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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

Comparison of mean values of ECG waveforms for healthy/sick persons.
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Related In: Results  -  Collection


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fig4: Comparison of mean values of ECG waveforms for healthy/sick persons.

Mentions: The training set consisted of modified ECG waveforms. We used a backpropagation neural network with topology 101-10-1. The output unit represents a diagnose 0/1, a healthy/sick person. A smaller number of inputs would not be appropriate due to the nature of the ECG waveform. We use 34 ECG time series associated with sick persons and 36 ECG time series associated with healthy persons. 25 time series of each group were used as a training set and the rest as a test set. Figure 4 shows a comparison of mean values of ECG waveforms for healthy/sick persons. We used the backpropagation method [5, 6] for the adaptation with the following parameters: the learning rate value is 0.1 and momentum is 0. The conducted experimental studies also showed that training patterns are mixed randomly in each cycle of adaptation. This ensures their greater diversity which acts as a measure of system stability. Uniform system in a crisis usually collapses entirely, while system with such diversity of trained patterns remains functional despite of crisis of its individual parts. The condition of end of the adaptation algorithm specified the limit value of the overall network error, E < 0.1.


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

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

Comparison of mean values of ECG waveforms for healthy/sick persons.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig4: Comparison of mean values of ECG waveforms for healthy/sick persons.
Mentions: The training set consisted of modified ECG waveforms. We used a backpropagation neural network with topology 101-10-1. The output unit represents a diagnose 0/1, a healthy/sick person. A smaller number of inputs would not be appropriate due to the nature of the ECG waveform. We use 34 ECG time series associated with sick persons and 36 ECG time series associated with healthy persons. 25 time series of each group were used as a training set and the rest as a test set. Figure 4 shows a comparison of mean values of ECG waveforms for healthy/sick persons. We used the backpropagation method [5, 6] for the adaptation with the following parameters: the learning rate value is 0.1 and momentum is 0. The conducted experimental studies also showed that training patterns are mixed randomly in each cycle of adaptation. This ensures their greater diversity which acts as a measure of system stability. Uniform system in a crisis usually collapses entirely, while system with such diversity of trained patterns remains functional despite of crisis of its individual parts. The condition of end of the adaptation algorithm specified the limit value of the overall network error, E < 0.1.

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