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Karakterizacija časovnih vrst vektorjev značilk prehodnih epizod segmenta ST elektrokardiograma
ID PIRNAR, ŽIGA (Author), ID Jager, Franc (Mentor) More about this mentor... This link opens in a new window

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PID: 20.500.12556/rul/b097692d-6623-4d96-8215-fb3d6d482d11

Abstract
V tem delu smo karakterizirali časovne vrste vektorjev morfoloških in diagnostičnih značilk segmenta ST elektrokardiograma (EKG) mednarodne referenčne podatkovne baze LTST DB 24-urnih ambulantnih posnetkov. Za ocenjevanje moči predstavitve prehodnih morfoloških sprememb segmenta ST z novimi časovnimi vrstami vektorjev značilk transformacije Karhunena in Loèva (KLT) ter transformacije na osnovi ortogonalnih Legendrovih polinomov (LPT) v primerjavi s tradicionalno predstavitvijo prehodnih diagnostičnih sprememb segmenta ST, ki temelji na merjenju nivoja in nagiba segmenta ST v časovnem prostoru, smo uporabili metrike rezidualne napake ob rekonstrukciji segmenta ST z nekaj prvimi koeficienti transformacije KLT in LPT, Pearsonove in Spearmanove korelacijske koeficiente med individualnimi koeficienti obeh transformacij ter nivojem in nagibom segmenta ST, ter križno korelacijo med posameznimi koeficienti KLT in LPT. Na podlagi rezultatov je razvidno, da predstavitev prehodnih sprememb morfologije segmenta ST s časovnimi vrstami vektorjev značilk v prostoru transformacije LPT izkazuje največjo predstavitveno moč na intervalih prehodnih ishemičnih epizod segmenta ST v smislu najnižje povprečne rezidualne napake, največje afinitete tradicionalni predstavitvi prehodnih diagnostičnih sprememb segmenta ST ter možnosti neposrednega ekspertnega vpogleda v klinično relevantne kategorije prehodnih morfoloških in diagnostičnih sprememb segmenta ST. V sklopu našega dela smo razvili tudi grafični uporabniški vmesnik WinECG za vizualizacijo in pregledovanje osnovnih signalov EKG, povprečnih srčnih utripov, časovnih vrst vektorjev značilk, številskih vrednosti vektorjev značilk ter ekspertnih oznak.

Language:Slovenian
Keywords:ambulantni elektrokardiogram, Long-Term ST Database, transformacija KLT, transformacija LPT, karakterizacija prehodnih epizod segmenta ST, časovne vrste vektorjev morfoloških značilk segmenta ST
Work type:Undergraduate thesis
Organization:FRI - Faculty of Computer and Information Science
Year:2016
PID:20.500.12556/RUL-91216 This link opens in a new window
Publication date in RUL:24.03.2017
Views:2152
Downloads:391
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Secondary language

Language:English
Title:Characterization of feature-vector time series of transient ST-segment episodes of electrocardiogram
Abstract:
In this thesis we characterized morphology and diagnostic electrocardiogram (ECG) ST-segment feature-vector time series of international reference database LTST DB of 24-hour ambulatory records. To estimate the power of representation of transient ST-segment morphology changes using feature-vector time series of the Karhunen-Loève Transformation (KLT) and of the Transformation based on the Legendre Polynomials (LPT) in comparison to traditional representation of transient ST-segment diagnostic changes, which is based on measuring of the ST-segment level and slope in time domain, we used several metrics which include the residual error during reconstruction of the ST-segment using the first few coefficients of the KLT and LPT transformation, the Pearson and Spearman correlation coefficients between individual coefficients of both transformations and the ST-segment level and slope, and cross-correlation between individual KLT and LPT coefficients. On the basis of the results it is evident that the representation of transient ST-segment morphology changes with feature-vector time series in the LPT transformation space exhibits the highest representational power during intervals of transient ischemic ST-segment episodes in terms of the lowest mean residual errors, highest affinity to traditional representation of transient diagnostic ST-segment changes, and possibility of direct expert insight into clinically relevant categories of transient ST-segment morphology and diagnostic changes. In the scope of our work we also developed a graphic user interface WinECG for visualization and examination of raw ECG signals, average heart beats, feature-vector time series, numerical values of feature vectors, and expert annotations.

Keywords:Ambulatory electrocardiogram, Long-Term ST Database, Transformation KLT, Transformation LPT, Characterization of transient ST-segment episodes, ST-segment morphology feature-vector time series

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