Scalable, Multiplatform, and Autonomous ECG Processor Supported by AI for Telemedicine Center

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F22%3A00583011" target="_blank" >RIV/68081731:_____/22:00583011 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10081823" target="_blank" >https://ieeexplore.ieee.org/document/10081823</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.22489/CinC.2022.052" target="_blank" >10.22489/CinC.2022.052</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Scalable, Multiplatform, and Autonomous ECG Processor Supported by AI for Telemedicine Center

Popis výsledku v původním jazyce

Background: Wearable devices play an essential role in the early diagnosis of heart diseases. However, effective management of long-term ECG measurements (1-3 weeks) by a telemedicine center (TMC) requires specifically designed software. Method: We used the multiplatform framework.NET to build the application. Deep-learning models for QRS detection, classification, and rhythm analysis were trained in the PyTorch framework, models were trained using data from Medical Data Transfer, s. r. o. Czechia (N=73,450 and 12,111). The ONNX runtime libraries were used for model inference, including acceleration by graphic cards Results: The pre-production benchmark (recordings of 82 patients) showed a mean accuracy of 0.97 ± 0.04 for QRS detection and classification into three classes, it also showed a mean accuracy of 0.97 ± 0.03 for rhythm classification into seven classes. Conclusion: The presented software is a fully automated, multiplatform, and scalable back-end application to process incoming ECG data in the TMC Although it is not freely accessible, we are open to considering processing ECG data for research and strictly non-commercial purposes.

Název v anglickém jazyce

Scalable, Multiplatform, and Autonomous ECG Processor Supported by AI for Telemedicine Center

Popis výsledku anglicky

Background: Wearable devices play an essential role in the early diagnosis of heart diseases. However, effective management of long-term ECG measurements (1-3 weeks) by a telemedicine center (TMC) requires specifically designed software. Method: We used the multiplatform framework.NET to build the application. Deep-learning models for QRS detection, classification, and rhythm analysis were trained in the PyTorch framework, models were trained using data from Medical Data Transfer, s. r. o. Czechia (N=73,450 and 12,111). The ONNX runtime libraries were used for model inference, including acceleration by graphic cards Results: The pre-production benchmark (recordings of 82 patients) showed a mean accuracy of 0.97 ± 0.04 for QRS detection and classification into three classes, it also showed a mean accuracy of 0.97 ± 0.03 for rhythm classification into seven classes. Conclusion: The presented software is a fully automated, multiplatform, and scalable back-end application to process incoming ECG data in the TMC Although it is not freely accessible, we are open to considering processing ECG data for research and strictly non-commercial purposes.

Druh

D - Stať ve sborníku

CEP obor

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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

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

2022 Computing in Cardiology (CinC)

ISBN

979-8-3503-0097-0

ISSN

2325-8861

e-ISSN

2325-887X

Počet stran výsledku

4

Strana od-do

052

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Tampere

Datum konání akce

4. 9. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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