Autonomic nervous activity analysis based on visibility graph complex networks and skin sympathetic nerve activity - Cai, Zhipeng; Cheng, Hongyi; Xing, Yantao; Chen, Feifei; Zhang, Yike; Cui, Chang (Frontiers in Physiology, 2022)
Background: Autonomic nerve system (ANS) plays an important role in regulating cardiovascular function and cerebrovascular function. Traditional heart rate variation (HRV) and emerging skin sympathetic nerve activity (SKNA) analyses from ultra-short-time (UST) data cannot fully reveal neural activity, thereby quantitatively reflect ANS intensity. Methods: Electrocardiogram and SKNA from sixteen patients (seven cerebral hemorrhage (CH) patients and nine control group (CO) patients) were recorded using a portable device. The derived HRV and visibility graph (VG) features were compared on 5-min and UST segments to verify their validity and robustness in discriminating CH and CO under different data lengths. Besides, their potential for quantifying ANS-Load were also investigated. Results: Comparison of HRV and VG features between CH and CO showed that most HRV features were not significantly different in distribution, while almost all VG features were clearly distinguishable between the two groups. For ANS analysis on UST segment, the time-domain features (NNmean, SDNN, RMSSD) and most frequency-domain features (LF, HF, LF/HF) in HRV features and the CC, Trans, Dia and GE of VG features remained stable in both activated and inactivated segments across all data lengths. The comparison results of HRV and VG features under different ANS-Load show that most HRV features (SDNN, LFHF, RMSSD, vLF, LF and HF) and almost all VG features were correlated to sympathetic nerve activity intensity. Conclusions: The proposed autonomic nervous activity analysis method based on VG and SKNA offers a new insight into ANS assessment in UST segments and ANS-Load quantification.