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Neural Computing and Applications

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01.11.2012 | Original Article

Adaptive neuro-fuzzy estimation of autonomic nervous system parameters effect on heart rate variability

verfasst von: D. Petković, Ž. Ćojbašić

Erschienen in: Neural Computing and Applications | Ausgabe 8/2012

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Abstract

Heart rate signal can be used as certain indicator of heart disease. Spectral analysis of heart rate variability (HRV) signal makes it possible to partly separate the low-frequency (LF) sympathetic component, from the high-frequency (HF) vagal component of autonomic cardiac control. Here, we used two important features to characterize the nonlinear fluctuations in the heart variability signal (HRV): cardiac vagal index (CVI) and cardiac sympathetic index (CSI) which indicates vagal and sympathetic function separately. This article presents a methodology for analyzing the influence of CVI and CSI on heart rate variability spectral patterns—low-frequency (LF) and high-frequency (HF) spectral bands and LF/HF ratio. An adaptive neuro-fuzzy network is used to approximate correlation between these two features and spectral patterns. This system is capable to find any change in ratio of features and spectral patterns of heart rate variability signal (HRV) and thus indicates state of both parasympathetic and sympathetic functions in newly diagnosed patients with heart diseases.

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Zhong Y, Wang H, Ju KH, Jan KM, Chon KH (2004) Nonlinear analysis of the separate contributions of autonomic nervous systems to heart rate variability using principal dynamic modes. IEEE Trans Biomed Eng 51:255–262CrossRef

Pan J, Tompkins JW (1985) A real-time QRS detection algorithm. IEEE Trans Biomed Eng 32:230–255CrossRef

Malik M (1996) Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Eur Heart J 17:354–381CrossRef

Berntson GG, Bigger JT et al (1997) Heart rate variability: origins, methods, and interpretative caveats. Psychophysiology 34:623–648CrossRef

Pincus MS (1991) Approximate entropy as a measure of system complexity. Proc Nat Acad Sci 88:2297–2301MathSciNetCrossRefMATH

Xinbao N, Chunhua B, Jun W, Ying C (2006) Research progress in nonlinear analysis of heart electric activities. Chin Sci Bull 51:385–393CrossRef

Chunhua B, Xinbao N (2004) Nonlinearity degree of short-term heart rate variability signal. Chin Sci Bull 49:530–534

Mourot L, Bouhaddi M, Perrey S et al (2004) Quantitative Poincare plot analysis of heart rate variability: effect of endurance training. Eur J Appl Physiol 91:79–87CrossRef

Lerma C, Infante I, Groves HP, Jose MV (2003) Poincare plot indexes of heart rate variability capture dynamic adaptations after haemodialysis in chronic renal failure patients. Clin Physiol Func Im 23:72–80CrossRef

10.

Addio GD, Pinna GD, Rovere MTL, Maestri R et al (2001) Prognostic value of Poincare plot indexes in chronic heart failure patients. Comput Cardiol 28:57–60

11.

Chiu HW, Wang TH, Huang LC et al (2003) The influence of mean heart rate on measures of heart rate variability as markers of autonomic function: a model study. Med Eng Phys 25:475–481CrossRef

12.

Basano L, Canepa F, Ottonello P (1998) Real-time spectral analysis of HRV signals: an interactive and user-friendly PC system. Comput Methods Programs Biomed 55:69–76CrossRef

13.

Wu GQ, Arzeno MN, Shen LL et al (2009) Chaotic signatures of heart rate variability and its power spectrum in health, aging and heart failure. PLoS One Public Library Sci 4:2

14.

Busek P, Vankova J, Opavsky J et al (2005) Spectral analysis of heart rate variability in sleep. Physiol Res 54:369–376

15.

Groome JL, Mooney MD, Bentz SL, Singh PK (1994) Spectral analysis of heart rate variability during quiet slep in normal human fetuses between 36 and 40 weeks of gestation. Early Hum Dev 38:1–10CrossRef

16.

Colak HO (2009) Preprocessing effects in time-frequency distributions and spectral analysis of heart rate variability. Dig Sig Proc 19:731–739CrossRef

17.

Khandoker AH, Jelinek HF, Moritani T, Palaniswami M (2010) Association of cardiac autonomic neuropathy with alteration of sympatho-vagal balance through heart rate variability analysis. Med Eng Phys 32:161–167CrossRef

18.

