DOI: https://doi.org/10.31071/kit2021.17.10

Inventory reference

ISSN 1812-7231(print), ISSN 2786-5800 (Online) Klin. inform. telemed. Volume 16, Issue 17, 2021, Pages 61-68

Author(s)

A. V. Savchuk, N. G. Ivanushkina

Institution(s)

National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute»

Article title

Analysis of electrocardiograms in combustiology using a registration system based on capacitive electrodes

Abstract (resume)

Introduction. With extensive skin damage there is a risk of disruption of the vital functions of life-threatening body functions. Patients need special long-term rehabilitation in the hospital under the supervision of physicians with the ability to monitor heart rate parameters and heart rate variability (HRV). With significant damage of the skin of the chest or the use of rehabilitation aids, it is often not possible to use classical electrodes to measure the electrocardiogram (ECG).

Materials and methods. An ECG signal recording system based on capacitive electrodes was used to record the ECG signal. The aim of the study was to prove the possibility of analysis of HRV based on ECG signals recorded with capacitive electrodes, provided that there are means for rehabilitation. ECG signals were recorded from pure prepared skin, as well as by applying gauze bandages soaked in saline and polyvinyl chloride (PVC) film on the skin. Data were recorded from one person and in both cases were filtered and used to calculate HRV parameters.

Results and discussion. On the base of results of ECG analysis recorded with capacitive electrodes and HRV estimation, it is shown that the pyHRV and NeuroKit2 libraries can be used for further research in the analysis of HRV of patients with burns. The obtained Poincare graphs and R-peak-synchronized QRS complexes with additional processing may be indicators of arrhythmia, extrasystole or absence of certain R waves in the ECG during daily monitoring and rehabilitation of patients in burn units.

Conclusion. HRV analysis showed that capacitive electrode-based ECG recording systems can be used for further research in the field of rehabilitation of patients with extensive skin lesions. Proposed method will expand the range of clinical indicators for practical use by combustiologists in everyday clinical practice. ECG recorded with capacitive electrodes and when using means for rehabilitation of patients with burns can be used for HRV analysis.

Keywords

Electrocardiogram; Capacitive electrodes; Modeling; Burns; Burn injury; Combustiology.

References

1. Burn key facts. World Health Organization (WHO), Geneva, 2018.

2. Preventable injuries kill 2000 children every day. World Health Organization (WHO), Geneva, 2008.

3. Falk L. Investigation of ECG electrodes for burn wounds. Student thesis. Uppsala University, Sweden, 2020, 40 с.

4. Яблучанский Н. И. Мартыненко А. В. Вариабельность сердечного ритма в помощь врачу. Для настоящих врачей. КНУ, Харків, 2010, 131 с.

5. Ryan M. L., Thorson C. M., Otero C. A., Vu T., Proctor K.G. Clini-cal Applications of Heart Rate Variability in the Triage and Assessment of Traumatically Injured Patients. Anesthesiology Research and Practice, 2011 vol. 2011, pp. 51–57. doi: 10.1155/2011/416590.

6. Gao Y., Soman V. V., Lombardi J. P., Rajbhandari P. P., Dhakal T. P., Wilson D. G., Poliks M. D., Ghose K., Turner J. N., Jin Z. Heart Monitor Using Flexible Capacitive ECG Electrodes. IEEE Transactions on Instrumentation and Measurement, 2020, vol. 69, no. 7, pp. 4314–4323. doi:10.1109/TIM.2019.2949320.

7. Lee J., Pearce F., Hibbs A., Matthews R., Morrissette C. Evalu-ation of a Capacitively-Coupled, Non-Contact (through Cloth-ing) Electrode or ECG Monitoring and Life Signs Detection for the Objective Force Warfighter. RTO-MP-HFM-109, 2004.

