Deep Learning End-to-End Approach for Precise QRS Complex Delineation Using Temporal Region-Based Convolutional Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0198316" target="_blank" >RIV/00216305:26220/26:0198316 - isvavai.cz</a>

Výsledek na webu

<a href="https://cinc.org/archives/2024/pdf/CinC2024-107.pdf" target="_blank" >https://cinc.org/archives/2024/pdf/CinC2024-107.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.22489/CinC.2024.107" target="_blank" >10.22489/CinC.2024.107</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep Learning End-to-End Approach for Precise QRS Complex Delineation Using Temporal Region-Based Convolutional Neural Networks

Popis výsledku v původním jazyce

Advancements in clinical diagnosis of heart disease are driven by technological innovations and signal processing developments. ECG segmentation, particularly QRS complex detection, plays a crucial role in cardiac cycle analysis. Deep learning has revolutionized automated ECG analysis, enhancing diagnostic accuracy significantly. This paper proposes an optimized Region Proposal Network (RPN) architecture for QRS complex detection, specifically designed for 1D signals. Leveraging a vast ECGdataset, extensive data augmentation, feature extraction, and RPN-based QRS detection were employed. Our method achieved a QRS detection F1 score of up to 99 %, highlighting its high reliability. Furthermore, QRS complex delineation exhibited deviations typically within 8 ms. The results were verified using a publicly available Lobachevsky University Electrocardiography Database (LUDB), with validation yielding F1 score of 91.59 % for QRS detection and RMSE of 10.38 ms for QRS complex delineation. The optimized RPN architecture for QRS complex detection presents a promising solution for efficient ECG analysis.

Název v anglickém jazyce

Deep Learning End-to-End Approach for Precise QRS Complex Delineation Using Temporal Region-Based Convolutional Neural Networks

Popis výsledku anglicky

Advancements in clinical diagnosis of heart disease are driven by technological innovations and signal processing developments. ECG segmentation, particularly QRS complex detection, plays a crucial role in cardiac cycle analysis. Deep learning has revolutionized automated ECG analysis, enhancing diagnostic accuracy significantly. This paper proposes an optimized Region Proposal Network (RPN) architecture for QRS complex detection, specifically designed for 1D signals. Leveraging a vast ECGdataset, extensive data augmentation, feature extraction, and RPN-based QRS detection were employed. Our method achieved a QRS detection F1 score of up to 99 %, highlighting its high reliability. Furthermore, QRS complex delineation exhibited deviations typically within 8 ms. The results were verified using a publicly available Lobachevsky University Electrocardiography Database (LUDB), with validation yielding F1 score of 91.59 % for QRS detection and RMSE of 10.38 ms for QRS complex delineation. The optimized RPN architecture for QRS complex detection presents a promising solution for efficient ECG analysis.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

Computing in Cardiology 2025

ISBN

—

ISSN

—

e-ISSN

2325-887X

Počet stran výsledku

4

Strana od-do

1-4

Název nakladatele

Computing in Cardioligy 2025

Místo vydání

Karlsruhe

Místo konání akce

Karlsruhe

Datum konání akce

8. 9. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—