PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

A new baroreflex sensitivity index based on improved Hilbert–Huang transform for assessment of baroreflex in supine and standing postures

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The aim of this study is to propose a new baroreflex sensitivity (BRS) index using improved Hilbert–Huang transform (HHT) using weighted coherence (CW) criterion and apply it to assess baroreflex in supine and standing postures. Improved HHT is obtained by addressing the mode mixing and end effect problems associated with empirical mode decomposition which is a required step in the computation of HHT and thus mitigating the unwanted low frequency component from the power spectrum. This study was first performed on synthetic signals generated using integral pulse frequency model and further extended to real RR interval and systolic blood pressure records of 50 healthy subjects, 20 post acute myocardial infarction patients undergoing postural stress from supine to standing position. Evaluation is also performed on standard EuroBaVar database, comprising of 21 subjects, under supine and standing positions. The results are (i) enhanced values of supine-to-standing low frequency BRS index (α-LF) equal to 1.78 and high frequency BRS index (α-HF) equal to 2.48 are obtained using improved HHT compared to standard HHT (α-LF = 1.54, α-HF = 2.36) and traditional power spectral density (α-LF = 1.55, α-HF = 2.34) for healthy subjects, (ii) there is an increased rate of change of LF/HF power ratios from supine to standing positions, and (iii) number of BRS responses obtained using CW criterion are greater than those obtained by using mean coherence criterion. In conclusion, the new BRS index takes into consid-eration the non-linear nature of interactions between heart rate variability and systolic blood pressure variability.
Twórcy
autor
  • Department of Electronics and Communication Engineering, Dr. B. R. Ambedkar National Institute of Technology, PB, India; School of Electronics and Electrical Engineering, Lovely Professional University, PB, India
autor
  • Department of Electronics and Communication Engineering, Dr. B. R. Ambedkar National Institute of Technology, PB, India
autor
  • Department of Instrumentation and Control, Dr. B. R. Ambedkar National Institute of Technology, PB, India
Bibliografia
  • [1] Westerhof BE, Gisolf J, Stok WJ, Wesseling KH, Karemaker JM. Time-domain cross-correlation baroreflex sensitivity: performance on the EUROBAVAR data set. J Hypertens 2004;22:1371–80.
  • [2] Lefrandt JD, Hoogenberg K, Van Roon AM, Dullaart RP, Gans RO, Smit AJ. Baroreflex sensitivity is depressed in microalbuminuric Type I diabetic patients at rest and during sympathetic manoeuvres. J Diabetol 1999;42:1345–9.
  • [3] La Rovere MT, Bigger Jr JT, Marcus FI, Mortara A, Schwartz PJ. Baroreflex sensitivity and heart-rate variability in prediction of total cardiac mortality after myocardial infarction. Lancet 1998;351:478–84.
  • [4] Ewing DJ, Martyn CN, Young RJ, Clarke BF. The value of cardiovascular autonomic function tests: 10 years experience in diabetes. J Diabetes Care 1985;8:491–8.
  • [5] Spallone V, Menzinger G. Diagnosis of cardiovascular autonomic neuropathy in diabetes. J Diabetes 1997;46:S67–76.
  • [6] 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. Eur Heart J 1996;17:354–438.
  • [7] Laude D, Luc Elghozi J, Girard A, Bellard E, Bouhaddi M, Castiglioni P, et al. Comparison of various techniques used to estimate spontaneous baroreflex sensitivity (the EuroBaVar study). Am J Physiol Regul Integr Comp Physiol 2004;286:R226–31.
  • [8] Laguna P, Moody GB, Mark RG. Power spectral density of unevenly sampled data by least square analysis performance and application to heart rate signals. IEEE Trans Biomed Eng 1998;45(June (6)):698–715.
  • [9] Malik M. Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circ Eur Soc Cardiol North Am Soc Pacing Electrophysiol 1996;93:1043–65.
  • [10] Echeverria JC, Crowe JA. Application of empirical mode decomposition to heart rate variability analysis. Med Biol Eng Comput 2001;39:471–9.
  • [11] Balocchi R, Menicucci D. Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition. Chaos Solitons Fractals 2004;20:171–7.
  • [12] Zhong Y, Wang H, Hwan Ju K, Jan K-M, Chon KH. Nonlinear analysis of the separate contributions of autonomic nervous systems to heart rate variability using principal dynamic modes. IEEE Trans Biomed Eng 2004;51(2):255–62. Feb.
  • [13] Hawkins WG. Fourier transform resampling: theory and application. IEEE Trans Nucl Sci 1997;44(August (4)):1543–51.
  • [14] Mateo J, Laguna P. New heart rate variability time-domain signal construction from the bead occurrence time and the IPFM model. Comput Cardiol 1996;185–8. http://dx.doi.org/10.1109/CIC.1996.542504.