8. Savchuk A. V., Popov A. O. Multichannel wireless electronic system for recording the signal of the surface electro-myogram. Electronics and Communications, 2016; vol. 21(3), pp. 1–57. doi: 10.20535/2312-1807.2016.21.3.68106.

9. Ueno A., Akabane Y., Kato T., Hoshino H., Kataoka S., Ishiy-ama Y. Capacitive Sensing of Electrocardiographic Potential Through Cloth From the Dorsal Surface of the Body in a Su-pine Position: A Preliminary Study. IEEE Transactions on Bio-medical Engineering, 2007, vol. 54(4), pp. 759–766. doi: 10.1109/TBME.2006.889201.

10. Ramos G., Alfaras M., Gamboa H. Real-Time Approach to HRV Analysis. Proceedings of the 11th International Joint Confer-ence on Biomedical Engineering Systems and Technologies, 11th International Conference on Bio-inspired Systems and Sig-nal Processing. 2018, Funchal, Madeira, Portugal, pp. 208–215. doi: 10.5220/0006641402080215.

11. Morbiducci U., Scalise L., De Melis M., Grigioni M. Optical Vibrocardiography: A Novel Tool for the Optical Monitoring of Cardiac Activity. Annals of Biomedical Engineering, 2007, vol. 35(1), pp. 45–58. doi: 10.1007/s10439-006-9202-9.

12. Loguidice M., Schutt R., Horton J., Minei J., Keeley E.. Heart Rate Variability as a Predictor of Death in Burn Patients, Journal of Burn Care & Research, 2016, vol. 37(3): pp. 227–233. doi: 10.1097/BCR.0000000000000260.

13. Савчук А . Безпровідна система для вимірювання біопотенціалів на основі ємнісних електродів, ХVII Міжнародна науково-технічна конференція «Фізичні процеси та поля технічних і біологічних об’єктів». Кременчук, Україна, 2018, 118–120 с.

14. Gomes P., Margaritoff P., and da Silva H. P., pyHRV: Development and evaluation of an open-source python toolbox for heart rate variability (HRV). Proc. Int’l Conf. on Electrical, Electronic and Computing Engineering (IcETRAN), 2019, pp. 822–828.

15. Makowski D., Pham T., Lau Z. J., Brammer J. C., Lespinasse F., Pham H., Scholzel C., & Chen S. A. NeuroKit2: A Python toolbox for neurophysiological signal processing. Behavior Research Methods, 2021, vol. 53(4), pp. 1689–1696. doi: 10.3758/s13428-020-01516-y.

16. Kalidas V., Tamil L. Real-time QRS detector using Stationary Wavelet Transform for Automated ECG Analysis. IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE), 2017, Washington, DC. doi: 10.1109/BIBE.2017.00-12.

17. Lipponen J., Tarvainen M. A robust algorithm for heart rate variability time series artifact correction using novel beat classification. Journal of Medical Engineering & Technology, 2019, vol. 43(3), pp. 173–181. doi: 10.1080/03091902.2019.1640306.

18. Shaffer F., Ginsberg J.P. An Overview of Heart Rate Variability Metrics and Norms. Frontiers in Public Health, 2017, vol. 5, p. 258. doi: 10.3389/fpubh.2017.00258.

19. Umetani K., Singer D., McCraty R., Atkinson M. Twenty-Four Hour Time Domain Heart Rate Variability and Heart Rate: Relations to Age and Gender Over Nine Decades. Journal of the American College of Cardiology, 1998, vol. 31(3), pp. 593–601. doi: 10.1016/S0735-1097(97)00554-8.

20. Savchuk A. Development of a model of electric impedance in the contact between the skin and a capacitive active electrode when measuring electrocardiogram in combustiology. Eastern-European Journal of Enterprise Technologies, 2021, vol. 2(5 (110)), pp. 32–38. doi: 10.15587/1729-4061.2021.228735.

Full-text version http://kit-journal.com.ua/en/viewer_en.html?doc/2021_17/010.pdf