Optimization of wavelet transform in the task of intracardiac ECG segmentation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU144637" target="_blank" >RIV/00216305:26220/22:PU144637 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_1.pdf" target="_blank" >https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_1.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Optimization of wavelet transform in the task of intracardiac ECG segmentation

Popis výsledku v původním jazyce

My work deals with the selection of an appropriate wavelet transform setting for feature extraction from intracardiac ECG recordings. The studied signals were obtained during electrophysiological examinations at the Department of Pediatric Medicine, University Hospital Brno. In this paper, several wavelets are tested for feature extraction which is followed by adaptive thresholding to detect atrial activity from the extracted features. The procedure is evaluated using the F-score. Although the presented procedure does not appear to be overall effective for intracardiac signal segmentation, it certainly does not reject the use of wavelet transforms in combination with advanced machine learning, neural network, or deep learning techniques.

Název v anglickém jazyce

Optimization of wavelet transform in the task of intracardiac ECG segmentation

Popis výsledku anglicky

My work deals with the selection of an appropriate wavelet transform setting for feature extraction from intracardiac ECG recordings. The studied signals were obtained during electrophysiological examinations at the Department of Pediatric Medicine, University Hospital Brno. In this paper, several wavelets are tested for feature extraction which is followed by adaptive thresholding to detect atrial activity from the extracted features. The procedure is evaluated using the F-score. Although the presented procedure does not appear to be overall effective for intracardiac signal segmentation, it certainly does not reject the use of wavelet transforms in combination with advanced machine learning, neural network, or deep learning techniques.

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

2022

Kód důvěrnosti údajů

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