Ultra-High Frequency ECG Deep-Learning Beat Detector Delivering QRS Onsets and Offsets
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Ultra-High Frequency ECG Deep-Learning Beat Detector Delivering QRS Onsets and Offsets
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20601 - Medical engineering
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2022 Computing in Cardiology (CinC)
ISBN
979-8-3503-0097-0
ISSN
2325-8861
e-ISSN
2325-887X
Number of pages
4
Pages from-to
"2022"-"eptember (2022)"
Publisher name
IEEE
Place of publication
New York
Event location
Tampere
Event date
Sep 4, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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