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master's thesis
Metode za otkrivanje mikrovoltažnih elektrofizioloških potencijala srčanog mišića
Zagreb: University of Zagreb, Faculty of Science, 2024. urn:nbn:hr:217:329335
Glogar, Ivan
University of Zagreb
Faculty of Science
Department of Physics

Institutional repository: Repository of the Faculty of Science

Cite this document

Glogar, I. (2024). Metode za otkrivanje mikrovoltažnih elektrofizioloških potencijala srčanog mišića (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:329335

Glogar, Ivan. "Metode za otkrivanje mikrovoltažnih elektrofizioloških potencijala srčanog mišića." Master's thesis, University of Zagreb, Faculty of Science, 2024. https://urn.nsk.hr/urn:nbn:hr:217:329335

Glogar, Ivan. "Metode za otkrivanje mikrovoltažnih elektrofizioloških potencijala srčanog mišića." Master's thesis, University of Zagreb, Faculty of Science, 2024. https://urn.nsk.hr/urn:nbn:hr:217:329335

Glogar, I. (2024). 'Metode za otkrivanje mikrovoltažnih elektrofizioloških potencijala srčanog mišića', Master's thesis, University of Zagreb, Faculty of Science, accessed 23 December 2024, https://urn.nsk.hr/urn:nbn:hr:217:329335

Glogar I. Metode za otkrivanje mikrovoltažnih elektrofizioloških potencijala srčanog mišića [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2024 [cited 2024 December 23] Available at: https://urn.nsk.hr/urn:nbn:hr:217:329335

I. Glogar, "Metode za otkrivanje mikrovoltažnih elektrofizioloških potencijala srčanog mišića", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:217:329335

Metadata
TitleMetode za otkrivanje mikrovoltažnih elektrofizioloških potencijala srčanog mišića
Title (english)Methods for detection of microvoltage electrophysiological potentials of the heart muscle
AuthorIvan Glogar
MentorAndrej Novak (mentor)
Committee memberAndrej Novak (predsjednik povjerenstva)
Committee memberNenad Pavin (član povjerenstva)
Committee memberVedran Đerek (član povjerenstva)
Committee memberDanko Radić (član povjerenstva)
Committee memberMatko Glunčić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Science
(Department of Physics)
Zagreb
Defense date and country2024-02-26, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Physics
Abstract
Kasni ventrikulski potencijali su visokofrekventni, niskoamplitudni signalicija prisutnost u elektrokardiogramu (EKG) ima visoku prediktivnu vrijednost za potencijalno smrtonosne ventrikulske aritmije. U ovom radu implementiramo 3 metode detekcije takvih signala iz 80 EKG zapisa pacijenata porijeklom iz baze podataka PTB Diagnostic ECG Database. Metode uključuju široko prihvaćenu metodu usrednjenja po signalima (SAECG), analize marginalnih komponenti Guaragnella et al. (2019), te modele strojnog učenja temeljene na modelu stabla odluke. QRS komplekse EKG signala u fazi pretprocesiranja detektiramo po vrhu R vala, poravnavamo metodama ”Double-level” i ”Matched filter”, te filtriramo po sličnosti i frekvencijama signala. Koristimo EKG snimke zdravih pacijenata na koje umjetno dodajemo VLP signale na temelju njihovih utvrđenih karakteristika. U korištenju metoda SAECG i metode analize marginalnih komponenti nije ostvarena izrazito visoka točnost detekcije. S druge strane, vrijednost modela strojnog učenja leži u mogućnosti modela za detekciju skrivenih signala na temelju jednog srčanog ciklusa; ovdje, modeli pokazuju značajnu točnost detekcije, gdje najbolji model detektira tražene signale s točnošću od 98%.
Abstract (english)
Ventricular late potentials are high-frequency, low-amplitude signals whose presence in the electrocardiogram (ECG) has high predictive value for potentially fatal ventricular arrhythmias. In this work, we implement three methods to detect such signals from 80 ECG recordings of patients from the PTB Diagnostic ECG Database. The methods include the widely accepted signal-averaged ECG (SAECG) method, the marginal component analysis method by Guaragnella et al. (2019), and machine learning models based od decision tree model. ECG signal beats are detected in the preprocessing phase by the peak of the R wave of the ECG, aligned using ’Double-level’ and ’Matched filter’ methods, and filtered by similarity and frequencies. We use ECG recordings of healthy patients to which we artificially add VLP signals based on their established characteristics. SAECG methods and marginal component analysis did not achieve very high detection accuracy. On the other hand, the value of machine learning models lies in their ability to detect hidden signals based on a single beat; here, the models show significant detection accuracy, with the best model detecting the desired signals with 98% accuracy.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:217:329335
Study programmeTitle: The University Integrated Undergraduate and Graduate Programme in Physics; Research Physics; specializations in: Research Physics
Course: Research Physics
Study programme type: university
Study level: integrated undergraduate and graduate
Academic / professional title: sveučilišni magistar fizike (sveučilišni magistar fizike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-04-05 12:01:18