New Modeling for Generation of Normal and Abnormal Heart Rate Variability Signals


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.

Share and Cite:

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.

Conflicts of Interest

The authors declare no conflicts of interest.


[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.
[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.
[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.
[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.
[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.
[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.
[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.

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.