Deep Learning Based Heart Murmur Detection Using Frequency-time Domain Features of Heartbeat Soundsopen access
- Authors
- Lee, Jungguk; Kang, Taein; Kim, Narin; Han, Soyul; Won, Hyejin; Gong, Wuming; Kwak, Il-Youp
- Issue Date
- Sep-2022
- Publisher
- IEEE Computer Society
- Citation
- Computing in Cardiology, v.2022-September
- Journal Title
- Computing in Cardiology
- Volume
- 2022-September
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/69996
- DOI
- 10.22489/CinC.2022.071
- ISSN
- 2325-8861
- Abstract
- 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. © 2022 Creative Commons.
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