Autonomic cardiovascular modulation with three different anesthetic strategies during neurosurgical procedures.

BACKGROUND: Autonomic cardiovascular modulation during surgery might be affected by different anesthetic strategies. Aim of the present study was to assess autonomic control during three different anesthetic strategies in the course of neurosurgical procedures by the linear and non-linear analysis of two cardiovascular signals. METHODS: Heart rate (EKG-RR intervals) and systolic arterial pressure (SAP) signals were analyzed in 93 patients during elective neurosurgical procedures at fixed points: anesthetic induction, dura mater opening, first and second hour of surgery, dura mater and skin closure. Patients were randomly assigned to three anesthetic strategies: sevoflurane+fentanyl (S-F), sevoflurane+remifentanil (S-R) and propofol+remifentanil (P-R). RESULTS: All the three anesthetic strategies were characterized by a reduction of RR and SAP variability. A more active autonomic sympathetic modulation, as ratio of low to high frequency spectral components of RR variability (LF/HF), was present in the P-R group vs. S-R group. This is confirmed by non-linear symbolic analysis of RR series and SAP variability analysis. In addition, an increased parasympathetic modulation was suggested by symbolic analysis of RR series during the second hour of surgery in S-F group. CONCLUSION: Despite an important reduction of cardiovascular signal variability, the analysis of RR and SAP signals were capable to detect information about autonomic control during anesthesia. Symbolic analysis (non-linear) seems to be able to highlight the differences of both the sympathetic (slow) and vagal (fast) modulation among anesthetics, while spectral analysis (linear) underlines the same differences but only in terms of balance between the two neural control systems.

K-nearest-neighbor conditional entropy approach for the assessment of the short-term complexity of cardiovascular control.

Complexity analysis of short-term cardiovascular control is traditionally performed using entropy-based approaches including corrective terms or strategies to cope with the loss of reliability of conditional distributions with pattern length. This study proposes a new approach aiming at the estimation of conditional entropy (CE) from short data segments (about 250 samples) based on the k-nearest-neighbor technique. The main advantages are: (i) the control of the loss of reliability of the conditional distributions with the pattern length without introducing a priori information; (ii) the assessment of complexity indexes without fixing the pattern length to an arbitrary low value. The approach, referred to as k-nearest-neighbor conditional entropy (KNNCE), was contrasted with corrected approximate entropy (CApEn), sample entropy (SampEn) and corrected CE (CCE), being the most frequently exploited approaches for entropy-based complexity analysis of short cardiovascular series. Complexity indexes were evaluated during the selective pharmacological blockade of the vagal and/or sympathetic branches of the autonomic nervous system. We found that KNNCE was more powerful than CCE in detecting the decrease of complexity of heart period variability imposed by double autonomic blockade. In addition, KNNCE provides indexes indistinguishable from those derived from CApEn and SampEn. Since this result was obtained without using strategies to correct the CE estimate and without fixing the embedding dimension to an arbitrary low value, KNNCE is potentially more valuable than CCE, CApEn and SampEn when the number of past samples most useful to reduce the uncertainty of future behaviors is high and/or variable among conditions and/or groups.

Short-term complexity indexes of heart period and systolic arterial pressure variabilities provide complementary information.

It is unclear whether the complexity of the variability of the systolic arterial pressure (SAP) provides complementary information to that of the heart period (HP). The complexity of HP and SAP variabilities was assessed from short beat-to-beat recordings (i.e., 256 cardiac beats). The evaluation was made during a pharmacological protocol that induced vagal blockade with atropine or a sympathetic blockade (beta-adrenergic blockade with propranolol or central sympathetic blockade with clonidine) alone or in combination, during a graded head-up tilt, and in patients with Parkinson's disease (PD) without orthostatic hypotension undergoing orthostatic challenge. Complexity was quantified according to the mean square prediction error (MSPE) derived from univariate autoregressive (AR) and multivariate AR (MAR) models. We found that: 1) MSPE(MAR) did not provide additional information to that of MSPE(AR); 2) SAP variability was less complex than that of HP; 3) because HP complexity was reduced by either vagal blockade or vagal withdrawal induced by head-up tilt and was unaffected by beta-adrenergic blockade, HP was under vagal control; 4) because SAP complexity was increased by central sympathetic blockade and was unmodified by either vagal blockade or vagal withdrawal induced by head-up tilt, SAP was under sympathetic control; 5) SAP complexity was increased in patients with PD; and 6) during orthostatic challenge, the complexity of both HP and SAP variabilities in patients with PD remained high, thus indicating both vagal and sympathetic impairments. Complexity indexes derived from short HP and SAP beat-to-beat series provide complementary information and are helpful in detecting early autonomic dysfunction in patients with PD well before circulatory symptoms become noticeable.

Information transfer through the spontaneous baroreflex in healthy humans.

OBJECTIVES: This study assesses the information transfer through the spontaneous baroreflex (i.e. through the pathway linking systolic arterial pressure to heart period) during an experimental condition soliciting baroreflex (i.e. head-up tilt). METHODS: The information transfer was calculated as the conditional entropy of heart period given systolic arterial pressure using a mutual neighbor approach and uniform quantization. The information transfer was monitored as a function of the forecasting time k. RESULTS: We found that during head-up tilt the information transfer at k = 0 decreased but the rate of rise of information transfer as a function of k was faster. CONCLUSIONS: We suggest that the characterization of the information transfer from systolic arterial pressure to heart period might complement the traditional characterization of the spontaneous baroreflex based on transfer function analysis.

Empirical mode decomposition to assess baroreflex gain from spontaneous variability during exercise in humans.

Estimation of the baroreflex gain has become an important tool in clinical practice in order to assess cardiac autonomic system control. Spectral analysis and sequence analysis techniques based on the spontaneous variability of systolic arterial pressure and heart period have been proposed to evaluate the baroreflex gain. These analyses can be significantly altered by the presence of nonstationarities. Recently, the empirical mode decomposition (EMD), a signal processing technique particularly suitable for nonstationary series, has been proposed as a new tool for data analysis. The aim of this study is to propose EMD-based approaches to the evaluation of the baroreflex gain to account for the possible presence of nonstationarities of systolic arterial pressure and heart period series.