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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

Training patterns, their representation in used test sets.
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Related In: Results  -  Collection


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fig9: Training patterns, their representation in used test sets.

Mentions: In order to test the efficiency of the method, we applied the same set of data that we used in the previous experimental part. Outputs from the classifier produce sets of values that are assigned to each recognized training pattern in the given test time series. It is important to appreciate what can be considered as an effective criterion related to consensus of similarity. The proposed threshold resulting from our experimental study was determined at least p = 70%. Figure 9 shows a comparison of patterns, how were learned (S2, S3, H3 train) and how were recognized in test time series (S2, S3, H3 test). The neural network is able to discover some connections, which are almost imperceptible. Illustration of some recognized patterns that occur in ECG time series is shown in Figure 8. Outputs from the classifier carry a predictive character. The neural network determines if the time series belongs to a healthy or sick person on the basis of the recognised ECG patterns which appear in the time series history.


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

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

Training patterns, their representation in used test sets.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig9: Training patterns, their representation in used test sets.
Mentions: In order to test the efficiency of the method, we applied the same set of data that we used in the previous experimental part. Outputs from the classifier produce sets of values that are assigned to each recognized training pattern in the given test time series. It is important to appreciate what can be considered as an effective criterion related to consensus of similarity. The proposed threshold resulting from our experimental study was determined at least p = 70%. Figure 9 shows a comparison of patterns, how were learned (S2, S3, H3 train) and how were recognized in test time series (S2, S3, H3 test). The neural network is able to discover some connections, which are almost imperceptible. Illustration of some recognized patterns that occur in ECG time series is shown in Figure 8. Outputs from the classifier carry a predictive character. The neural network determines if the time series belongs to a healthy or sick person on the basis of the recognised ECG patterns which appear in the time series history.

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