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论文类型：期刊论文

发表时间：2017-02-01

发表刊物：PHYSIOLOGICAL MEASUREMENT

收录刊物：SCIE、PubMed、Scopus

卷号：38

期号：2

页面范围：289-309

ISSN号：0967-3334

关键字：heart sound features; left ventricular blood pressure; back propagation neural network; continuous estimation; correlation analysis

摘要：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 realtime blood pressure monitoring and it has promising applications in home health care environments.