• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.66 no.3
• /
• pp.581-586
• /
• 2017

The well-known Integral Pulse Frequency Modulation (IPFM) cardiac oscillator has been used to generate the heart beat fluctuations as a representation of the modulatory autonomic nervous activity in terms of sympathetic and parasympathetic state. The IPFM model produces heartbeats by integrating the modulated sinusoid signals and applying the threshold of unity or chaotic threshold levels. This study aims at evaluating the performance of IPFM model by analyzing the influence of the threshold level with comparatively applying preset threshold of unity and Logistic-map and Henon-map chaotic-threshold. Based on our simulated results with interpreting the spectral features of Heart Rate Variability (HRV), we can conclude that the IPFM model with preset threshold level of unity can generate the optimal heartbeat variations int the sense of clinically valid heartbeats.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.64 no.5
• /
• pp.799-804
• /
• 2015

This study aims at the new analysis of heart beat fluctuations by applying physiological Heart Rate Variability Model with representing the cardiac control system in sympathetic and parasympathetic-coupling oscillator constants, C_s and C_p. To find the effects of coupling constants on the beat-to-beat fluctuations, Integral Pulse Frequency Modulation (IPFM) model is adopted to generate the time series data of ECG R-peaks and represent them by poincaré scattergram plot in the time domain and HRV in the frequency domain, respectively. The actual poincaré plots and HRV spectrum are also analyzed by acquiring the experimental data from the subjects exposed to the emotional-stress invoking environment and the function of the coupling constants are verified in terms of antagonism in sympathetic and parasympathetic activity.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.65 no.3
• /
• pp.493-498
• /
• 2016

Heart Rate Variability (HRV) represents beat-to-beat fluctuations of R-R intervals in Electrocardiogram (ECG). On of the clinical applications of HRV is to assess the mental-stress state by evaluating its power spectral density distribution. This study aims at finding new discriminative role of the coupled-oscillating coupling constants, Cs and Cp in the Integral Pulse Frequency Modulation (IPFM) model. Based on comparing with power spectral density of HRV in terms of the relative ratio of the low and high-frequency power component, we can conclude the fact that the coupling parameters Cs and Cp can replace the role of HRV power spectrum interpretation for judging the mental-stress state.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.543-543
• /
• 2000

A new method for HRV(heart rate variability) detection from the R-wave signal, based on the IPFM(integral pulse frequency modulation) model and its similarity to pulse position modulation, is presented. The proposed method exert lowpass filtering with a Kaiser window. In this paper, The proposed method presents a powerful, but simple, tool for investigation of HRV. It also guarantees real-time behavior. simplicity in design, and phase linearity. Even without the basic assumption of IPFM model. the new algorithm can still be used on-line and with higher performance. It is thoroughly proved that lowpass filtering is an ideal method for PSD(Power Spectrum Density) analysis of HRV.

• Proceedings of the KIEE Conference
• /
• 2000.07d
• /
• pp.3233-3235
• /
• 2000

A new method for HRV(heart rate variability} detection from the R-wave signal, based on the IPFM (integral pulse frequency modulation) model and its similarity to pulse position modulation, is presented. The proposed method exert lowpass filtering with a Kaiser window. In this paper, The proposed method presents a powerful, but simple, tool for investigation of HRV. It also guarantees real-time behavior, simplicity in design, and phase linearity. Even without the basic assumption of IPFM model, the new algorithm can still be used on-line and with higher performance. It is thoroughly proved that lowpass filtering is an ideal method for PSD (Power Spectrum Density) analysis of HRV.

• Proceedings of the KOSOMBE Conference
• /
• v.1997 no.05
• /
• pp.275-278
• /
• 1997

Standard methods estimating the power spectral density(PSD) from an irregularly sampled cardiac event series require deriving a new evenly-spaced signal applicable to those methods. To avoid that requirement, in this study, the power spectrum of heart rate variability was estimated by Lomb-Scargle's algorithm, which is a means of obtaining PSD estimates directly from irregularly sampled timeseries observed in astronomy. To assess the performance of Lomb-Scargle algorithm in the power spectral analysis of heart rate variability, it was applied to various cardiac event series derived through integral pulse frequency modulation model(IPFM) simulation and from real ECG signals, and the resultant power spectra was compared with those obtained by a conventional method based on the FFT. In result, it is concluded that Lomb-Scargle's periodogram is very effective in the power spectral analysis of heart rate variability, especially in the presence of arrhythmia and/or dropouts of cardiac events.