The goal of the George B. Moody PhysioNet Challenge 2022 was to use heart sound recordings gathered from various auscultation locations to identify murmurs and clinical outcomes. Our team, CAU_UMN, proposes a deep learning-based model that automatically identifies heart murmurs from a phonocardiogram (PCG). We converted the heartbeat sound into 2D features in the frequency-time domain through feature extraction techniques such as log-mel spectrogram, Short Time Fourier Transform (STFT), and Constant Q Transform (CQT). The frequency-temporal 2D features were modeled using voice classification models such as Convolutional neural networks (CNN) and Light CNN (LCNN). The model using log-mel spectrogram and LCNN was ranked 5th for murmur detection with a weighted accuracy of 0.767 and 5th for clinical outcome detection with a cost of 11933 in the test dataset of the George B. Moody PhysioNet Challenge. We believe that our deep learning based heart murmur detection system will be a promising system for automatic heart murmur detection from PCG.
|Original language||English (US)|
|Title of host publication||2022 Computing in Cardiology, CinC 2022|
|Publisher||IEEE Computer Society|
|State||Published - 2022|
|Event||2022 Computing in Cardiology, CinC 2022 - Tampere, Finland|
Duration: Sep 4 2022 → Sep 7 2022
|Name||Computing in Cardiology|
|Conference||2022 Computing in Cardiology, CinC 2022|
|Period||9/4/22 → 9/7/22|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (No. NRF-2020R1C1C1A01013020).
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