Radiotekhnika
Publishing house Radiotekhnika

"Publishing house Radiotekhnika":
scientific and technical literature.
Books and journals of publishing houses: IPRZHR, RS-PRESS, SCIENCE-PRESS


Тел.: +7 (495) 625-9241

 

New Geometric Method of Heart Rate Variability Estimation based on the Multiscale Crrelation Analysis Representation

DOI 10.18127/j15604136-2018007-20

Keywords:

Viacheslav Antsiperov - Kotelnikov Institute of Radioengineering and Electronics of Russian Academy of Sciences, Mokhovaya 11–7, Moscow, Russia 

Contact: antciperov@cplire.ru


The nature and characteristics of ECG signals have been the subject of research for more than 100 years. This is because ECG signals contain a valuable diagnostic infor- mation about the functioning of the heart and, indirectly, about some other organs. Now, despite the impressive track record of the diagnostics and recommendations already de- veloped [1], ECG signals continue to be the subject of in- tensive study both in the field of cardiology and biomed- ical engineering. Cardiologists search for new diagnostic properties of ECG signals, while engineers are interested in new approaches and methods for such signals process- ing and analysing. New methods imply, in particular, new techniques of noise suppression, efficient signal represen- tation and feature extraction.
Feature extraction is an essential step in ECG processing. It consists in formation of some feature patterns – a set of descriptors most adequately describing the signal. Among these features / descriptors the most important are those with diagnostic properties. As a rule, they represent time domain parameters, though sometimes they can be defined in the frequency domain. Basing on ECG patterns, one can make some initial diagnosis [1]. For example, an “irregu- lar” QRS complex without P wave is the hallmark of atrial fibrillation, the shape of the QRS with a left bundle branch block indicates a risk of cardiomyopathy, etc.
ECGpatterns, especiallyP–QRS–Tcomplexes, arealsothe basis for the subsequent heart rate analysis. Usually it is a more complex phase of ECG signal processing. However, now there are many methods and approaches related, for example, to the heart rate variability (HRV) analysis [2]. Quantitative evaluation of the HRV is usually based on the calculation of some indices connected with the variability of sequential NN intervals (normal–to–normal intervals be- tween adjacent QRS complexes). Some of these indices are determined in the time domain – SDNN, CV, RMSSD, PNN5O, etc. Others – in the frequency domain – VLF, LF, HF spectral power components, IC index, etc. It should be noted that, due to the complexity of the EEG signal, its fractal–like nature, the values of almost all indices can es- sentially diverge for different lengths of time intervals anal- ysed, even for the same record. Therefore, the European Society of Cardiology suggests differentiating the diagnos- tic methodologies based on HRV indices obtained from the short–term recordings (of 2 to 5 min) and from the long– term recordings (of entire 24–h period) [3].
The above discussion is illustrated in Figure 1 by cardioin- tervalograms (CIG, the NN interval dependences on time) for a healthy person (A) and a heart disease person (B). It is clear from the figure that both CIGs are qualitatively dif- ferent. For example, at a small temporal scale, the variability (spread) of NN intervals in a healthy person is greater, which is a well known fact [5] – but on large scales the vari- ability becomes equal in both cases. This observation sug- gests that the diagnosis based on HRV is adequte, but di- agnostic procedures (automatic in particular) may be non- trivial. This circumstance gives high relevance to the de- velopment of new ECG processing methods based on long records – more than 10 minutes, for example, Holter.

References:
  1. J.S. Coviello. ECG interpretation made incredibly easy! Philadelphia: Wolters Kluwer, 3 edition, 2017.
  2. R.M. Baevsky and A.G. Chernikova. Heart rate variabil- ity analysis: physiological foundations and main methods. Cardiometry, (10):66–76, 2017.
  3. Task Force of the European Society of Cardiology, the North American Society of Pacing, and Electrophysiol- ogy. Heart rate variability. standards of measurement, physiological interpretation, and clinical use. Eur Heart J, 7(3):354–381, 1996.
  4. A.L. Goldberger and H.E. Stanley et al. Long-range an- ticorrelations and non-gaussian behavior of the heartbeat. Phys Rev Letters, 70(9):1343–1346, 1996.
  5. S.S.Yamamoto J.F. Thayer and J.F. Brosschot. The re- lationship of autonomic imbalance, heart rate variability and cardiovascular disease risk factors. Phys Rev Letters, 141(2):122–131, 2010.
  6. M. Malik. Geometrical methods for heart rate variability assessment. In M. Malik and A. Camm, editors, Heart Rate Variability, pages 47–61. Armonk, Futura Publishing Company -New York, 1995.
  7. V.E. Antsiperov and I.V. Zabrosaev. New results for the pvc / spb detection using based on the mca heart rhythm estimation method. In Proceedings of the 12th Russian- German Conference on Biomedical Engineering, pages 182–186. Vladimir State University, jul 2016.
  8. V.E. Antsiperov. Syclic renewal point processes for heart rate variability modeling. In Extended abstracts of the sec- ond Russian Conference with international participation "Physics for Life Sciences", page 61. Ioffe Institute sience- tech information department, sen 2017.
  9. R. Serfozo. Basics of Applied Stochastic Processes. Springer, -Berlin, -Heidelberg, 2009. [10] L.A.N. Amaral A.L. Goldberger and et al. P.h. Ivanov. Physiobank, physiotoolkit, and physionet: Components of a new research resource for complex physiologic signals. Circulation, 101(23):e215–e220, 2000

© Издательство «РАДИОТЕХНИКА», 2004-2017            Тел.: (495) 625-9241                   Designed by [SWAP]Studio