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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=52831#.VKn-g8nQrzE
Author(s)
This research is performed based on the modeling of
biological signals. We can produce Heart Rate (HR) and Heart Rate
Variability (HRV) signals synthetically using the mathematical
relationships which are used as input for the Integral Pulse Frequency
Modulation (IPFM) model. Previous researches were proposed same methods
such as one model of ECG signal synthetically based on RBF neural
network, a model based on IPFM with random threshold, method was based
on the estimation of produced signals which are dependent on autonomic
nervous system using IPFM model with fixed threshold, a new method based
on the theory of vector space that based on time-varying uses of IPMF
model (TVTIPMF) and special functions, and two different methods for
producing HRV signals with controlled characteristics and structure of
time-frequency (TF) for using non-stationary HRV analysis. In this
paper, several chaotic maps such as Logistic Map, Henon Map, Lorenz and
Tent Map have been used. Also, effects of sympathetic and
parasympathetic nervous system and an internal input to the SA node and
their effects in HRV signals were evaluated. In the proposed method,
output amount of integrator in IPFM model was compared with chaotic
threshold level. Then, final output of IPFM model was characterized as
the HR and HRV signal. So, from HR and HRV signals obtaining from this
model, linear features such as Mean, Median, Variance, Standard
Deviation, Maximum Range, Minimum Range, Mode, Amplitude Range and
frequency spectrum, and non-linear features such as Lyapunov Exponent,
Shanon Entropy, log Entropy, Threshold Entropy, sure Entropy and mode
Entropy were extracted from artificial HRV and compared them with
characteristics as extracted from natural HRV signal. Also, in this
paper two patients that called high sympathetic Balance and
Cardiovascular Autonomy Neuropathy (CAN) which is detected and evaluated
by HRV signals were simulated. These signals by changing the values of
the some coefficients of the normal simulated signal and with extracted
frequency feature from these signals were simulated. For final
generation of these abnormal signals, frequency features such as energy
of low frequency band (EL), energy of high frequency band (HL), ratio of
energy in low frequency band to the energy in high frequency band
(EL/EH), ratio of energy in low frequency band to the energy in all
frequency band (EL/ET) and ratio of energy in high frequency band to the
energy in all frequency band (EH/ET) from abnormal signals were
extracted and compared with these extracted values from normal signals.
The results were closely correlated with the real data which confirm the
effectiveness of the proposed model. Various signals derived from the
output of this model can be used for final analysis of the HRV signals,
such as arrhythmia detection and classification of ECG and HRV signals.
One of the applications of the proposed model is the easy evaluation of
diagnostic ECG signal processing devices. Such a model can also be used
in signal compression and telemedicine application.
KEYWORDS
Cite this paper
Safdarian, N. (2014) New Modeling for Generation of Normal and Abnormal Heart Rate Variability Signals. Journal of Biomedical Science and Engineering, 7, 1122-1143. doi: 10.4236/jbise.2014.714110.
| [1] | Dabanloo, N.J., McLernon, D.C., Ayatollahi, A. and Majd, V.J. (2004) A Nonlinear Signal Processing Approach to Model Heart Rate Variability. Signal Processing and Information Technology, Proceedings of the Fourth IEEE International Symposium, 64-67. |
| [2] |
Jafarnia-Dabanloo, N., McLernon,
D.C., Zhang, H., Ayatollahi, A. and Johari-Majd, V. (2007) A Modified
Zeeman Model for Producing HRV Signals and Its Application to ECG Signal
Generation. Journal of Theoretical Biology, 244, 180-189. http://www.sciencedirect.com/science/article/pii/S0022519306003481 http://dx.doi.org/10.1016/j.jtbi.2006.08.005 |
| [3] |
Attarodi, G., Dabanloo, N.J.,
Abbasvandi, Z. and Hemmati, N. (2013) A New IPFM Based Model for
Artifical Generation of HRV with Random Input. IJCSI International
Journal of Computer Science Issues, 10, 1-5. http://ijcsi.org/papers/IJCSI-10-2-2-1-5.pdf |
| [4] |
McSharry, P.E., Clifford, G.D.,
Tarassenko, L. and Smith, L.A. (2003) A Dynamical Model for Generating
Synthetic Electrocardiogram Signals. IEEE Transactions on Biomedical
Engineering, 50, 289-294. http://dx.doi.org/10.1109/TBME.2003.808805 |
| [5] | Bailon, R., et al. (2011) The Integral Pulse Frequency Modulation Model with Time-Varying Threshold: Application to Heart Rate Variability Analysis during Exercise Stress Testing. Biomedical Engineering Transactions, 58, 642-652. |
| [6] | Seydnejad, S.R., et al. (2001) Time-Varying Threshold Integral Pulse Frequency Modulation. Biomedical Engineering Transactions, 48, 949-962. |
| [7] |
Orini, M., Bailón, R., Mainardi,
L. and Laguna, P. (2012) Synthesis of HRV Signals Characterized by
Predetermined Time-Frequency Structure by Means of Time-Varying ARMA
Models. Biomedical Signal Processing and Control, 7, 141-150. http://www.sciencedirect.com/science/article/pii/S1746809411000437 http://dx.doi.org/10.1016/j.bspc.2011.05.003 |
| [8] |
Almasi, A., Shamsollahi, M.-B.
and Senhadji, L. (2011) A Dynamical Model for Generating Synthetic
Phonocardiogram Signals. Engineering in Medicine and Biology Society,
EMBC, 2011 Annual International Conference of the IEEE, 5686-5689. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3390312/ |
| [9] | Martín-Martínez, D., Casaseca-de-la-Higuera, P., Martín-Fernández, M. and Alberola-López, C. (2013) Stochastic Mo- deling of the PPG Signal: A Synthesis-by-Analysis Approach with Applications. IEEE Transactions on Biomedical Engineering, 60, 2432-2441 |
| [10] |
McLernon, D.C., Dabanloo, N.J.,
Ayatollahi, A., Majd, V.J. and Zhang, H. (2004) A New Nonlinear Model
for Generating RR Tachograms. Computers in Cardiology, 31, 481-484. http://www.cinc.org/archives/2004/pdf/481.pdf |
| [11] | Ayatollahi, A., Dabanloo, N.J. and McLernon, D.C. (2005) A Comprehensive Model for Generating ECG Signals Using the IPFM Model. Proceedings 13th Iranian Conference on Electrical Engineering (ICEE), Zanjan University, Iran. eww150105lx |
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