Evaluating Pauses in Holter ECG Signals
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F21%3A00555164" target="_blank" >RIV/68081731:_____/21:00555164 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9662914" target="_blank" >https://ieeexplore.ieee.org/document/9662914</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23919/CinC53138.2021.9662914" target="_blank" >10.23919/CinC53138.2021.9662914</a>
Alternative languages
Result language
angličtina
Original language name
Evaluating Pauses in Holter ECG Signals
Original language description
Background: Information related to pauses in heart activity is an important output of ECG Holter monitoring reports. This information should be quickly assessed from inter-beat (RR) intervals only (a naïve approach). However, evaluating pauses in Holter ECGs recorded during usual daily activities can be more challenging due to signal lower quality. In this paper, we propose a method to improve pause detection in heart activity from Holter ECG recordings. Method: We used 978 recordings (length 45 seconds, 1-lead ECG, sampled at 200 or 250 Hz) with a known longest RR interval (from 1.12 to 19.0 seconds, mean duration of 2.72 ± 1.26 seconds). QRS complexes were detected by a convolutional neural network with a recurrent layer. This study started with the automated removal of suspicious QRS complexes by a QRS amplitude. Then we iterated through RR intervals, seeking saturated areas, missed QRS, or a strong noise, potentially, examined RR intervals were further refined. The longest interval was reported for each recording. Results: The ability to find life-threatening pauses improved from an F1 score of 0.95 to 0.97. Conclusion: The presented method improved pause detection in Holter ECG recordings compared to the naïve approach.
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
<a href="/en/project/FW01010305" target="_blank" >FW01010305: Artificial Intelligence for Autonomous ECG Classification in Online Telemedicine Platform</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2021 Computing in Cardiology (CinC)
ISBN
978-166547916-5
ISSN
2325-8861
e-ISSN
2325-887X
Number of pages
4
Pages from-to
107
Publisher name
IEEE
Place of publication
New York
Event location
Brno
Event date
Sep 12, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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