Toichi M, Sugiura T, Murai T, Sengoku A (1997) A new method of assessing cardiac autonomic function and its comparision with spectral analysis and coefficient of variation of R-R interval. J Aut Nerv Syst 62:79–84CrossRef

19.

Lin CW, Wang JS, Chung PC (2010) Mining physiological conditions from heart rate variability analysis. IEEE Comput Intell Mag 5:50–58

20.

Apfelbaum JD, Caravati EM, Kerns WP, Bossart PJ, Larsen G (1995) Cardiovascular effects of carbamazepine toxicity. Ann Emerg Med 25:631–635CrossRef

21.

Meneses ASJ, Moreira HG, Daher MT (2004) Analysis of heart rate variability in hypertensive patients before and after treatment with angiotensin II-converting enzyme inhibitors. Arquivos Brasileros de Cardiologia 83:169–172

22.

Lado MJ, Vila XA, Rodrigez LL, Mendez AJ, Olivieri DN, Felix P (2009) Detecting sleep apnea by heart rate variability analysis: assessing the validity of databases and algorithms. J Med Syst 33:1–9

23.

Persson H, Ericson M, Tomson T (2007) Heart rate variability in patients with untreated epilepsy. Seizure 16:504–508CrossRef

24.

Hallioglu O, Ocuyaz C, Mert E, Makharoblidze K (2008) Effects of antiepileptic drug therapy on heart rate variability in children with epilepsy. Epilepsy Res 79:49–54CrossRef

25.

Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685CrossRef

26.

Yardimci A (2009) Soft computing in medicine. Appl Soft Comput 9:1029–1043CrossRef

27.

Ubeyli ED (2009) Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents. Comput Method Prog Biomed 93:313–321CrossRef

28.

Nazmy TM, El-Messiry H, Al-Bokhity B (2009) Adaptive neuro-fuzzy inference system for classification of ECG signals. J Theor Appl Inform Tech 12:71–76

29.

Rajendra UA, Subbanna PB, Iyengar SS, Rao A, Dua S (2003) Classification of heart rate data using artificial neural network and fuzzy equivalence relation. Pattern Recogn 36:61–68CrossRefMATH

30.

Ubeyli ED (2010) Recurrent neural networks employing Lyapunov exponents for analysis of ECG signals. Exp Syst Appl 37:1192–1199CrossRef

31.

Maglaveres N, Stamkopolulos T, Diamantaras K, Pappas C, Strintzis M (1998) ECG pattern recognition and classification using non-linear transformations and neural networks: a review. Int J Med Inform 52:191–208CrossRef

32.

Ozbay Y, Tezel G (2009) A new method for classification of ECG arrhythmias using neural network with adaptive activation function. Digi Sig Proc 20:1040–1049

33.

Hosseini HG, Luo D, Reynolds KJ (2006) The comparision of different feed forward neural network architectures for ECG signal diagnosis. Med Eng Phys 28:372–378CrossRef

34.

Ceylan R, Ozbay Y, Karlik B (2009) A novel approach for classification of ECG arrhythmias: type-2 fuzzy clustering neural network. Exp Syst Appl 36:6721–6726CrossRef

35.

Osowski S, Markiewicz T, Hoai LT (2009) Recognition and classification system of arrhythmia using ensemble of neural networks. Measurement 41:610–617CrossRef

36.

Anuradha MB, Reddy VCV (2009) Cardiac arrhythmia classification using fuzzy classifiers. J Theor Appl Inform Tech 4:353–359

37.

Mohammadzadeh-Asl B, Setarehdan SK (2006) Neural network based arrhythmia classification using heart rate variability signal. In: 14th European signal processing conference

38.

Patil SB, Kumaraswamy YS (2009) Intelligent and effective heart attack prediction system using data mining and artificial neural network. Eur J Sci Res 31:642–656

39.

Annuradha B, Reddy VCV (2008) ANN for classification of cardiac arrhythmias. ARPN J Engin Appl Sci 3:1–6

Titel: Adaptive neuro-fuzzy estimation of autonomic nervous system parameters effect on heart rate variability
verfasst von: D. Petković
Ž. Ćojbašić
Publikationsdatum: 01.11.2012
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 8/2012
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-011-0629-z

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