Automated Identification of Paced Beats in Holter ECG
Identifikátory výsledku
Kód výsledku v 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>
Nalezeny alternativní kódy
RIV/00159816:_____/20:00075911
Výsledek na webu
<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>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Automated Identification of Paced Beats in Holter ECG
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
Automated Identification of Paced Beats in Holter ECG
Popis výsledku anglicky
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.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
20601 - Medical engineering
Návaznosti výsledku
Projekt
<a href="/cs/project/FW01010305" target="_blank" >FW01010305: Umělá inteligence pro autonomní klasifikaci EKG v rámci online telemedicínské platformy</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2020
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
2020 Computing in Cardiology (CinC 2020)
ISBN
978-1-7281-7382-5
ISSN
2325-8861
e-ISSN
—
Počet stran výsledku
4
Strana od-do
(2020)
Název nakladatele
IEEE
Místo vydání
New York
Místo konání akce
Rimini
Datum konání akce
13. 9. 2020
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
000657257000157