Algorithm for Detecting Reference Points on a Digital Electrocardiogram in Real Time

Relevance. The use of digital electrocardiographs and cardiac monitors with built-in algorithms for automatic processing, analysis and interpretation of electrocardiograms allows the doctor to effectively diagnose cardiac arrhythmias. It is known that in order to provide emergency care to a patient, the duration of arrhythmia diagnostics should not exceed several tens of seconds, which requires the emergence of new algorithms for detecting informative features indicating arrhythmia, operating in real time. The need to introduce new and effective technologies for diagnosing cardiovascular diseases is also reflected in public health development programmes.

Research goal. Development and quality indicators analysis of the algorithm for reference points detection on a digital electrocardiogram, bearing informative signs for the procedure of arteries diagnosis.

The methods used. The study is based on an analysis of existing approaches to the problem of reference points detection on digital electrocardiogram, as well as conducting a test of the proposed algorithms by mathematical modelling methods. The quality indicators of the algorithms defined in accordance with the principles of signal detection theory and diagnostic testing, at the junction of which the task of electrocardiogram reference point detection is located. The proposed algorithm was tested on materials of MIT-BIH Arrhythmia Database, which is widely used for verification and validation of real-time digital electrocardiogram signal processing algorithms.

The results. The study proposes an algorithm for detecting reference points on a digital electrocardiogram that carry informative features for the arrhythmia diagnostic procedure. The proposed algorithm is based on digital signal filtering using a decision rule based on a three-step two-threshold principle of pre-processed electrocardiogram signal values comparison on a sliding window. An experiment on the materials of the open verified MIT-BIH Arrhythmia DB showed that the quality of the proposed algorithm for detecting reference points is higher than that of the algorithms used in modern digital electrocardiographs and cardiac monitors. The proposed algorithm based on digital signal filtering and the three-step two-threshold decision rule have elements of scientific novelty.

The significance. The results of this work can be used in the development of digital heart rate monitors, cardiac devices and for automatic processing, analysis and real-time computer-assisted digital electrocardiogram signal interpretation.

1. Yuldashev Z.M. The duration of arrhythmia emergency diagnosis should not exceed several tens of seconds. Medvestnik. 2018. (in Russ.) URL: https://medvestnik.ru/content/interviews/Prodoljitelnost-diagnostiki-aritmii-dlya-okazaniya-ekstrennoi-pomoshi-ne-doljna-prevyshat-neskolkih-desyatkov-sekund.html [Accessed 30.09.2024]

2. Nesterova E.A. Electrocardiography Bases. Normal Electrocardiography (The Module for Continuous Medical Education). Cardiology: News. Opinions. Training. 2016;2(9):77‒85. (in Russ.) EDN:WFLXIP

3. Rudnitskiy L.V. Pocket Guide to Medical Analysis. St. Petersburg: Piter Publ.; 2014. 320 p. (in Russ.)

4. Akopyan B.K. Classification of heart rhythm disorder episodes by informative features in the electrocardiogram time domain. Journal of Instrument Engineering. 2024;67(4):305‒314. (in Russ.) DOI:10.17586/0021-3454-2024-67-4-305-314. EDN:DSWAXC

5. Ivanov G.G., Dvornikov V.E., Sbeytan S., Bulgakova E.Ju., Alexandrova M.R., Gribanov A.N. The Analysis of Parameters Structure Variability of Heart Rhythm at Healthy Persons on Data PP-and RR-Intervals Determinants of Evolution of ECG Signs of Left Ventricular. RUDN Journal of Medicine. 2007;4:26‒34. (in Russ.) EDN:JVDXGH

6. Antsiperov V.E., Zabrosayev I.V., Rastyagayev D.V. Detection of Heart Rhythm Disturbances Using Analytical Spectra. Journal of Radio Electronics. 2015;12:16. (in Russ.) EDN:VHTMVJ

7. Friesen G.M., Jannett T.C., Jadallah M.A., Yates S.L., Quint S.R., Nagle H.T. A comparison of the noise sensitivity of nine QRS-detection algorithms. IEEE Transactions on Biomedical Engineering. 1990;37(1):85‒98. DOI:10.1109/10.43620

8. Akopyan B. Development of the automated cardiac rhythm disorders detection and classification algorithm. Bulletin of the UNESCO Department “Distance education in engineering” of the SUAI. 2022;7:28‒31. EDN:QSKIML

9. Kohler B.-U., Hennig C., Orglmeister R. The principles of software QRS detection. IEEE Engineering in Medicine and Biology Magazine. 2002;21(1):42‒57. DOI:10.1109/51.993193.

10. Zong W., Moody G.B., Jiang D. A robust open-source algorithm to detect onset and duration of QRS-complexes. Proceedings of the Conference on Computers in Cardiology, 21‒24 September 2003, Thessaloniki, Greece. IEEE; 2003. p.737‒740. DOI:10.1109/CIC.2003.1291261

11. Zharinov O., Zharinov I. Correlation-extreme method for detection tasks solution and time points estimation of QRS-complexes in an electrocardiogram. Scientific and Technical Journal of Information Technologies, Mechanics and Optics. 2011; 5(75):85‒90. (in Russ.) EDN:OCBFFH

12. Bozhenko V.V., Chernysh N.Yu., Tatarnikova T.M. Data Mining in the Diagnosis of Anemia by Clinical Indicators. Journal of Instrument Engineering. 2024;67(4):321‒329. DOI:10.17586/0021-3454-2024-67-4-321-329. (in Russ.) EDN:AUAHNY

13. Raskopina A.S., Bozhenko V.V., Tatarnikova T.M. Using Deep Learning in Pneumonia Diagnosis from X-Rays Patterns. Journal of Instrument Engineering. 2024;67(4):315‒320. DOI:10.17586/0021-3454-2024-67-4-321-329. (in Russ.) EDN:UPSNQQ

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