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Evaluation of cardiac intervals order and length regularity with the use of mathematical statistics methods

Keywords:

S.V. Motorina - Post-graduate Student, Department of Biotechnical Systems, Saint Petersburg Electrotechnical University «LETI» E-mail: motorina_sv@mail.ru A.N. Kalinichenko - Dr.Sc. (Eng.), Professor, Department of Biotechnical Systems, Saint Petersburg Electrotechnical University «LETI» E-mail: ank-bs@yandex.ru


This work is devoted to the development of new cardiac rhythm analysis methods for the automatic ECG monitoring devices and systems. The approach based on different forms of cardiac intervals sequence representation statistical analysis is presented. The following forms of cardiac intervals graphical presentation were considered: Poincare plots (a set of points having coordinates equal to two adjacent RR-interval values). Phase portrait vectors (set of lines connecting consequent points of Poincare plot) and sets of points in 2-D and 3-D domains of these vectors directions. Each of the listed above representation forms produce compact groups of points in case of normal rhythm or extrasystoles while for atrial fibrillation the uniform distribution of these points is characteristic. The most adequate clusterization method was defined for each form of presentation. The optimal number of clusters was determined with the use of Davies-Bouldin and Duda-Hart criteria. The intergroup and intragroup distances between formed clusters were used as indexes for the differentiation between atrial fibrillation and other types of cardiac rhythm. The best differentiation was achieved in case when the joint components method was applied to the 2-D representation of the vectors directions values. Only intergroup variance works as an informative index in this case. The threshold value corresponding to minimal error of atrial fibrillation detection was determined. The obtained values of the presented method quality estimations correspond to the level of the best published atrial fibrillation detection algorithms.
References:

 

  1. Moody G.B., Mark R.G. A new method for detecting atrial fibrillation using R-R intervals // Computers in Cardiology. 1983. № 10. R. 227-230.
  2. Logan B., Healey J. Detection of Atrial Fibrillation for a Long Term Telemonitoring System // Computers in Cardiology. 2005. № 32. R. 619-622.
  3. Linker D.T. Long-Term Monitoring for detection of Atrial Fibrillation. Seattle, US: Patent Application Publication. 2006. 498 r.
  4. Tatento K., Glass L. Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of RR and RR intervals // Medical& Biological Engineering & Computing. 2001. № 39. R. 664-671.
  5. Cerutti S., Mainardi L.T., Porta A., Bianchi A.M. Analysis of the dynamics of RR interval series for the detection of atrial fibrillation episodes // Computersin Cardiology. 1997. № 24. R. 77-80.
  6. Slocum J., Sahakian A., Swiryn S. Diagnosis of Atrial Fibrillation From Surface Electrocardiograms Based on Computer-detected Atrial Activity // Journal of Electrocardiology. 1992. № 25. R. 1-8.
  7. Schmidt R., Harris M., Novac D., Perkhun M. Atrial Fibrillation Detection. Eindhoven, Netherlands: Patent Cooperation Treaty. 2008. 731 r.
  8. Babaeizadeh S., Gregg R., Helfenbein E., Lindauer J., Zhou S. Improvements in atrial fibrillation detection for real-timemonitoring // Journal of Electrocardiology. 2009. № 42 R. 522-526.
  9. Couceiro R., Carvalho P., Henriques J., Antunes M., Harris M., Habetha J. Detection of Atrial Fibrillation using model-based ECG analysis // 19th International Conference on Pattern Recognition. Tampa. 2008. R. 1-5.
  10. Motorina S.V., Kalinichenko A.N. Algoritm raspoz-navanija mercatelnojj aritmii na osnove graficheskikh metodov // Izvestija SPbGEHTU «LEHTI». 2014. № 10. S. 55-60.
  11. Ajjvazjan S.A., Bukhshtaber V.M., Enjukov I.S., Meshalkin L.D. Prikladnaja statistika: Klassifikacija i snizhenie razmernosti. M.: Finansy i statistika. 1989. 607 s.
  12. Bondarev V.A., Lisicyna A.V., Menshutina N.V. Primenenie pravil ostanovki klasternogo analiza v sluchae slabojj i silnojj ierarkhii klasterov na primere belkovykh struktur // Uspekhi v khimii i khimicheskojj tekhnologii. 2007. №1. S. 105-109.
  13. JAckiv I., Gusarova L. Metody opredelenija kolichestva klasterov pri klassifikacii bez obuchenija // Transport and Telecommunication. 2003. №1. S. 23-28.
  14. Physionet: the research resource for physiologic signals. www.physionet.org.
  15. Sajjt proekta CardioQVARK: www.cardioqvark.ru.

 

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