Fetal heart rate variability (fHRV) is an important indicator of health and disease, yet its physiological origins, neural contributions in particular, are not well understood. We aimed to develop novel experimental and data analytical approaches to identify fHRV measures reflecting the vagus nerve contributions to fHRV. In near-term ovine fetuses, a comprehensive set of 46 fHRV measures was computed from fetal pre-cordial electrocardiogram recorded during surgery and 72 hours later without (n=24) and with intra-surgical bilateral cervical vagotomy (n=15). The fetal heart rate did not change due to vagotomy. We identify fHRV measures specific to the vagal modulation of fHRV: Multiscale time irreversibility asymmetry index (AsymI), Detrended fluctuation analysis (DFA) α 1 , Kullback-Leibler permutation entropy (KLPE) and Scale dependent Lyapunov exponent slope (SDLE α). We provide a systematic delineation of vagal contributions to fHRV across signal-analytical domains which should be relevant for the emerging field of bioelectronic medicine and the deciphering of the "vagus code". Our findings also have clinical significance for in utero monitoring of fetal health during surgery.
Key points• Fetal surgery causes a complex pattern of changes in heart rate variability measures with overall reduction of complexity or variability • At 72 hours after surgery, many of the HRV measures recover and this recovery is delayed by an intrasurgical cervical bilateral vagotomy • We identify HRV pattern representing complete vagal withdrawal that can be understood as part of "HRV code", rather than any single HRV measure • We identify HRV biomarkers of recovery from fetal surgery and discuss the effect of anticholinergic medication on this recovery al. , 2011;Sassi et al. , 2015) While some of these techniques capture properties of time series' variability that are correlated, most offer a unique aspect of variability by characterizing different mathematical properties of the signals. They include measures characterizing the statistical properties (e.g., standard deviation, root mean square of successive differences of R-R intervals of electrocardiogram (ECG), RMSSD), the informational complexity (e.g., entropy measures), the pattern of variations across time scales (i.e., time-invariant features such as fractal measures, power law exponents) or the energy contained in the signal (e.g., spectral measures). Researchers generally agree that a plurality of techniques offers the most complete evaluation. (Goldberger, 1996;Goldberger et al. , 2002) An important underlying assumption about studying biological variability is that it has predictive properties about physiological systems from which it emerges. Hence, variability properties are a form of biological code, taking place on different scales in space and time. While the time component can be taken literally, the space component can represent different organs and regions of the body contributing to the properties of a variability pattern in question. The space component can also...