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Detekce fibrilace síní v EKG / ECG based atrial fibrillation detection

This diploma thesis deals with detection of atrial fibrillation from HRV, classification of Poincare map and in the end the divide into two groups, one with detected atrial fibrillation and one not. The result is the decision on which variables are statistically significant for the identification of atrial fibrillations and which are not, and classification of the ECG signals with Bayes and Lavenberg-Marquardt neural networks. Bayes neural network with 23 neurons in hidden layer is best with F1 measure = 83,6 %, Sensitivity = 88,1 % and Specificity 94,5 %.

Identiferoai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:403760
Date January 2019
CreatorsPlch, Vít
ContributorsKolářová, Jana, Ronzhina, Marina
PublisherVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií
Source SetsCzech ETDs
LanguageCzech
Detected LanguageEnglish
Typeinfo:eu-repo/semantics/masterThesis
Rightsinfo:eu-repo/semantics/restrictedAccess

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