Automated Identification of Paced Beats in Holter ECG
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F20%3A00551341" target="_blank" >RIV/68081731:_____/20:00551341 - isvavai.cz</a>
Alternative codes found
RIV/00159816:_____/20:00075911
Result on the web
<a href="https://ieeexplore.ieee.org/document/9344294" target="_blank" >https://ieeexplore.ieee.org/document/9344294</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.22489/CinC.2020.067" target="_blank" >10.22489/CinC.2020.067</a>
Alternative languages
Result language
angličtina
Original language name
Automated Identification of Paced Beats in Holter ECG
Original language description
Background: Identification of paced beats is a necessity for Holter ECG monitoring reports. However, lower sampling frequencies make the pacing stimuli hard to identify. In this study, we present a method to distinguish between paced and non-paced beats. Method: One-hour ECG recordings (158 patients, single lead, 250 Hz) were recorded during usual daily activities. A total of 44,918 QRS complexes were detected and marked as paced (19,004) or non-paced (25,914). This dataset was split (60%, out-of-patient) to training and testing datasets. Three features based on amplitude envelopes in two frequency bands were used to build a logistic regression model. An additional external dataset (2,193 recordings with 16,941 QRS) was assessed at a different facility and was used for the cross-database test. Results: The model showed the test Fl-score of 0.93, cross-database test shown Fl-score of 0.924. Conclusion: The presented method recognizes paced and non-paced heartbeats in lower sampling frequency, even if it can hardly be visually observed in the raw signal.
Czech name
—
Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
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
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2020 Computing in Cardiology (CinC 2020)
ISBN
978-1-7281-7382-5
ISSN
2325-8861
e-ISSN
—
Number of pages
4
Pages from-to
(2020)
Publisher name
IEEE
Place of publication
New York
Event location
Rimini
Event date
Sep 13, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000657257000157