  • [15] Mateo J, Laguna P. Improved heart rate variability signal analysis from the beat occurrence times according to the IPFM model. IEEE Trans Biomed Eng 2000;47(August (8)): 985–96.
  • [16] Yang C, Kuo T. Assessment of cardiac sympathetic regulation by respiratory-related arterial pressure variability in the rat. J Physiol 1999;515:887–96.
  • [17] Bianchi AM, Mainardi LT, Merloni C, Chierchia S, Cerutti S. Continuous monitoring of the sympatho-vagal balance through spectral analysis. IEEE Eng Med Biol Mag 1997;16 (September/October (5)):64–73.
  • [18] Orini M, Laguna P, Mainardi LT, Bailon R. Assessment of the dynamic interactions between heart rate and arterial pressure by the cross time–frequency analysis. Physiol Meas 2012;33:315–31.
  • [19] Clayton RH, Bowman AJ, Ford GA, Murray A. Measurement of baroreflex gain from heart rate and blood pressure spectra: a comparison of spectral estimation techniques. Physiol Meas 1995;16:131.
  • [20] Li H, Kwong S, Yang L, Huang D, Xiao D. Hilbert–Huang transform for analysis of heart rate variability in cardiac health. IEEE/ACM Trans Comput Biol Bioinf 2011;8:1557–67.
  • [21] Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond Ser A 1998;454:903–95.
  • [22] Neto ES, Custaud MA, Cejka JC, Abry P, Frutoso J, Gharib D, et al. Assessment of cardiovascular autonomic control by the empirical mode decomposition. Methods Inf Med 2004;43(1):60–5.
  • [23] De Souza Neto EP, Abry P, Loiseau P, Cejka JC, Custaud MA, Frutoso J, et al. Empirical mode decomposition to assess cardiovascular autonomic control in rats. Fundam Clin Pharmacol 2007;21(5):481–96.
  • [24] Ortiz MR, Bojorges ER. Analysis of high frequency fetal heart rate variability using empirical mode decomposition. Comput Cardiol 2005;32:675–8. Sept.
  • [25] Shafqat K, Pal SK, Kumari S, Kyriacou PA, et al. Empirical mode decomposition (EMD) analysis of HRV data from locally anesthetized patients. Proc. IEEE Ann. Int'l Conf. Eng. in Medicine and Biology Soc.. 2009. pp. 2244–7.
  • [26] Abdulhay E, Guméry PY, Fontecave J, Baconnier P, et al. Cardiogenic oscillations extraction in inductive plethysmography: ensemble empirical mode decomposition. Proc. 31st IEEE Ann. Int'l Conf. Eng. in Medicine and Biology Soc.. 2009. pp. 2240–3.
  • [27] Yeh JR, Fan SZ, Shieh JS. Human heart beat analysis using a modified algorithm of detrended fluctuation analysis based on empirical mode decomposition. Med Eng Phys 2009;31 (1):92–100.
  • [28] Hadjileontiadis LJ. A novel technique for denoising explosive lung sounds empirical mode decomposition and fractal dimension filter. IEEE Eng Med Biol Mag 2007;26 (January/February (1)):30–9.
  • [29] Zhang YF, Gao YL, Wang L, Chen JH, Shi XL. The removal of wall components in Doppler ultrasound signals by using the empirical mode decomposition algorithm. IEEE Trans Biomed Eng 2007;54(September (9)):1631–42.
  • [30] Singh D, Vinod K, Saxena SC, Deepak KK. Effects of RR segment duration on HRV spectrum estimation. Physiol Meas 2004;25(June (3)):721–35.
  • [31] Kumar V, Barua A, Sattaraju I, Mallavarapu N. Weighted coherence: a more effective measure than average coherence. Cardiovasc Eng Int J 2003;3(December (4)).
  • [32] Qi K, He Z, Zi Y. Cosine window-based boundary processing method for EMD and its application in rubbing fault diagnosis. Mech Syst Signal Process 2007;21(7):2750–60.
  • [33] Pal S, Mitra M. Empirical mode decomposition based ECG enhancement and QRS detection. Comput Biol Med 2012;42 (1):83–92.
  • [34] Singh D, Saini BS, Kumar V. Heart rate variability – a bibliographical survey. IETE J Res 2008;54(3):209.
  • [35] Singh A, Saini BS, Singh D. Multiscale joint symbolic transfer entropy for quantification of causal interactions between heart rate and blood pressure variability under postural stress. Fluct Noise Lett 2015;14(3):1550031-1– 1550031-15.
  • [36] Singh A, Saini BS, Singh D. An alternative approach to approximate entropy threshold value (r) selection: application to heart rate variability and systolic blood pressure variability under postural challenge. Med Biol Eng Comput 2015. http://dx.doi.org/10.1007/s11517-015-1362-z.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-30542278-b611-48a6-b53c-9f3d1c07a53c
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.