Changes in volume and internal medium accompany vital activity of organs and tissues of the human body. Electrical activity is also modified as a result of the vital activity. Modification of blood content is one of the main factors affecting biological impedance. Dynamic changes in impedance are associated with the volume of blood. Changes in the pulsation have little effect on the bio- logical impedance level. It was demonstrated in direct experiments that such changes were 0.05% of the com- plete impedance of the biological object (BO). The limit- ing values of the range of blood pulsation rate differ more than 500-fold: rheogram amplitude at the aorta is ΔZ = 0.1 Ω; at the femur, ΔZ = 0.05 Ω; at the dental pulp, ΔZ = 25 Ω. To provide rheogram sampling error of no more than 2%, the noise level should be no more than ~0.002 Ω (signal/noise ratio, 50,000). These requirements are more stringent than in the case of electrocardiosignal (ECS) detection (allowable noise level, 20 μV) [1]. Thus, an asymmetrical ECS channel is not appropriate to monitor impedance dynamics because ECS and rheosignal occu- py virtually equal frequency ranges. A dynamic bioimpedance meter should meet strin- gent requirements in time and frequency resolution. Improvement of performance of a bioimpedance meter requires further methodological research and modeling under MATLAB. A model with two synchronous detec- tors for quadrature impedance components was used as the prototype model of the bioimpedance meter. A model of the monitoring tract based on standard graphical units of Matlab/Simulink software is shown in Fig. 1. The model contains two multipliers, instrumenta- tional amplifier (IA), monitoring electrodes, high-fre- quency filters, and two sinusoidal generators with π/2 phase shift. The model of dynamic component of bioimpedance was used to generate the test signal [2]. Cardiosignal was simulated using a divider with variable frequency equal to pulsation rate. Output voltage of the divider was con- trolled by a generator with frequency equal to that of car- diosignal. The 1-mV generator was used as model of car- diosignal. The LFF cutting frequency was selected to pass the constant signal component (0.1 Hz). Another LFF passes the cardiosignal frequency up to 50 Hz. The noise resistance of the monitoring tract was test- ed using quadrature detector testing only one channel. The power of the cardiosignal and the noise signal was concentrated at the same frequency. Signal/noise ratio was measured using the spectroanalyzer shown in Fig. 1. The window of the spectroanalyzer settings is shown in Fig. 2a; the Simulink Extras_ Additional sinks library window is shown in Fig. 2b. The spectroanalyzer parameters are: – length of buffer in readings; – number of points for FFT (fast Fourier transform); – plot after how many points; – sample time (sec). Spectroanalyzer windows for ECS simulation (a) and for hemodynamics simulation (b) are shown in Fig. 3. The windows contain three curves: – time curve of input signal; Biomedical Engineering, Vol. 47, No. 4, November, 2013, pp. 205-208. Translated from Meditsinskaya Tekhnika, Vol. 47, No. 4, Jul.-Aug., 2013, pp. 30-32. Original article submitted May 16, 2013. 205 0006-3398/13/4704-0205 © 2013 Springer Science+Business Media New York 1 South-Western State University, Kursk, Russia; E-mail: shatolg@mail.ru 2 Sana'a University, Sana'a, Yemen. * To whom correspondence should be addressed. Simulation of the Effect of Electrocardiosignal on Evaluation of the Dynamic Component of Bioimpedance Awadh Ali Mohammed 1 , O. V. Shatalova 1 *, Adel Mohammed AlKdasi 2 , and V. N. Snopkov 1 Simulation of the effect of electrocardiosignal on evaluation of the dynamic component of bioimpedance is pro- posed for optimizing the structure of an impedance meter based on combined mathematical and physical model- ing of a virtual bioimpedance analyzer implemented using the Simulink software for MATLAB. The virtual model of the bioimpedance analyzer optimizes monitoring of current parameters with respect to signal/noise ratio, as well as determines limiting parameters of the monitoring circuitry.