Research on prediction method of left ventricular blood pressure based on external heart sounds_Journal of Biomedical Engineering

Authors：

ZHANG Jinghui ,  TANG Hong

Department of Biomedical Engineering, Dalian University of Technology, Dalian, Liaoning 116024, P.R.China;

Corresponding author：

TANG Hong, Email: tanghong@dlut.edu.cn

Keywords：

heart sound feature; left ventricular blood pressure; blood pressure prediction; artificial neural network

DOI：

10.7507/1001-5515.201606068

Video：

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Abstract Full text Figures/Tables Video References Cited by

The continuous left ventricle blood pressure prediction based on selected heart sound features was realized in this study. The experiments were carried out on three beagle dogs and the variations of cardiac hemodynamics were induced by various dose of epinephrine. The phonocardiogram, electrocardiogram and blood pressures in left ventricle were synchronously acquired. We obtained 28 valid recordings in this study. An artificial neural network was trained with the selected feature to predict left ventricular blood pressure and this trained network made a good performance. The results showed that the absolute average error was 7.3 mm Hg even though the blood pressures had a large range of fluctuation. The average correlation coefficient between the predicted and the measured blood pressure was 0.92. These results showed that the method in this paper was helpful to monitor left ventricular hemodynamics non-invasively and continuously.

Citation： ZHANG Jinghui, TANG Hong. Research on prediction method of left ventricular blood pressure based on external heart sounds. Journal of Biomedical Engineering, 2017, 34(3): 335-341. doi: 10.7507/1001-5515.201606068 Copy

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25.	赵建华. 一种基于交叉验证思想的半监督分类方法. 西南科技大学学报, 2014(1): 34-38.

1. Cobra S D, Cardoso R M, Rodrigues M P. Usefulness of the second heart sound for predicting pulmonary hypertension in patients with interstitial lung disease. Sao Paulo Med J, 2016, 134(1): 34-39.
2. Elgendi M, Bobhate P, Jain S, et al. The unique heart sound signature of children with pulmonary artery hypertension. Pulm Circ, 2015, 5(4): 631-639.
3. Zheng Yineng, Guo Xingming, Qin Jian, et al. Computer-assisted diagnosis for chronic heart failure by the analysis of their cardiac reserve and heart sound characteristics. Comput Methods Programs Biomed, 2015, 122(3): 372-383.
4. Redlarski G, Gradolewski D, Palkowski A. A system for heart sounds classification. PLoS One, 2014, 9(11): e112673.
5. Tranulis C, Durand L G, Senhadji L, et al. Estimation of pulmonary arterial pressure by a neural network analysis using features based on time-frequency representations of the second heart sound. Med Biol Eng Comput, 2002, 40(2): 205-212.
6. Sakamoto T, Kusukawa R, Maccanon D M, et al. Hemodynamic Determinants of The Amplitude of The First Heart Sound. Circ Res, 1965, 16(1): 45-57.
7. Blick E F, Sabbah H N, Stein P D. One-dimensional model of diastolic semilunar valve vibrations productive of heart sounds. J Biomech, 1979, 12(3): 223-227.
8. Sikarskie D L, Stein P D, Vable M. A mathematical model of aortic valve vibration. J Biomech, 1984, 17(11): 831-837.
9. Zhang X Y, Zhang Y T. Model-based analysis of effects of systolic blood pressure on frequency characteristics of the second heart sound//28th Annual International Conference of the IEEE, 2006.
10. Ozcan G H, Bahadirlar Y. Estimation of systolic blood pressure from the second heart sounds//Proceedings of the 1998 2nd International Conference, 1998.
11. Hoon Lim K, Duck Shin Y, Hi Park S, et al. Correlation of blood pressure and the ratio of S1 to S2 as measured by esophageal stethoscope and wireless bluetooth transmission. Pak J Med Sci, 2013, 29(4): 1023-1027.
12. Zhang X Y, Zhang Y T. A model-based study of relationship between timing of second heart sound and systolic blood pressure//28th Annual International Conference of the IEEE, 2006.
13. Zhang Xinyu, Macpherson E, Zhang Yuanting. Relations between the timing of the second heart sound and aortic blood pressure. IEEE Trans Biomed Eng, 2008, 55(4): 1291-1297.
14. Wong M Y, Zhang X Y, Zhang Y T. The cuffless arterial blood pressure estimation based on the timing-characteristics of second heart sound//28th Annual International Conference of the IEEE, 2006.
15. Wong M Y, Poon C C, Zhang Y T. Can The timing-characteristics of phonocardiographic signal be used for cuffless systolic blood pressure estimation?//28th Annual International Conference of the IEEE, 2006.
16. Peng Rongchao, Yan Wenrong, Zhang Ningling, et al. Cuffless and continuous blood pressure estimation from the heart sound signals. Sensors (Basel), 2015, 15(9): 23653-23666.
17. WHO. Global status report on noncommunicable diseases 2014. World Self-Medication Industry, 2015.
18. American P A. Guidelines for ethical conduct in the care and use of animals. J Exp Anal Behav, 1986, 45(2): 127-132.
19. Liang H, Lukkarinen S, Hartimo L. Heart sound segmentation algorithm based on heart sound envelogram. Comput Cardio, 1997, 24(24): 105-108.
20. Leng Shuang, Tan Rusan, Chai K T, et al. The electronic stethoscope. Biomed Eng Online, 2015, 14: 66.
21. Castro A, Mattos S S, Coimbra M T. Noninvasive blood pressure and the second heart sound analysis//2014 36th Annual International Conference of The Ieee Engineering In Medicine and Biology Society (Embc), 2014: 5494-5497.
22. Rumelhart D E, Hinton G E, Williams R J. Learning repre-sentations by back-propagating errors. Nature, 1986, 323(6088): 533-536.
23. Uğuz H. A biomedical system based on artificial neural network and principal component analysis for diagnosis of the heart valve diseases. J Med Syst, 2012, 36(1): 61-72.
24. Jain L C, Seera M, Lim C P, et al. A review of online learning in supervised neural networks. Neural Comput Appl, 2014, 25(3/4): 491-509.
25. 赵建华. 一种基于交叉验证思想的半监督分类方法. 西南科技大学学报, 2014(1): 34-38.

Journal of Biomedical Engineering

Research on prediction method of left ventricular blood pressure based on external heart sounds

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