期刊
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
卷 24, 期 7, 页码 2082-2092出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2019.2955281
关键词
Heart; Phonocardiography; Feature extraction; Databases; Valves; Hidden Markov models; Support vector machines; Heart sound; cardiovascular disease; machine listening; artificial intelligence; healthcare
类别
资金
- Natural Science Foundation of Shenzhen University General Hospital, China [SUGH2018QD013]
- Zhejiang Lab's International Talent Fund for Young Professionals (Project HANAMI), China
- JSPS Postdoctoral Fellowship for Research in Japan from the Japan Society for the Promotion of Science (JSPS), Japan [P19081]
- Ministry of Education, Culture, Sports, Science and Technology, Japan [19F19081, 17H00878]
- Horizon H2020 Marie Sklodowska-Curie Actions Initial Training Network European Training Network (MSCA-ITN-ETN) Project [766287]
- Bavarian State Ministry of Education, Science and the Arts
- Austrian Science Fund (FWF) [P19081] Funding Source: Austrian Science Fund (FWF)
- Grants-in-Aid for Scientific Research [17H00878, 19F19081] Funding Source: KAKEN
Auscultation of the heart is a widely studied technique, which requires precise hearing from practitioners as a means of distinguishing subtle differences in heart-beat rhythm. This technique is popular due to its non-invasive nature, and can be an early diagnosis aid for a range of cardiac conditions. Machine listening approaches can support this process, monitoring continuously and allowing for a representation of both mild and chronic heart conditions. Despite this potential, relevant databases and benchmark studies are scarce. In this paper, we introduce our publicly accessible database, the Heart Sounds Shenzhen Corpus (HSS), which was first released during the recent INTERSPEECH 2018 ComParE Heart Sound sub-challenge. Additionally, we provide a survey of machine learning work in the area of heart sound recognition, as well as a benchmark for HSS utilising standard acoustic features and machine learning models. At best our support vector machine with Log Mel features achieves 49.7% unweighted average recall on a three category task (normal, mild, moderate/severe).
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