Coherence analysis of peripheral blood flow signals is a potential method for evaluating autonomic nervous system function

Qiuyue Lyu¹Yuning Qin²Xin Wang³Qizhen Wang²Qi Liu⁴Na Tu¹Yuhan Liu⁵Zixin Huo⁵Xiaojing Song¹Shuyou Wang¹Weibo Zhang¹Xue Cao²Enshi Lu²Xiaoliang Zhao²Shuyong Jia^1*Liyun He^2*Guangjun Wang^1*

• ¹Institute of Acupuncture and Moxibustion, China Academy of Chinese Medical Sciences, Beijing, China
• ²China Academy of Chinese Medical Sciences Institute of Basic Research in Clinical Medicine, Beijing, China
• ³China Academy of Chinese Medical Sciences, Beijing, China
• ⁴China Academy of Chinese Medical Sciences Institute of Acupuncture and Moxibustion, Beijing, China
• ⁵Tianjin University of Traditional Chinese Medicine, Tianjin, China

The autonomic nervous system (ANS) is crucial for maintaining homeostasis in the body and plays an important role in cardiovascular diseases. The evaluation of ANS function can be achieved through various methods, including heart rate variability (HRV) analysis, direct measurement of sympathetic nerve activity by microneurography, detection of plasma catecholamine levels and evaluation of pressure-sensing reflexes etc. Among them, HRV has become one of the commonly used indicators in clinical practice and research due to its non-invasiveness and ease of operation. Only the pulsation of the heart is considered, without taking into account the peripheral blood vessels, which are also regulated by ANS function. Therefore, HRV has certain limitations in evaluating ANS function. In this study, the ANS function of healthy subjects was artificially disturbed by drinking glucose of different temperatures. The results indicate that through the coherence analysis of peripheral blood flow signals, the peak values within specific frequency bands are significantly correlated with heart rate variability, suggesting that peripheral blood flow signals can be used to evaluate the autonomic nervous system function. Finally, the quantitative relationship was verified by using an independent dataset. This provides a new perspective for the non-invasive assessment of ANS, supplementing traditional HRV evaluation methods and potentially establishing a more comprehensive assessment system.