Automated Conversion and Analysis of Printed ECG Using Random Signal Pretrained Digitizer

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F24%3A00645406" target="_blank" >RIV/68081731:_____/24:00645406 - isvavai.cz</a>

Result on the web

<a href="https://cinc.org/archives/2024/pdf/CinC2024-004.pdf" target="_blank" >https://cinc.org/archives/2024/pdf/CinC2024-004.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.22489/CinC.2024.004" target="_blank" >10.22489/CinC.2024.004</a>

Result language

angličtina

Original language name

Automated Conversion and Analysis of Printed ECG Using Random Signal Pretrained Digitizer

Original language description

This study presents the ISIBrno-AIMT team’s approach to addressing the George B. Moody PhysioNet Challenge 2024. The solution devised for the challenge’s digitization task involves a sequential application of three neural networks: lead detection, classification, and digitization. Digitization of the leads is performed by a neural network comprising 2D convolutional layers and gated recurrent units (GRU). The pipeline initially identifies bounding boxes encompassing lead signals and their corresponding names. Subsequently, the lead names are cropped and classified, while the lead signals are extracted using the detected bounding boxes and digitized by the third network. In the final step, the lead names and signals are linked via bounding box intersection, completing the digitization process. In this task, our team achieved a score of-0.675 SNR. The classification task to 11 different classes was addressed using a model developed for the PhysioNet/Computing in Cardiology Challenge 2021, whichincorporates convolutional neural network (CNN) layers and an attention mechanism. We applied this model to digitized ECG signals obtained from the preceding digitization task. The model was fine-tuned in two stages: initially, using augmented oracle signals, and subsequently, using signals digitized by our digitization model. Our approach resulted in an F1-measure score of 0.306.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

20601 - Medical engineering

Project

<a href="/en/project/FW06010766" target="_blank" >FW06010766: Remote Therapy in Heart Failure Patients Using Artificial Intelligence and Fusion of Multimodal Inputs</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

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

Article name in the collection

Computing in Cardiology 2024 (CinC 2024)

ISBN

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ISSN

2325-887X

e-ISSN

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Number of pages

4

Pages from-to

4

Publisher name

Computing in Cardiology

Place of publication

Neuveden

Event location

Karlsruhe

Event date

Nov 8, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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