Analysis of heart rate variability dynamics during propofol and dexmedetomidine anesthesia

HRV-derived data similarity and distribution index based on ensemble neural network for measuring depth of anaesthesia

Evaluation of depth of anaesthesia (DoA) is critical in clinical surgery. Indices derived from electroencephalogram (EEG) are currently widely used to quantify DoA. However, there are known to be inaccurate under certain conditions; therefore, experienced anaesthesiologists rely on the monitoring of vital signs such as body temperature, pulse rate, respiration rate, and blood pressure to control the procedure. Because of the lack of an ideal approach for quantifying level of consciousness, studies have been conducted to develop improved methods of measuring DoA. In this study, a short-term index known as the similarity and distribution index (SDI) is proposed. The SDI is generated using heart rate variability (HRV) in the time domain and is based on observations of data distribution differences between two consecutive 32 s HRV data segments. A comparison between SDI results and expert assessments of consciousness level revealed that the SDI has strong correlation with anaesthetic depth. To optimise the effect, artificial neural network (ANN) models were constructed to fit the SDI, and ANN blind cross-validation was conducted to overcome random errors and overfitting problems. An ensemble ANN was then employed and was discovered to provide favourable DoA assessment in comparison with commonly used Bispectral Index. This study demonstrated the effectiveness of this method of DoA assessment, and the results imply that it is feasible and meaningful to use the SDI to measure DoA with the additional use of other measurement methods, if appropriate.

Hemodynamic effects of dexmedetomidine during intra-operative electrocorticography for epilepsy surgery

Background:

Dexmedetomidine, a predominant alpha-2-adrenergic agonist has been used in anesthetic practice to provide good sedation. The drug is being recently used in neuroanesthesia during awake surgery for brain tumors and in functional neurosurgery.

Materials and Methods:

This prospective study analyzed the hemodynamic effects of dexmedetomidine infusion during electrocorticography in patients undergoing surgery for mesial temporal sclerosis. Dexmedetomidine infusion was administered during intra-operative electrocorticography recording, 15 minutes after the end tidal MAC of N₂O and isoflurane were decreased to zero. Anesthesia was maintained with O₂ : air mixture = 50:50, vecuronium and fentanyl. Heart rate (HR), mean arterial pressure (MAP) and end tidal carbon dioxide (ETCO₂) were recorded across at induction, 2 min prior to dexmedetomidine (PreDEX), 5 min during dexmedetomidine infusion (DEX; 1 μg/kg), 5 min after stopping dexmedetomidine and 10 minutes after stopping dexmedetomidine.

Results:

Forty patients with mesial temporal sclerosis (M: F = 27:13, mean age = 28.15 ± 10.9 years; duration of epilepsy = 12.0 ± 7.9 years) underwent anterior temporal lobe resection with amygdalohippocampectomy for drug-resistant epilepsy. Infusion of dexmedetomidine caused a transient fall in HR in 87.5% of patients and an increase in MAP in 62.5% of patients, which showed a tendency to revert back towards PreDEX values within 10 min after stopping the infusion. Sixty-five percent of the patients showed ≤25% reduction and 10% of them showed >25% reduction in HR. 47.5% of the patients showed ≤25% increase and 15% of them showed >25% increase in MAP. These changes were over a narrow range and within physiological limits.

Conclusion:

The infusion of dexmedetomidine for a short period causes reduction of HR and increase in MAP in patients, however the variations are within acceptable range.

Monitoring heartbeat nonlinear dynamics during general anesthesia by using the instantaneous dominant Lyapunov exponent

We present a novel methodology for instantaneous estimation of quantitative correlates of depth of Anesthesia from noninvasive electrocardiographic recordings. The analysis is based on a point process model of heartbeat dynamics that allows for continuous tracking of linear and nonlinear HRV indices, including a novel instantaneous assessment of the Lyapunov Spectrum by using a cubic autoregressive formulation. The effective estimation of the model parameters is ensured by the Laguerre expansion of the Wiener-Volterra kernels along with the maximum local log-likelihood procedure. We apply the proposed assessment to experimental recordings from healthy subjects during propofol anesthesia. The new assessment reveals novel time-varying complex heartbeat dynamics that underlie the quasi-periodic heartbeat fluctuations elicited by the sympatho-vagal balance. Results suggest that such quantification provides important information which is independent from the standard autonomic assessment and significantly correlated with loss of consciousness. Further investigation will focus on evolving our mathematical approach towards a promising monitoring tool for an accurate, noninvasive assessment of general anesthesia.