Evaluating Pauses in Holter ECG Signals

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Evaluating Pauses in Holter ECG Signals

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Evaluating Pauses in Holter ECG Signals

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20601 - Medical engineering

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)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2021 Computing in Cardiology (CinC)

ISBN

978-166547916-5

ISSN

2325-8861

e-ISSN

2325-887X

Počet stran výsledku

4

Strana od-do

107

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Brno

Datum konání akce

12. 9. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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