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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F15%3AA1601EEZ" target="_blank" >RIV/61988987:17310/15:A1601EEZ - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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 interestingresults based on the unique capability of this approach bringing natural language interpretation of particular prediction, that is, the properties of time series.

Název v anglickém jazyce

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

Popis výsledku anglicky

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 interestingresults based on the unique capability of this approach bringing natural language interpretation of particular prediction, that is, the properties of time series.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

THESCIENTIFICWORLDJO

ISSN

1537-744X

e-ISSN

—

Svazek periodika

2015

Číslo periodika v rámci svazku

205749

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

1-10

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-84937019532