The role of the autonomic nervous system in hypertension: a bond graph model study.

A bond graph model of the cardiovascular system with embedded autonomic nervous regulation was developed for a better understanding of the role of the autonomic nervous system (ANS) in hypertension. The model is described by a pump model of the heart and a detailed representation of the head and neck, pulmonary, coronary, abdomen and extremity circulation. It responds to sympathetic and parasympathetic activities by modifying systemic peripheral vascular resistance, heart rate, ventricular end-systolic elastance and venous unstressed volumes. The impairment of ANS is represented by an elevation of the baroreflex set point. The simulation results show that, compared with normotensive, in hypertension the systolic and diastolic blood pressure (SBP/DBP) rose from 112/77 mmHg to 144/94 mmHg and the left ventricular wall thickness (LVWT) increased from 10 mm to 12.74 mm. In the case that ANS regulation was absent, both the SBP and DBP further increased by 8 mmHg and the LVWT increased to 13.22 mm. The results also demonstrate that when ANS regulation is not severely damaged, e.g. the baroreflex set point is 97 mmHg, it still has an effect in preventing the rapid rise of blood pressure in hypertension; however, with the worsening of ANS regulation, its protective role weakens. The results agree with human physiological and pathological features in hemodynamic parameters and carotid baroreflex function curves, and indicate the role of ANS in blood pressure regulation and heart protection. In conclusion, the present model may provide a valid approach to study the pathophysiological conditions of the cardiovascular system and the mechanism of ANS regulation.

Enhancing the deceleration capacity index of heart rate by modified-phase-rectified signal averaging.

Deceleration capacity (DC) of heart rate is a novel indicator of autonomic nervous system (ANS) activity. In this paper, we proposed a modified DC index based on improved phase-rectified signal averaging (PRSA) algorithm. Sinusoidal analysis is applied to elucidate the rationality of the improved PRSA. Then the validity of the modified DC is verified by the databases of chronic heart failure (CHF) patients and control group. Both the conventional and modified DCs are significantly lower in CHF patients than that in the control group (2.12 +/- 2.98 vs. 6.34 +/- 1.92 ms, P < 0.0001 and 5.45 +/- 2.48 vs. 10.64 +/- 1.76 ms, P < 0.0001, respectively). And the modified DC provides higher accuracy in distinguishing CHF than the conventional one (87.4 vs. 82.1%). The results indicate that the suggested technique enhances the performance of PRSA and improves the efficiency of DC in assessing ANS activity in CHF patients.