Non-invasive arterial pressure estimating with the cardiac monitor CardioQvark
Matematičeskaâ biologiâ i bioinformatika, Tome 12 (2017) no. 2, pp. 536-545.

Voir la notice de l'article provenant de la source Math-Net.Ru

The outcome of the research on possibility to non-invasively estimate systolic blood pressure is presented. The estimating was performed by applying machine learning techniques to the data acquired with the cardiac monitor CardioQvark. The developed in Russia cardiac monitor represents a portable device capable of registering synchronous electrocardiogram and photoplethysmogram. The presented results confirm the possibility of constructing algorithms capable of estimating systolic blood pressure of individual patients. Also the possibility to construct general purpose algorithms, i.e. algorithms capable of estimating blood pressure of any patient without additional setup, was confirmed.
@article{MBB_2017_12_2_a1,
     author = {O. V. Senko and V. Ya. Chuchupal and A. A. Dokukin},
     title = {Non-invasive arterial pressure estimating with the cardiac monitor {CardioQvark}},
     journal = {Matemati\v{c}eska\^a biologi\^a i bioinformatika},
     pages = {536--545},
     publisher = {mathdoc},
     volume = {12},
     number = {2},
     year = {2017},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/MBB_2017_12_2_a1/}
}
TY  - JOUR
AU  - O. V. Senko
AU  - V. Ya. Chuchupal
AU  - A. A. Dokukin
TI  - Non-invasive arterial pressure estimating with the cardiac monitor CardioQvark
JO  - Matematičeskaâ biologiâ i bioinformatika
PY  - 2017
SP  - 536
EP  - 545
VL  - 12
IS  - 2
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/MBB_2017_12_2_a1/
LA  - ru
ID  - MBB_2017_12_2_a1
ER  - 
%0 Journal Article
%A O. V. Senko
%A V. Ya. Chuchupal
%A A. A. Dokukin
%T Non-invasive arterial pressure estimating with the cardiac monitor CardioQvark
%J Matematičeskaâ biologiâ i bioinformatika
%D 2017
%P 536-545
%V 12
%N 2
%I mathdoc
%U http://geodesic.mathdoc.fr/item/MBB_2017_12_2_a1/
%G ru
%F MBB_2017_12_2_a1
O. V. Senko; V. Ya. Chuchupal; A. A. Dokukin. Non-invasive arterial pressure estimating with the cardiac monitor CardioQvark. Matematičeskaâ biologiâ i bioinformatika, Tome 12 (2017) no. 2, pp. 536-545. http://geodesic.mathdoc.fr/item/MBB_2017_12_2_a1/

[1] Penazr M., Nock H., Khudanpur S., “Photoelectric measurement of blood pressure, volume and flow in the finger”, Digest of the 10th International Conference on Medical, and Biological Engineering, International Federation for Medical and Biological Engineering, Dresden, 1973, 904

[2] Silke B., McAuley D., “Accuracy and precision of blood pressure determination with the Finapres: an overview using re-sampling statistics”, J. Hum. Hypertens., 12:6 (1998), 403–409 | DOI

[3] Hofhuizen C. M., Lemson J., Hemelaar A. E. A., Settels J. J., Schraa O., Singh S. K., van der Hoeven J. G., Scheffer G. J., “Continuous non-invasive finger arterial pressure monitoring reflects intra-arterial pressure changes in children undergoing cardiac surgery”, British Journal of Anaesthesia, 105:4 (2010), 493–500 | DOI

[4] Takayuki Sato T., Nishinaga M., Kawamoto A., Ozawa T., Takatsuji H., “Accuracy of a Continuous Blood Pressure Monitor Based on Arterial Tonometry”, Hypertension, 21:6 (1993), 666–874

[5] Peripheral arterial tonometry with ascending aortic waveform analysis using the SphygmoCor system, February/March 2006, Application 1079, Assessment report (data obrascheniya: 27.11.2017 ) http://www.msac.gov.au/internet/ msac/ publishing.nsf/ Content/ 790058377BEF1DE7CA25801000123B2B/ \$File/1079-Assessment-Report.pdf

[6] SOMNOtouch$^{\mathrm{TM}}$ NIBP, SOMNOmedics GmbH, , 2015 (data obrascheniya: 27.11.2017) http://somnomedics.eu/fileadmin/SOMNOmedics/Dokumente/Studien/FolderBP_engl_Rev5_SOT_NIBP_2015-04-07_SB_mail.pdf

[7] Ahmad S., Chen S., Soueidan K., Batkin I., Bolic M., Dajani H., Groza V., “Electrocardiogram-assisted blood pressure estimation”, IEEE Trans. Biomed. Eng., 59:3 (2012), 608–618 | DOI

[8] Hennig A., Patzak A., “Continuous blood pressure measurement using pulse transit time”, Somnologie, 2013 | DOI

[9] Gesche H., Grosskurth D., Kuchler G., Patzak A., “Continuous blood pressure measurement by using the pulse transit time: comparison to a cuff-based method”, Eur. J. Appl. Physiol., 112:1 (2012), 309–315 | DOI

[10] Radha M., Zhang G., Gelissen J., de Groot K., Haakma R., Aarts R. M., “Arterial path selection to measure pulse wave velocity as a surrogate marker of blood pressure”, IOP Publishing Ltd Biomedical Physics Engineering Express, 3:1 (2017)

[11] Chen C. H., Nevo E., Fetics B., Pak P. H., Yin F. C., Maughan W. L., Kass D. A., “Estimation of central aortic pressure waveform by mathematical transformation of radial tonometry pressure. Validation of generalized transfer function”, Circulation, 95 (1997), 1827–1836 | DOI

[12] Payne R. A., Symeonides C. N., Webb D. J., Maxwell S. R. J., “Pulse transit time measured from the ECG: an unreliable marker of beat-to-beat blood pressure”, J. Appl. Physiol., 100 (2006), 136–141 | DOI

[13] Proenca J., Muehlsteff J., Aubert X., Carvalho P., Is pulse transit time a good indicator of blood pressure changes during short physical exercise in a young population?, Conf. Proc. IEEE Eng. Med. Biol. Soc., 2010, 598–601

[14] Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., Vanderplas J., Passos A., Cournapeau D., Brucher M., Perrot M., Duchesnay E., “Scikit-learn: Machine Learning in Python”, Journal of Machine Learning Research, 12 (2011), 2825–2830 | MR

[15] Breiman L., “Random Forests”, Machine Learning, 45:1 (2001), 5–32 | DOI

[16] Friedman J., “Greedy Function Approximation: A Gradient Boosting Machine”, The Annals of Statistics, 29:5 (2001) | DOI | MR

[17] Friedman J. H., Hastie T., Tibshirani R., “Regularization paths for generalized linear models via coordinate descent”, Journal of Statistical Software, 33:1 (2010) | DOI | MR