The information technology for rapid determination life-threatening states of the cardiovascular system
DOI:
https://doi.org/10.52575/2687-0932-2021-48-1-130-141Keywords:
cardiovascular diseases, ECG, digital signal processing, mobile device, QRS complex, DTW methodAbstract
The paper examines data flow models and stages of digital signal processing in a mobile device for the operational determination of life-threatening conditions of the cardiovascular system. The device records the patient's signal electrocardiography (ECG) and analyzes various types of cardiac arrhythmias. When a life-threatening condition is detected, the device generates a message to the patient and transmits ECG data to the doctor's computer. A feature of this device is its personification and the reliability of the capabilities of the QRS complexes. Reliability is due to the fact that, in addition to approaches to the construction of digital signal processing algorithms, QRS comparison methods are also used, algorithms for comparing selected complexes with templates are used. An algorithm based on the dynamic time warping (DTW) method to analyze the shape QRS complex is used. If all the specified patterns are at a great distance from the highlighted QRS complex, then it is considered that this complex was highlighted in error. The DTW method is widely used for processing audio signals, in this context, its use is new.
Acknowledgements: the study was carried out with the financial support of the Russian Foundation for Basic Research, project No 18-413-310002.
Downloads
References
Ефремова О.А., Никитин В.М., Чурносов М.И., Камышникова Л.А., Липунова Е.А., Муромцев В.В. 2016. Виртуальный способ оценки риска развития ишемической болезни сердца у носителей полиморфных кардиогенов. Научные ведомости Белгородского государственного университета. Серия «Медицина. Фармация», 26 (247): 76–83.
Муромцев В.В., Никитин В.М., Ефремова О.А., Камышникова Л.А. 2019. Подход к улучшению автоматизированной системы компьютерного анализа электрокардиограммы. Медицинские технологии. Оценка и выбор, 2 (36): 42–48.
Обухов С.А., Степанов В.П. 2019. Алгоритм обнаружения QRS-комплекса на электрокардиограмме в реальном времени. Инженерный журнал: наука и инновации, 5. [Электронный ресурс] URL: http://dx.doi.org/10.18698/2308-6033-2019-5-1877. (дата обращения
декабря 2020).
Петров С.П., Епишина Е.В., Воронин В.В. 2014. Оценка алгоритмов распознавания образов для задач автоматического анализа электрокардиограмм. Евразийский союз ученых,
(8–8): 27–29. [Электронный ресурс] URL: https://euroasia-science.ru/wp-content/uploads/2016/11/ evro_8p8_6-169.pdf (дата обращения 12 декабря 2020).
Рослякова А.В., Чупраков П.Г. 2012. Сравнительный анализ алгоритмов обнаружения
R-зубца Электрокардиосигнала. Вятский медицинский вестник, 2: 29–35.
Черемных С.В., Семенов И.О., Ручкин В.С. 2001. Структурный анализ систем:
IDEF-технологии. М., Финансы и статистика, 208.
Al-Naymat G., Chawla S., Taheri J. 2012. SparseDTW: A Novel Approach to Speed up Dynamic Time Warping arXiv:1201.2969v1 [cs.DB]. Available at: https://arxiv.org/pdf/1201.2969v1.pdf (accessed 12 December 2020).
Ansari S., Farzaneh N., Duda M., Horan K., Andersson H.B. 2017. Review of Automated Methods for Detection of Myocardial Ischemia and Infarction Using Electrocardiogram and Electronic Health Records. IEEE Reviews in Biomedical Engineering, 10: 264–298.
Choi E., Bahadori M.T., Schuetz A., Stewart W.F., Sun J. 2016. Doctor AI: Predicting clinical events via recurrent neural networks. In Machine Learning for Healthcare Conference. 56: 301–318.
Fernandez Biscay C., Arini P.D., Rincón Soler A.I. et al. 2020. Classification of ischemic and non-ischemic cardiac events in Holter recordings based on the continuous wavelet transform. Medical & Biological Engineering & Computing, 58, 1069–1078 [Electronic resource] Available at: https://doi.org/10.1007/s11517-020-02134-8. (accessed 12 December 2020).
LabVIEW. National Instruments. 2020. [Electronic resource] Available at: http://www.ni.com/. (accessed 12 December 2020).
Muromtsev V.V., Nikitin V.M., Efremova O.A., Kamyshnikova L.A., Ushakova S.N. 2020. One of the approaches to automating the analysis of the shape of ECG sections International Journal of Advanced Research in Engineering and Technology (IJARET), 11(7): 179–186.
Pan J., Tompkins W.J. 1985. A real time QRS detection algorithm. IEEE transactions on Biomedical Engineering, 32: 230–236.
PhysioBank. Databases. 2020. [Electronic resource]. Available at: http://physionet.org/physiobank/database/ (accessed 12 December 2020).
Sandau K.E., Funk M., Auerbach A., Barsness G.W., Blum K., Cvach M., Lampert R., May J.L., McDaniel G.M., Perez MV. 2017. Update to practice standards for electrocardiographic monitoring in hospital settings: a scientific statement from the American heart association. Circulation, 136 (19): e273–e344.
Steinhubl S.R., Waalen J., Edwards A.M., et al. 2018. Effect of a Home-Based Wearable Continuous ECG Monitoring. Patch on Detection of Undiagnosed Atrial Fibrillation: The mSToPS Randomized Clinical Trial. JAMA, 320 (2): 146–155.
Surawicz B., Knilans T. 2008. Chou’s Electrocardiography in Clinical Practice. 6th Edition. Saunders, 752.
Visio. 2020. [Electronic resource]. Available at: https://www.microsoft.com/ru-ru/microsoft-365/visio/flowchart-software. (accessed 12 December 2020).
Abstract views: 100
Share
Published
How to Cite
Issue
Section
Copyright (c) 2021 ECONOMICS. INFORMATION TECHNOLOGIES
This work is licensed under a Creative Commons Attribution 4.0 International License.
