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Cited by 8Phonocardiography and Auscultation TechniquesNon-Invasive Vital Sign MonitoringArtificial Intelligence in Healthcare

The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.

A non-invasive approach to investigation of ventricular blood pressure using cardiac sound features

Open access
Tang, Hong, Zhang, Jinghui, Chen, Huaming, Mondal, Ashok, Park, Yongwan·2017·Physiological Measurement
DOI 10.1088/1361-6579/aa552a
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Abstract

Heart sounds (HSs) are produced by the interaction of the heart valves, great vessels, and heart wall with blood flow. Previous researchers have demonstrated that blood pressure can be predicted by exploring the features of cardiac sounds. These features include the amplitude of the HSs, the ratio of the amplitude, the systolic time interval, and the spectrum of the HSs. A single feature or combinations of several features have been used for prediction of blood pressure with moderate accuracy. Experiments were conducted with three beagles under various levels of blood pressure induced by different doses of epinephrine. The HSs, blood pressure in the left ventricle and electrocardiograph signals were simultaneously recorded. A total of 31 records (18 262 cardiac beats) were collected. In this paper, 91 features in various domains are extracted and their linear correlations with the measured blood pressures are examined. These features are divided into four groups and applied individually at the input of a neural network to predict the left ventricular blood pressure (LVBP). The analysis shows that non-spectral features can track changes of the LVBP with lower standard deviation. Consequently, the non-spectral feature set gives the best prediction accuracy. The average correlation coefficient between the measured and the predicted blood pressure is 0.92 and the mean absolute error is 6.86 mmHg, even when the systolic blood pressure varies in the large range from 90 mmHg to 282 mmHg. Hence, systolic blood pressure can be accurately predicted even when using fewer HS features. This technique can be used as an alternative to real-time blood pressure monitoring and it has promising applications in home health care environments.

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Crossref · 10.1088/1361-6579/aa552a

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Research works printed but not linked: 6

  1. Castro A, 2014, Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, 2014, 5494
  2. Liang H, 1997, Comput. Cardiol., 24, 105
  3. Ozcan Gulcur H, 1998, 2nd Int. Conf. in Biomedical Engineering, 39
  4. Wong M Y, 2006, Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, 1, 2878
  5. Wong M Y, 2006, Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, 1, 1487
  6. Zhang X Y, 2006, Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, 1, 2888

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Metadata

Authors
Tang, H., Zhang, J., Chen, H., Mondal, A., & Park, Y.
Affiliations
  • Dalian University of Technology (CN)
  • National Institute of Technology Karnataka (IN)
  • Yeungnam University (KR)
Corresponding author
Tang, Hong
ORCID
Venue
Physiological Measurement
Publisher
IOP Publishing
Published
2017-01-18
Volume
38
Issue
2
Pages
289-309
ISSN
  • 0967-3334 (ISSN-L)
  • 1361-6579
DOI
10.1088/1361-6579/aa552a
OpenAlex
W2579826766
Cited by
8
References
24
Citations by year
2025: 1 · 2024: 1 · 2022: 2 · 2020: 1 · 2019: 2 · 2017: 1
FWCI
0.90
Type
journal article
Language
en
Open access
Licence
Locations
  • Physiological Measurement (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
National Natural Science Foundation of China (61471081)
Topics
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Phonocardiography and Auscultation Techniques
  • Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
heart sounds, blood pressure prediction, systolic blood pressure, beagle dogs, neural network prediction, non-invasive blood pressure monitoring, home health care
MeSH terms
Animals, Blood Pressure, Blood Pressure Determination, Dogs, Heart Sounds, Linear Models, Ventricular Function, Left

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