QRS Complex Detection in Paced and Spontaneous Ultra-High-Frequency ECG

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064173%3A_____%2F21%3AN0000101" target="_blank" >RIV/00064173:_____/21:N0000101 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.23919/CinC53138.2021.9662647" target="_blank" >https://doi.org/10.23919/CinC53138.2021.9662647</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/CinC53138.2021.9662647" target="_blank" >10.23919/CinC53138.2021.9662647</a>

Jazyk výsledku

angličtina

Název v původním jazyce

QRS Complex Detection in Paced and Spontaneous Ultra-High-Frequency ECG

Popis výsledku v původním jazyce

Background: Analysis of ultra-high-frequency ECG (UHF-ECG, sampled at 5,000 Hz) informs about dyssynchrony of ventricles activation. This information can be evaluated in real-time, allowing optimization of a pacing location during pacemaker implantation. However, the current method for real-time QRS detection in UHF-ECG requires suppressed pacemaker stimuli. Aim: We present a deep learning method for real-time QRS complex detection in UHF-ECG. Method: A 3-second window from V1, V3, and V6 lead of UHF-ECG signal is standardized and processed with the UNet network. The output is an array of QRS probabilities, further transformed into resultant QRS annotation using QRS probability and distance criterion. Results: The model had been trained on 2,250 ECG recordings from the FNUSA-ICRC hospital (Brno, Czechia) and tested on 300 recordings from the FNKV hospital (Prague, Czechia). We received an overall F1 score of 97.11 % on the test set. Conclusion: Presented approach improves UHF-ECG analysis performance and, consequently, could reduce measurement time during implant procedures.

Název v anglickém jazyce

QRS Complex Detection in Paced and Spontaneous Ultra-High-Frequency ECG

Popis výsledku anglicky

Background: Analysis of ultra-high-frequency ECG (UHF-ECG, sampled at 5,000 Hz) informs about dyssynchrony of ventricles activation. This information can be evaluated in real-time, allowing optimization of a pacing location during pacemaker implantation. However, the current method for real-time QRS detection in UHF-ECG requires suppressed pacemaker stimuli. Aim: We present a deep learning method for real-time QRS complex detection in UHF-ECG. Method: A 3-second window from V1, V3, and V6 lead of UHF-ECG signal is standardized and processed with the UNet network. The output is an array of QRS probabilities, further transformed into resultant QRS annotation using QRS probability and distance criterion. Results: The model had been trained on 2,250 ECG recordings from the FNUSA-ICRC hospital (Brno, Czechia) and tested on 300 recordings from the FNKV hospital (Prague, Czechia). We received an overall F1 score of 97.11 % on the test set. Conclusion: Presented approach improves UHF-ECG analysis performance and, consequently, could reduce measurement time during implant procedures.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

30201 - Cardiac and Cardiovascular systems

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

Computing in Cardiology, Vol. 48

ISBN

978-1-66546-721-6

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

1-4

Název nakladatele

IEEE

Místo vydání

New Jersey

Místo konání akce

Brno

Datum konání akce

12. 9. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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