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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    20601 - Medical engineering

Result continuities

  • Project

  • 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