Internet 
Українська  English  Русский  

DOI: https://doi.org/10.31071/kit2019.15.03


Inventory reference ISSN 1812-7231 Klin.inform.telemed. Volume 14, Issue 15, 2019, Pages 46-52


Author(s) G. Raimondi1, A. Martynenko2, N. Marchitto3, S. Ostropolets2


Institution(s)

1University of Roma "Sapienza", Italy

2V. N. Karazin Kharkiv National University, Ukraine

3ASL Latina, Italy


Article title Artificial intelligence for heart rate variability analyzing with arrhythmias


Abstract (resume)

Introduction. Existing standards of Heart Rate Variability (HRV) technology limit its use to sinus rhythm. A small number of extrasystoles is allowed, if the device used has special procedures for the detection and replacement of ectopic complexes. However, it is important to expand the indicated limits of the applicability of the HRV technology. This specially regards the cases when the HRV technology looks promising in the diagnostics, as, for example, in atrial fibrillation and atrial flutter.

Materials and Methods. All ECG measurements were performed on XAI-MEDICA® equipment and software. Processing of the obtained RR Series was carried out using the software Kubios® HRV Standard. All recommended HRV characteristics for Time-Domain, Frequency-Domain and Nonlinear were calculated.

The purpose of the work. The article presents an artificial intelligence (AI) procedure for detecting episodes of arrhythmias and reconstruction of core patient's rhythm, and demonstrates the efficacy of its use for the HRV analysis in patients with varying degrees of arrhythmias.

The results of the study. It was shown efficiency of developed artificial intelligence procedure for HRV analyzing of patients with different level of arrhythmias. These were demonstrated for Time-Domain, Frequency-Domain and Nonlinear methods. The direct inclusion into review of Arrhythmia Episodes and the use of the initial RR Series leads to a significant distortion of the results of the HRV analysis for the whole set of methods and for all considered options for arrhythmia.

Conclusion. High efficacy of operation of the procedure AI core rhythm extraction from initial RR Series for patients with arrhythmia was reported in all cases.


Keywords Hearth rate variability, Arrhythmias, Artificial intelligence


References

1. Task force of the European society of cardiology and the North American society of pacing and electrophysiology. Heart rate variability — standards of measurement, physiological interpretation, and clinical use. Circulation, 1996, vol. 93, iss. 5, pp. 1043–1065.

2. Yabluchansky N., Martynenko A. Variabel'nost' serdechnogo ritma v klinicheskoj praktike [Heart Rate Variability for clinical practice]. Kharkiv, Univer. Press, 2010. 131 p. (in Russ.).

3. Attia Z. I., Noseworthy P. A., Lopez-Jimenez F, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. The Lancet. 2019.
doi: 10.1016/S0140-6736(19)31721-0

4. Turing A. M. Computing machinery and intelligence. Mind. 1950, vol. 59, pp. 433–60.

5. Lusted L. B. Medical progress — medical electronics. N. Engl. J. Med. 1955, vol. 252, pp. 580–585.

6. Ledley R. S., Lusted L. B. Reasoning foundations of medical diagnosis. Science. 1959, vol. 130, pp. 9–21.

7. Ramesh A. N., Kambhampati C., Monson J. R. T., Drew P. J. Artificial intelligence in medicine. Ann. R. Coll. Surg. Engl. 2004, vol. 86, pp. 334–338.
doi: 10.1308/147870804290

8. Jiang F., Jiang Y., Zhi H., et al. Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology. 2017, p.2. e000101.
doi: 10.1136/svn-2017-000101

9. Darcy A. M., Louie A. K., Roberts L. Machine Learning and the Profession of Medicine. JAMA, 2016, iss. 315, pp. 551–5522.
doi: 10.1001/jama.2015.18421

10. Murff H. J., Fitz H. F., Matheny M. E., et al. Automated identification of postoperative complications within an electronic medical record using natural language processing. JAMA, 2011, vol. 306, pp. 848–855.
doi: 10.1001/jama.2011.1204

11. Goodfellow I., Bengio Y., Courville A. Deep Learning. First Edition: The MIT Press. 2016, 800 p.

12. Pattern recognition and machine Learning (Information Science and Statistics). Ed. Bishop C. M. Springer, 2011, 738 p.


Full-text version http://kit-journal.com.ua/en/viewer_en.html?doc/2019_15/003.pdf