Deep Learning Based Heart Murmur Detection Using Frequency-time Domain Features of Heartbeat Sounds
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Jungguk | - |
dc.contributor.author | Kang, Taein | - |
dc.contributor.author | Kim, Narin | - |
dc.contributor.author | Han, Soyul | - |
dc.contributor.author | Won, Hyejin | - |
dc.contributor.author | Gong, Wuming | - |
dc.contributor.author | Kwak, Il-Youp | - |
dc.date.accessioned | 2024-01-09T06:30:53Z | - |
dc.date.available | 2024-01-09T06:30:53Z | - |
dc.date.issued | 2022-09 | - |
dc.identifier.issn | 2325-8861 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/69996 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE Computer Society | - |
dc.title | Deep Learning Based Heart Murmur Detection Using Frequency-time Domain Features of Heartbeat Sounds | - |
dc.type | Article | - |
dc.identifier.doi | 10.22489/CinC.2022.071 | - |
dc.identifier.bibliographicCitation | Computing in Cardiology, v.2022-September | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85152941699 | - |
dc.citation.title | Computing in Cardiology | - |
dc.citation.volume | 2022-September | - |
dc.type.docType | Conference paper | - |
dc.publisher.location | 미국 | - |
dc.description.journalRegisteredClass | scopus | - |
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