Ultra-High Frequency ECG Deep-Learning Beat Detector Delivering QRS Onsets and Offsets

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F22%3A00583012" target="_blank" >RIV/68081731:_____/22:00583012 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10081715" target="_blank" >https://ieeexplore.ieee.org/document/10081715</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.22489/CinC.2022.230" target="_blank" >10.22489/CinC.2022.230</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Ultra-High Frequency ECG Deep-Learning Beat Detector Delivering QRS Onsets and Offsets

Popis výsledku v původním jazyce

Background: QRS duration is a common measure linked to conduction abnormalities in heart ventricles. Aim: We propose a QRS detector, further able to locate QRS onset and offset in one inference step. Method: A 3-second window from 12 leads of UHF ECG signal (5 kHz) is standardized and processed with the UNet network. The output is an array of QRS probabilities, further processed with probability and distance criterion, allowing us to determine duration and final location of QRSs. Results: The model was trained on 2,250 ECG recordings from the FNUSA-ICRC hospital (Brno, Czechia). The model was tested on 5 different datasets: FNUSA, a dataset from FNKV hospital (Prague, Czechia), and three public datasets (Cipa, Strict LBBB, LUDB). Regarding QRS duration, results showed a mean absolute error of 13.99 ± 4.29 ms between annotated durations and the output of the proposed model. A QRS detection F-score was 0.98 ± 0.01. Conclusion: Our results indicate high QRS detection performance on both spontaneous and paced UHF ECG data. We also showed that QRS detection and duration could be combined in one deep learning algorithm.

Název v anglickém jazyce

Ultra-High Frequency ECG Deep-Learning Beat Detector Delivering QRS Onsets and Offsets

Popis výsledku anglicky

Background: QRS duration is a common measure linked to conduction abnormalities in heart ventricles. Aim: We propose a QRS detector, further able to locate QRS onset and offset in one inference step. Method: A 3-second window from 12 leads of UHF ECG signal (5 kHz) is standardized and processed with the UNet network. The output is an array of QRS probabilities, further processed with probability and distance criterion, allowing us to determine duration and final location of QRSs. Results: The model was trained on 2,250 ECG recordings from the FNUSA-ICRC hospital (Brno, Czechia). The model was tested on 5 different datasets: FNUSA, a dataset from FNKV hospital (Prague, Czechia), and three public datasets (Cipa, Strict LBBB, LUDB). Regarding QRS duration, results showed a mean absolute error of 13.99 ± 4.29 ms between annotated durations and the output of the proposed model. A QRS detection F-score was 0.98 ± 0.01. Conclusion: Our results indicate high QRS detection performance on both spontaneous and paced UHF ECG data. We also showed that QRS detection and duration could be combined in one deep learning algorithm.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

2022 Computing in Cardiology (CinC)

ISBN

979-8-3503-0097-0

ISSN

2325-8861

e-ISSN

2325-887X

Počet stran výsledku

4

Strana od-do

"2022"-"eptember (2022)"

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Tampere

Datum konání akce

4. 9. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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