In vivo Measurement of Cardiac Sympathovagal Nerve Activities and Its Relationship to HRV Parameters in Ambulatory Dogs

Abstract

Heart rate variability (HRV) analysis is well known for assessing cardiac autonomic nervous activities (CANA). With the development of an implantable radio transmitter system, direct measurement of CANA became possible for ambulatory animals. Thus, the direct comparison between HRV parameters and sympathovagal balance also became possible. However, measured CANAs include not only CANA but also cardiac electric activity (CEA) that prevents accurate quantification of CANAs. In this study, we propose a novel CEA removal method using moving standard deviation and cubic smoothing spline. This method consists of two steps of detecting CEA segments and eliminating CEAs in detected segments. Using implanted devices, we recorded stellate ganglion nerve activity (SGNA), vagal nerve activity (VNA), and superior left ganglionated plexi nerve activity (SLGPNA) directly from four ambulatory dogs. The CEA-removal performance of the proposed method was evaluated and compared with commonly used high pass filtration (HPF) for various heart rates and CANA amplitudes. Results tested with simulated CEA and simulated true CANA revealed stable and excellent performance of the suggested method compared to the HPF method. The averaged relative error percentages of the proposed method were less than 0.67%, 0.65%, and 1.76% for SGNA, VNA, and SLGPNA, respectively. We also compared SGNA and VNA to HRV parameters after normalizing SGNA and VNA by its minimum value into sympathetic nervous activity (SNA) and parasympathetic nervous activity (PNA). Using SNA and PNA, amended sympathetic and parasympathetic activity ratio (ASPR) was defined. HRV parameters were assessed by selecting SNA activated and deactivated segments and effect of PNA was also assessed. To estimate ASPR from HRV parameters, multiple linear regression and the neural network method were employed. The results showed r=0.71 for the multiple linear regression method and accuracies were 89.7% and 86.3% for the neural network method. It is expected that results of this study will contribute to the discussion of HRV analysis and will improve the ability of HRV parameters to assess CANA.