Analiza wpływu wybranych czynników na entropię wieloskalową rytmu pracy serca w populacji badanych pochodzącej z laboratorium snu

• Autorzy: Radliński J. , Baran Z. , Tomalak W.

Warianty tytułu

EN

Multiscale entropy analysis of the influence of selected factors on heart rate time series in sleep laboratory based cohort

• Języki publikacji: PL

Abstrakty

PL

Celem pracy jest wstępna analiza wpływu czynników: fizjologicznego - wieku oraz chorobowych - zaburzeń oddychania, na entropię wieloskalową (Multiscale Entropy) rytmu pracy serca. W pracy dokonano analizy wpływu wieku na dwóch szerokich grupach badanych: osobach zdrowych (wiek od 1,5 do 63 lat) oraz grupie w wąskim przedziale wiekowym (40-50 lat) o różnym zakresie występowania zaburzeń oddychania w czasie snu (wskaźnik liczby zaburzeń oddychania RDI - Respiratory Disturbance Index 0,5 - 111 1/h). Wykazano wpływ wieku na wartość entropii (zmniejszanie), szczególnie w grupie osób dorosłych, oraz wpływ zaburzeń oddychania i zjawisk im towarzyszących (wybudzenia).

EN

High costs and complication of standard polysomnography (PSG) lead to attempts to develop cheaper and less complicated methods. The analysis of heart rate complexity using non-linear dynamics methods seems to be promising, however the dynamics of heart rate is biased by several physiological factors. The aim of that study was to check the influence of a physiological factor - age and of respiratory disorders during sleep on heart rate variability analysed by multiscale entropy (MSE). The two groups were selected from archived measurements from the Sleep Laboratory of Institute for TBC and Lung Diseases Rabka Branch: a healthy group and a group of semi-constant age but without limitations to those disorders (Table 1). In both groups the full night diagnostics PSG according to the American Academy of Sleep Medicine (AASM) rules was performed. The R-R intervals were detected in the recorded ECG signal (250Hz), and the multiscale entropy (Goldberg's MSE) was calculated. We found high correlation between the entropy and age (Figs. 1, 2, 3) in adults, however in the children group (age<15) there was no such relation. Similar results were found in analysis of the influence of respiratory disorders on the RR time series entropy (Figs. 4, 5, 6, 7). The results lead to a conclusion that heart rate complexity described with use of the MSE analysis is strongly biased by age. MSE could also detect changes in RR time series associated with respiratory disorders during sleep. The further investigations should be performed to

Słowa kluczowe

PL

entropia; entropia wieloskalowa; rytm pracy serca; EKG; zaburzenia oddychania

EN

entropy; multiscale entropy; heart rate; ECG; respiratory disorders

• Wydawca: Wydawnictwo PAK
• Czasopismo: Pomiary Automatyka Kontrola
• Rocznik: 2012
• Tom: R. 58, nr 4
• Strony: 365--368
• Opis fizyczny: Bibliogr. 23 poz., rys., tab.

Twórcy

autor

Radliński J.

autor

Baran Z.

autor

Tomalak W.

• Zakład Fizjopatologii Układu Oddychania, Instytut Gruźlicy i Chorób Płuc, Oddział w Rabce-Zdroju, ul. Prof. Jana Rudnika 3b, 34-700 Rabka-Zdrój,

Bibliografia

• [1] Huikuri H. V.: Measurement of heart rate variability: a clinical tool or a research toy? J. Am. Coll. Cardiol. 1999;34;1878-1883.
• [2] Tulppo M., Huikuri H. V.: Origin and significance of heart rate variability. J. Am. Coll. Cardiol. 2004;43;2278-2280.
• [3] Heart Rate Variability: Standards of Measurement, Physiological Interpretation, and Clinical Use Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology Circulation 93, 1043-1065, 1996.
• [4] Goldberger A. L. Overture: Why is Physiologic Variability Important?, HRV 2006: Techniques, Applications and Future directions, Boston, April 2006.
• [5] Ott E.: Chaos w układach dynamicznych, WNT, Warszawa 1997.
• [6] Kantz H., Schreiber T.: Nonlinear time series analysis. Cambridge University Press, Cambridge 2000, 2003.
• [7] Pincus S. M.: Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA 88, 2297-2301, 1991.
• [8] Pincus S. M.: Approximate entropy (ApEn) as a complexity measure. Chaos 5, 110-117, 1995.
• [9] Richman J. S, Moorman J. R.: Physiological time-series analysis using approximate entropy and sample entropy Am J Physiol Heart Circ 278, H2039-H2049, 2000.
• [10] Costa M., Goldberger A. L., Peng C. K.: Multiscale entropy analysis of physiologic time series. Phys Rev Lett 89, 062102, 2002.
• [11] Costa M., Goldberger A. L., Peng C. K.: Multiscale entropy analysis of biologic signals. Phys Rev E 71, 021906, 2005.
• [12] Costa M., Healey J. A.: Multiscale Entropy Analysis of Complex Heart Rate Dynamics: Discrimination of Age and Heart Failure Effects. Computers in Cardiology, 30:705-708, 2003.
• [13] Viola A. U, Tobaldini E., Chellappa S. L., Casali K. R., Porta A., et al.: Short-Term Complexity of Cardiac Autonomic Control during Sleep: REM as a Potential Risk Factor for Cardiovascular System in Aging. PLoS ONE 6 (4): e19002. doi:10.1371/journal.pone.0019002, 2011.
• [14] Beckers F., Verheyden B., Aubert A. E.: Aging and nonlinear heart rate control in a healthy population. Am J Physiol Heart Circ Physiol 290:2560-2570, 2006.
• [15] Zamarrón C. et all.: Heart rate regularity analysis obtained from pulse oximetric recordings in the diagnosis of obstructive sleep apnea. Sleep Breath. 10(2):83-9, 2006.
• [16] Radliński J. et all.: Analiza wieloskalowa entropii rytmu pracy serca potencjalnym predyktorem ryzyka sercowo - naczyniowego, Materiały IX Sympozjum „Modelowanie i Pomiary w Medycynie”, Wydawnicto AGH, 83-88, 2009.
• [17] Norris P. R, Stein P. K, Morris J. A Jr: Reduced heart rate multiscale entropy predicts death in critical illness: a study of physiologic complexity in 285 trauma patients. J Crit Care 23(3), 399-405, 2008.
• [18] The International Classification of Sleep Disorders: Diagnostic & Codind Manual. 2 ed. Westchester: American Academy of Sleep Medicine, 2005.
• [19] Zieliński J., Pływaczewski R., Bednarek M.: Zaburzenia oddychania w czasie snu. Wydawnictwo Lekarskie PZWL, 1997, Wydanie II.
• [20] Goldberger A. L., Amaral L. A. N., Glass L., Hausdorff J. M., Ivanov P. Ch., Mark R. G., Mietus J. E., Moody G. B., Peng C. K., Stanley H. E. ; PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101 (23), e215-e220, [Circulation Electronic Pages; http://circ.ahajournals.org/ cgi/content/full/101/23/e215]; 2000.
• [21] http://www.physionet.org/physiotools/wag/ecgpuw-1.htm
• [22] Jan R., Blasi A., Garcia J., and Laguna P.: Evaluation of an automatic threshold based detector of waveform limits in Holter ECG with the QT database. Computers in Cardiology 24, 295-298, 1997.
• [23] Radliński J., Tomalak W., Baran Z. Heart rate analysis using multiscale entropy in OSA patients under CPAP treatment - pilot study. European Respiratory Journal, 38, suppl. 55: 393, 2011.