Scalable, Multiplatform, and Autonomous ECG Processor Supported by AI for Telemedicine Center
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Scalable, Multiplatform, and Autonomous ECG Processor Supported by AI for Telemedicine Center
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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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
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2022 Computing in Cardiology (CinC)
ISBN
979-8-3503-0097-0
ISSN
2325-8861
e-ISSN
2325-887X
Number of pages
4
Pages from-to
052
Publisher name
IEEE
Place of publication
New York
Event location
Tampere
Event date
Sep 4, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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