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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

A backpropagation network architecture.
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fig1: A backpropagation network architecture.

Mentions: The backpropagation neural network architecture is a hierarchical design consisting of fully interconnected layers or rows of processing units (with each unit itself comprised of several individual processing elements). Backpropagation belongs to the class of mapping neural network architectures and therefore the information processing function that it carries out is the approximation of a bounded mapping or function f : A ⊂ Rn → Rm, from a compact subset A of n-dimensional Euclidean space to a bounded subset f[A] of m-dimensional Euclidean space, by means of training on examples (x1, z1), (x2, z2),…,(xk, zk)…. It will always be assumed that such examples of a mapping f are generated by selecting xk vectors randomly from A in accordance with a fixed probability density function p(x). The operational use to which the network is to be put after training is also assumed to involve random selections of input vectors x in accordance with p(x). The backpropagation architecture described in this paper is the basic, classical version (Figure 1). The backpropagation learning algorithm is composed of two procedures: (a) forward propagation of signals and (b) backpropagation weight training [5].


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

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

A backpropagation network architecture.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig1: A backpropagation network architecture.
Mentions: The backpropagation neural network architecture is a hierarchical design consisting of fully interconnected layers or rows of processing units (with each unit itself comprised of several individual processing elements). Backpropagation belongs to the class of mapping neural network architectures and therefore the information processing function that it carries out is the approximation of a bounded mapping or function f : A ⊂ Rn → Rm, from a compact subset A of n-dimensional Euclidean space to a bounded subset f[A] of m-dimensional Euclidean space, by means of training on examples (x1, z1), (x2, z2),…,(xk, zk)…. It will always be assumed that such examples of a mapping f are generated by selecting xk vectors randomly from A in accordance with a fixed probability density function p(x). The operational use to which the network is to be put after training is also assumed to involve random selections of input vectors x in accordance with p(x). The backpropagation architecture described in this paper is the basic, classical version (Figure 1). The backpropagation learning algorithm is composed of two procedures: (a) forward propagation of signals and (b) backpropagation weight training [5].

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