


ECG Prediction Based on Classification via Neural Networks and Linguistic Fuzzy Logic Forecaster.

Volna E, Kotyrba M, Habiballa H - ScientificWorldJournal (2015)

The cardiac action potentials.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4499654&req=5

fig2: The cardiac action potentials.
Mentions: ECG scanning has its own rules, which are in accordance with the laws of physics. The heart irritation spreads in all directions. In the case that the depolarisation spreads towards the electrode, which is placed on the body surface, a positive deflection is recorded on an ECG monitor. A negative deflection is recorded at the opposite end of the body. The ECG waveform is written with a chart speed of 25 mm·s−1. An algorithm describing the curve goes in the following steps. First, we evaluate the shape and rhythm of ventricular complexes or atrial, which can be either regular or irregular. Then we evaluate the frequency of ventricular complexes and atrial fibrillations. Contraction of each muscle of the human body (and thus the heart as well) is associated with electrical changes called depolarization, which can be detected by electrodes. The heart contains two basic types of cells: myocardial cells, which are responsible for generating the pressure necessary to pump blood throughout the body, and conduction cells, which are responsible for rapidly spreading electrical signals to the myocardial cells in order to coordinate pumping. A graph of an action potential of a muscle of cardiac cells is shown in Figure 2.

Bottom Line: All experimental results from both of the proposed classifiers are mutually compared in the conclusion.We also experimented with the new method of time series transparent prediction based on fuzzy transform with linguistic IF-THEN rules.Preliminary results show interesting results based on the unique capability of this approach bringing natural language interpretation of particular prediction, that is, the properties of time series.

View Article: PubMed Central - PubMed

Affiliation: University of Ostrava, 30 Dubna 22, 70103 Ostrava, Czech Republic.

ABSTRACT
The paper deals with ECG prediction based on neural networks classification of different types of time courses of ECG signals. The main objective is to recognise normal cycles and arrhythmias and perform further diagnosis. We proposed two detection systems that have been created with usage of neural networks. The experimental part makes it possible to load ECG signals, preprocess them, and classify them into given classes. Outputs from the classifiers carry a predictive character. All experimental results from both of the proposed classifiers are mutually compared in the conclusion. We also experimented with the new method of time series transparent prediction based on fuzzy transform with linguistic IF-THEN rules. Preliminary results show interesting results based on the unique capability of this approach bringing natural language interpretation of particular prediction, that is, the properties of time series.

No MeSH data available.


Related in: MedlinePlus