Determination of a new VLF band in HRV for ventricular tachyarrhythmia patients

Journal of Medical Systems

Volumn 34, Issue 2, 2010, Pages 155-160

  Bilgin, Suleyman ^a   Çolak, Omer H ^b   Polat, Ovunc ^c   Koklukaya, Etem ^d  

^a MEHMET AKIF ERSOY UNIVERSITY   (Turkey)

^b AKDENIZ UNIVERSITY   (Turkey)

^c SULEYMAN DEMIREL UNIVERSITY   (Turkey)

^d SAKARYA UNIVERSITY   (Turkey)

Author keywords

Heart rate variability; MLPNN; Ventricular tachyarrhythmia; Very low frequency band; Wavelet packet

Indexed keywords

ACCURACY; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; DATA BASE; ENERGY YIELD; FREQUENCY ANALYSIS; HEART RATE VARIABILITY; HEART VENTRICLE TACHYCARDIA; MATHEMATICAL MODEL; MULITLAYER PERCEPTRON NEURAL NETWORK; SIGNAL DETECTION; SIMULATION; SPECTRAL SENSITIVITY; VERY LOW FREQUENCY BAND;

FOURIER ANALYSIS; HEART RATE; HUMANS; NEURAL NETWORKS (COMPUTER); RADIO WAVES; TACHYCARDIA, VENTRICULAR;

EID: 77950458327     PISSN: 01485598     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10916-008-9227-8     Document Type: Article

References (11)

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. 93:1043-1065, 1996.
2. Acharya, U. R., Joseph, K. P., Kannathal, N., Lim, C. M., and Suri, J. S., Heart rate variability: a review. Med. Bio. Eng. Comput. 44:1031-1051, 2006. (Pubitemid 47090818)
3. Chen, S. W., A wavelet based HRV analysis for the study of nonsustained ventricular Tachycardia, IEEE transactions on biomedical engineering, Vol:49, No:7, 2002.
4. Malarvili, M. B., Mesbah, M., Boashash, B., Time-frequency analysis of heart rate variability for Neonatal Seizure detection. EURASIP Journal on Advances in Signal Processing, Vol:2007, Article ID: 50396, pp. 10., 2007. doi:10.1155/2007/50396. (Pubitemid 46609379)
5. Shafqat, K., Pal, S. K., Kumari, S., and Kyriacou, P. A., Changes in heart rate variability in patients under local Anesthesia. Proceedings of the 29th Annual International Conference of IEEE EMBSLyon: France, 2007.
6. Hossen, A., Ghunaimi, B. A., and Hassan, M. O., Subband decomposition soft-decision Algorithm for heart rate variability analysis in patients with obstructive sleep Apnea and normal controls. Signal Processing. 85:95-106, 2005.
7. Morris, F., Edhouse, J., Brady, W. J., Camm, J., ABC OF Clinical Electrocardiography, BMJ Books, First published in 2003, by BMJ Books, BMA House, Tavistock Square, London WC1H 9JR. http://www.physionet.org/physiobank/ database/mvtdb/
8. Bilgin, S., Colak, O. H., Koklukaya, E., and Ari, N., Efficient solution for frequency band decomposition problem using wavelet packet for HRV. Digital Signal Processing. 18:892-1299, 2008. doi:10.1016/j.dsp.2008.04.007.
9. Addison, P. S., The Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and Finance, IOP Publishing Ltd, 2002.
10. Park, J.-W., Harley, R. G., and Venayagamoorthy, G. K., Comparison of MLP and RBF neural networks using deviation signals for on-line identification of a synchronous generator. Power Engineering Society Winter Meeting. 1:274-279, 2002.
11. Lera, G., and Pinzolas, M., A quasi-local Levenberg-Marquardt algorithm for neural network training neural networks proceedings. IEEE World Congress on Computational Intelligence, The 1998 IEEE International Joint Conference. 3:2242-2246, 1998.