Ballistocardiogram analysis on edge computing nodes
https://doi.org/10.15514/ISPRAS-2018-30(2)-12
Abstract
In this paper we present the contactless method of analyzing the cardiac activity of a person based on recording and analyzing a ballistic cardiogram signal. A measuring device for registration of microscopic movements of the body uses a piezoelectric sensor of high sensitivity. Due to sensor’s high sensitivity, the level of background noise is higher than the signal level, so mathematical methods are used for noise reduction. Butterworth filter is used to extract cardiac signal. This approach is more computationally efficient compared to machine learning-based methods, and can be implemented on an edge computing node to which several sensors are connected. The quality of the signal obtained after filtration allows us to detect cardiac cycles. The algorithm used for detection of heartbeats proposed in this paper is also computationally simple enough to be implemented at the edge node. After preprocessing described above data is transmitted to the datacenter (cloud).
About the Authors
A. S. Nuzhny
Nuclear Safety Institure of the Russian Academy of Sciences
Russian Federation
Russian Federation
A. A. Prozorov
Moscow Institute of Physics and Technology
Russian Federation
Russian Federation
V. I. Bugaev
Moscow Institute of Physics and Technology
Russian Federation
Russian Federation
N. D. Shuvalov
Moscow Institute of Physics and Technology
Russian Federation
Russian Federation
V. V. Podumov
Moscow Institute of Physics and Technology
Russian Federation
Russian Federation
References
1. https://forinnovations.ru.
2. J.W. Gordon. Certain molar movements of the human body produced by the circulation of the blood. J. Anatomy Physiol., vol. 11, 1877, pp. 533–536.
3. A. S. Devyatisilny, “Intrapretation of optical accelerometer measurements”, Journal of technical physics, vol 74, issue 9, 2004, pp. 141-142 (in Russian).
4. RMFIT R-series sensor. https://www.emfit.com/r-series-sensors.
5. A. Ahmed and E. Ahmed. A survey on mobile edge computing. In Proc. of the 10th International Conference on Intelligent Systems and Control (ISCO), 2016, pp. 1-8.
6. W. Shi, J. Cao, Q. Zhang, Y. Li and L. Xu. Edge Computing: Vision and Challenges. IEEE Internet of Things Journal, vol. 3, no. 5, Oct. 2016, pp. 637-646.
7. Prozorov A.A., Bugaev V.I., Tsarenko S.V. A device for continuous contactless measurements of major biometric indexes of a lying patient: Russian patent application №2017101824 from 20 Jan. 2017.
8. Joonas Paalasmaa. Monitoring Sleep with Force sensor Measurement. Doctoral dissertation, Department of Computer Science, Series of Publications A, report A-2014-2. 2014, 59 p.
9. W. Chen, X. Zhu, T. Nemoto, K. Kitamura, K. Sugitani, and D. Wei. Unconstrained monitoring of long-term heart and breath rates during sleep. Physiol. Meas., vol. 29, , 2008, pp. N1–N10.
10. O. Postolache, P. Silva Girao, G. Postolache, and M. Pereira. Vital signs monitoring system based on EMFi sensors and wavelet analysis. In Proc. of the Instrumentation and Measurement Technology Conference, 2007, pp. 1–4.
11. W. Xu, W. A. Sandham, A. C. Fisher, and M. Conway. Detection of the seismocardiogram W complex based on multiscale edges. In Proc. of the 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1996, pp. 1023–1024.
12. D. H. Phan, S. Bonnet, R. Guillemaud, and N. Y. P. T. E. Castelli. Estimation of respiratory waveform and heart rate using an accelerometer. In Proc. of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology, 2008, pp. 4916–4919.
13. Joan Gomez-Clapers , Albert Serra-Rocamora , Ramon Casanella , Ramon Pallas-Areny. Towards the standardization of ballistocardiography systems for J-peak timing measurement. Measurement, vol. 58, 2014, pp. 310-316
14. S. Junnila, A. Akhbardeh, A. V¨arri, and T. Koivistoinen. An EMFi-film sensor based ballistocardiographic chair: Performance and cycle extraction method. In Proc. IEEE Workshop on Signal Processing Systems Design and Implementation, 2005, pp. 373–377.
15. Christoph Bruser, Kurt Stadlthanner, Stijn de Waele, and Steffen Leonhardt. Adaptive Beat-to-Beat Heart Rate Estimation in Ballistocardiograms. IEEE Transactions on Information Technology in Biomedicine, vol. 15, issue 5, Sept. 2011, pp. 778-786.
16. S. Haykin. Adaptive Filter Theory. 4rd Edition. Paramus, NJ: Prentice-Hall, 2001.
17. J. Paalasmaa and M. Ranta. Detecting heartbeats in the ballistocardiogram with clustering. In Proc. of the ICML/UAI/COLT 2008 Workshop on Machine Learning for Health-Care Applications, 2008, pp. 1-4.
18. J. H. Shin, B. H. Choi, Y. G. Lim, D. U. Jeong, and K. S. Park. Automatic ballistocardiogram (BCG) beat detection using a template matching approach. In Proc. of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008, pp. 1144–1146.
Review
For citations:
Nuzhny A.S., Prozorov A.A., Bugaev V.I., Shuvalov N.D., Podumov V.V. Ballistocardiogram analysis on edge computing nodes. Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2018;30(2):251-262. (In Russ.) https://doi.org/10.15514/ISPRAS-2018-30(2)-12
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