Limits...
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.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4499654&req=5

fig11: Experimental results, test error.

Mentions: The methodology of testing is shown in Figure 10. This means that if the test pattern S1, S2, S3, or S4 appeared in ECG waveform with probability pS ≥ p (p = 70%), thus it was predicted to be “a sick person.” Then we work only with the remaining time series. If the test pattern H1, H2, H3, or H4 appeared in ECG waveform with probability pH ≥ p (p = 70%), thus it was predicted to be “a healthy person.” In all other cases, the ECG time series was unspecified. We examined a total of 20 data sets. Each of them contains 101 values that assign 92 possible patterns. The whole number of examined patterns is 1840. The graph in Figure 11 demonstrates a summary of results, where “sick persons” represent patterns S1–S4 and “healthy persons” represent patterns H1–H4. The resulting prediction is based on the methodology; see Figure 10.


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.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig11: Experimental results, test error.
Mentions: The methodology of testing is shown in Figure 10. This means that if the test pattern S1, S2, S3, or S4 appeared in ECG waveform with probability pS ≥ p (p = 70%), thus it was predicted to be “a sick person.” Then we work only with the remaining time series. If the test pattern H1, H2, H3, or H4 appeared in ECG waveform with probability pH ≥ p (p = 70%), thus it was predicted to be “a healthy person.” In all other cases, the ECG time series was unspecified. We examined a total of 20 data sets. Each of them contains 101 values that assign 92 possible patterns. The whole number of examined patterns is 1840. The graph in Figure 11 demonstrates a summary of results, where “sick persons” represent patterns S1–S4 and “healthy persons” represent patterns H1–H4. The resulting prediction is based on the methodology; see Figure 10.

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