Multiple Instance Learning Framework Used For ECG Premature Contraction Localization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F20%3APU140465" target="_blank" >RIV/00216305:26220/20:PU140465 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216305:26220/21:PU141500

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Multiple Instance Learning Framework Used For ECG Premature Contraction Localization

Popis výsledku v původním jazyce

We propose the model combining convolutional neural network with multiple instance learning in order to localize the premature atrial contraction and premature ventricular contraction. The model is based on ResNet architecture modified for 1D signal processing. Model was trained on China Physiological Signal Challenge 2018 database extended by manually labeled ground truth positions of premature complexes. The presented method did not reach satisfying results in PAC localization (with dice = 0.127 for avg-pooling implementation). On the other hand, results of localization of PVCs were comparable with other published studies (with dice = 0.952 for avg-pooling implementation).

Název v anglickém jazyce

Multiple Instance Learning Framework Used For ECG Premature Contraction Localization

Popis výsledku anglicky

We propose the model combining convolutional neural network with multiple instance learning in order to localize the premature atrial contraction and premature ventricular contraction. The model is based on ResNet architecture modified for 1D signal processing. Model was trained on China Physiological Signal Challenge 2018 database extended by manually labeled ground truth positions of premature complexes. The presented method did not reach satisfying results in PAC localization (with dice = 0.127 for avg-pooling implementation). On the other hand, results of localization of PVCs were comparable with other published studies (with dice = 0.952 for avg-pooling implementation).

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

20601 - Medical engineering

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

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