HRV and BPV neural network model with wavelet based algorithm calibration

Measurement: Journal of the International Measurement Confederation

Volumn 42, Issue 6, 2009, Pages 805-814

  Postolache, G ^a   Silva Carvalho, L ^c   Postolache, O ^b   Girao P ^b   Rocha, I ^c  

^a Universidade Atlantica   (Portugal)

^b INSTITUTO SUPERIOR TÉCNICO   (Portugal)

^c Faculdade de Medicina   (Portugal)

Author keywords

Autonomic nervous system; Neural network model; Wavelet transform

Indexed keywords

ARTIFICIAL NEURAL NETWORKS; AUTONOMIC FUNCTIONS; AUTONOMIC NERVOUS SYSTEM; CARDIOVASCULAR FUNCTIONS; DAUBECHIES; DISCRETE-WAVELET-TRANSFORM; EVALUATION TOOLS; FAST ASSESSMENTS; HEART RATES; HIGH FREQUENCY COMPONENTS; LOW FREQUENCIES; MULTI-LAYER PERCEPTRON NEURAL NETWORKS; NEURAL NETWORK MODEL; NEURAL STRUCTURES; PRESSURE POWER SPECTRUMS; PROCESSING TECHNIQUES; RADIAL BASIS FUNCTIONS; RESPIRATORY ACTIVITIES; WAVELET BASED ALGORITHMS;

BLOOD; BLOOD PRESSURE; DISCRETE WAVELET TRANSFORMS; POWER SPECTRUM; RADIAL BASIS FUNCTION NETWORKS;

MULTILAYER NEURAL NETWORKS;

EID: 67349186189     PISSN: 02632241     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.measurement.2009.01.003     Document Type: Article

References (23)

1. 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. Circulation, vol. 93, 1996, pp. 1043-1065.
2. Malik M., and Camm A.J. Heart Rate Variability (1995), Futura Publishing Company, Inc., Armonk, NY
3. Cerutti S., Bianchi A.M., and Mainardi L.T. Advanced spectral methods for detecting dynamic behaviour. Autonomic Neuroscience: Basic and Clinical 90 (2001) 41-46
4. Parati G., Frattola A., Di Rienzo M., Castiglioni P., and Mancia G. Broadband spectral analysis of blood pressure and heart rate variability in very elderly subjects. Hypertension 30 (1997) 803-808
5. Parati G., Saul J.P., Di Rienzo M., and Mancia G. Spectral analysis of blood pressure and heart rate variability in evaluating cardiovascular regulation. Hypertension 25 (1995) 1276-1286
6. Pagani M., Lombardi F., Guzzetti S., Rimoldi O., Furlan R., Pizzinelli P., Sandrone G., Malfatto G., DellÓrto S., Piccaluga E., Turiel M., Baselli G., Cerutti S., and Malliani A. Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympatho-vagal interaction in man and conscious dog. Circulation Research 59 (1986) 178-193
7. Ducla-Soares J.L., Santos-Bento M., Laranjo S., Andrade A., Ducla-Soares E., Boto J.P., Silva-Carvalho L., and Rocha I. Wavelet analysis of autonomic outflow of normal subjects on head-up tilt, cold pressor test, Valsava manoeuvre and deep breathing. Experimental Physiology 92 4 (2008) 677-686
8. Accurso V., Shamsuzzaman A.S.M., and Somers V.K. Rhythms, rhymes, and reasons-spectral oscillations in neural cardiovascular control. Autonomic Neuroscience: Basic and Clinical 90 (2001) 41-46
9. Ho K.K.L., Moody G.B., Peng C.K., Mietus J.E., Larso M.G., Levy D., and Goldberg A.L. Predicting survival in heart failure case and control subjects by use of fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics. Circulation 96 (1997) 842-848
10. Postolache G., Rocha I., Silva-Carvalho L., Postolache O., and Girão P. A wavelet-based approach for monitoring baroreceptors function test in rats. Proceedings of the IEEE-IMTC'2004 (2004) 844-850
11. Tan B. Wavelet transform analysis of heart rate variability to assess the autonomic changes associated with spontaneous coronary spasm of variant angina. Journal of Electrocardiology 36 2 (2003) 117-124
12. Burri H., Chevalier P., Arzi M., Rubel P., Kirkorian G., and Touboul P. Wavelet transform for analysis of heart rate variability preceding ventricular arrhythmias in patients with ischemic heart disease. International Journal of Cardiology 109 1 (2006) 101-107
13. Postolache G., Rocha I., Silva Carvalho L., Postolache O., and Girão P. A practical approach of wavelets analysis to follow transitory modulation of the cardiac autonomic system after ethanol administration. Proceedings of the IEEE IMTC'2003 1 (2003) 218-222
14. In: Lisboa P.J.G., Ifeachor E.C., and Szczepaniak P.S. (Eds). Artificial Neural Networks in Biomedicine (2000), Springer-Verlag, London
15. D. Übeyli, Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents, Computer Methods and Programs in Biomedicine, Elsevier. Available from , in press.
16. Übeyli D. Analysis of EEG signals by implementing eigenvector methods/recurrent neural network. Digital Signal Processing 19 1 (2009) 134-143
17. Norgaard M., Ravn O., Poulsen N.K., and Hansen L.K. Neural Networks for Modeling and Control of Dynamic Systems: A Practitioner's Handbook (2003), Springer
18. Jiang W., and Kong S. G, Block-Based Neural Networks for Personalized ECG Signal Classification. IEEE Transactions on Neural Networks 18 6 (2007) 1750-1761
19. Haykin S. Neural Networks. A Comprehensive Foundation (1999), Prentice Hall
20. Chen S., and Billings S.A. Neural networks for non-linear dynamic system modelling and identification. Proceedings of the International Journal of Control 56 4 (1992) 319-346
21. Postolache O., Girão P., and Pereira M. Neural Network in automatic measurement system: state of art and new research trends. Proceedings of the IEEE - IJCNN 3 (2001) 2310-2315
22. Gao X.Z. A/D converter resolution enhancement using neural networks. Proceedings of the IEEE IMTC (1997) 1112-1117
23. Bors A.G., and Pitas I. In: Howlett R.J., and Jain L.C. (Eds). Robust RBF Networks in Radial Basis Function Neural Networks: Design and Applications (2001), Physica-Verlag, Heidelberg 125-153