Utilization of Deep Learning and Expert Feature Classifier for Detection of Heart Murmurs
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F22%3A00583010" target="_blank" >RIV/68081731:_____/22:00583010 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10081763" target="_blank" >https://ieeexplore.ieee.org/document/10081763</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.22489/CinC.2022.041" target="_blank" >10.22489/CinC.2022.041</a>
Alternative languages
Result language
angličtina
Original language name
Utilization of Deep Learning and Expert Feature Classifier for Detection of Heart Murmurs
Original language description
This paper introduces our solution (ISIBrno-AIMT team) to the Physionet Challenge 2022. The main goal of the challenge was a classification of heart murmurs from phonocardiographic recordings into three mutually exclusive classes (i.e., present, unknown, and not present) and to determine whether the patient's overall status is Normal or Abnormal. We propose a deep learning method that classifies whether there is a heart murmur in the phonocardiographic recording and also provides heart sound segmen-tation. Furthermore, the expert feature classifier assesses whether the patient's status is normal or abnormal. Our approach achieved a hidden test challenge score of 0.755 in the murmur classification task and a score of 12313 in the patient outcome classification task. Our team was ranked as 9th and 12th out of 40 teams in the official ranking for murmur and outcome classification, respectively.
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
041
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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