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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

Experimental results, test error for healthy/sick persons.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig5: Experimental results, test error for healthy/sick persons.

Mentions: The test set consisted of 20 samples (11 health and 9 sick persons) that were not included in the training set. The summary results for this type of experiment are shown in a graph in Figure 5. For clarity, the results of testing are given in percentage. The average test error was 0.194. A healthy population was detected with an average error of 0.263 and sick population with an average error of 0.109.


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

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

Experimental results, test error for healthy/sick persons.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig5: Experimental results, test error for healthy/sick persons.
Mentions: The test set consisted of 20 samples (11 health and 9 sick persons) that were not included in the training set. The summary results for this type of experiment are shown in a graph in Figure 5. For clarity, the results of testing are given in percentage. The average test error was 0.194. A healthy population was detected with an average error of 0.263 and sick population with an average error of 0.109.

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