Utilization of Deep Learning and Expert Feature Classifier for Detection of Heart Murmurs

0583010 - ÚPT 2024 RIV US eng C - Conference Paper (international conference)
Nejedlý, Petr - Pavlus, Ján - Smíšek, Radovan - Vargová, Enikö - Koščová, Zuzana - Viščor, Ivo - Jurák, Pavel - Plešinger, Filip
Utilization of Deep Learning and Expert Feature Classifier for Detection of Heart Murmurs.
2022 Computing in Cardiology (CinC). New York: IEEE, 2022, 2022-eptember (2022), č. článku 041. ISBN 979-8-3503-0097-0. ISSN 2325-8861. E-ISSN 2325-887X.
[Computing in Cardiology 2022 /49./. Tampere (FI), 04.09.2022-07.09.2022]
R&D Projects: GA TA ČR(CZ) FW01010305
Institutional support: RVO:68081731
Keywords : heart murmurs * deep learning * classification
OECD category: Medical engineering
https://ieeexplore.ieee.org/document/10081763 https://www.cinc.org/archives/2022/pdf/CinC2022-041.pdf

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.
Permanent Link: https://hdl.handle.net/11104/0351652