title: ECG Prediction Based on Classification via Neural Networks and Linguistic Fuzzy Logic Forecaster.
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.
authors.affiliation: University of Ostrava, 30 Dubna 22, 70103 Ostrava, Czech Republic.
authors.forename: Eva
authors.initials: E
authors.lastname: Volna
authors.affiliation: University of Ostrava, 30 Dubna 22, 70103 Ostrava, Czech Republic.
authors.forename: Martin
authors.initials: M
authors.lastname: Kotyrba
authors.affiliation: University of Ostrava, 30 Dubna 22, 70103 Ostrava, Czech Republic.
authors.forename: Hashim
authors.initials: H
authors.lastname: Habiballa
mesh_terms: D000200:Action Potentials; D000465:Algorithms; D004562:Electrocardiography / Q000379:methods*; D017143:Fuzzy Logic; D006801:Humans; D008037:Linguistics; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors; D058067:Wavelet Analysis