Heart Sound Classification Using Multi Modal Data Representation and Deep Learning
- Authors
- Lee, Jang Hyung; Kyung, Sun Young; Oh, Pyung Chun; Kim, Kwang Gi; Shin, Dong Jin
- Issue Date
- Mar-2020
- Publisher
- AMER SCIENTIFIC PUBLISHERS
- Keywords
- Heart Sound; Deep Learning; Phonocardiogram; Frequency Spectrum; Support Vector Machine; Physionet; Fourier Transform
- Citation
- JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, v.10, no.3, pp.537 - 543
- Journal Title
- JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS
- Volume
- 10
- Number
- 3
- Start Page
- 537
- End Page
- 543
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/17598
- DOI
- 10.1166/jmihi.2020.2987
- ISSN
- 2156-7018
- Abstract
- Heart anomalies are an important class of medical conditions from personal, public health and social perspectives and hence accurate and timely diagnoses are important. Heartbeat features two well known amplitude peaks termed S1 and S2. Some sound classification models rely on segmented sound intervals referenced to the locations of detected S1 and S2 peaks, which are often missing due to physiological causes and/or artifacts from sound sampling process. The constituent and combined models we propose are free from segmentation, which consequently is more robust and meritful from reliability aspects. Intuitive phonocardiogram representation with relatively simple deep learning architecture was found to be effective for classifying normal and abnormal heart sounds. A frequency spectrum based deep learning network also produced competitive classification results. When the classification models were merged in one via SVM, performance was seen to improve further. The SVM classification model, comprised of two time domain submodels and a frequency domain submodel, produced 0.9175 sensitivity, 0.8886 specificity and 0.9012 accuracy.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - 보건과학대학 > 의용생체공학과 > 1. Journal Articles
- 의과대학 > 의학과 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/17598)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.