Estimation of the depth of anaesthesia from interacting physiological oscillators

Nowadays, physiological monitoring is imperative for the safety of medical operations. However, systems which monitor the depth of anaesthesia are still far from reliable, such that still some patients may experience the trauma of remaining conscious under general anaesthesia during surgery. The long term goal of our interdisciplinary project "BRACCIA" was to develop a device to measure the depth of anaesthesia. And, in view of this important goal, the main objective of research was to establish how the couplings between the cardiac, respiratory and cortical oscillations change in anaesthesia. Under the framework of this project, our objectives were: 1) The detection of the deep-light change of anaesthesia from experimental recordings on rats, and furthermore, the investigation of the interdependencies among three physiological activities, namely, the cardiac activity (H), respiration (R) and cortical activities (B) from experimental recordings of rats and humans, for each state of the depth of anaesthesia. 2) The modelling of the slow brain waves, and to consider the effect of anaesthesia on this realized model. The analysis of the recordings were carried out with five methods. First method is the "S-estimator", which indirectly quantifies the amount of synchronization within a data set measuring the contraction of the embedding dimension of the state space. Second method is the "new S-estimator". In this new one, a linear transformation of the reconstructed state space trajectory orthonormalizes the state variables within each model such that global state space volume reduction becomes a measure of synchronization exclusively between the different models. Third method is the "embedding dimension analysis", which examines the time evolution of the embedding dimension obtained with false nearest neighbors method on each windowed time series. The last two methods consist in a more detailed analysis of the dependencies among three systems. Fourth method is the "coupling matrix". This calculates the coupling matrix, CM, which infers linear interactions between multivariate time series after constructing separately the self model of each signal from reconstructed states. The last method is the "nonparametric Granger causality". This method calculates the Granger causality, GC, which measures bivariate causal influence in frequency domain. Here, a nonparametric estimation approach was used to remove the difficulties such as uncertainty in model parameters. For this method, permutation tests are added to figure out the real causality. From the obtained results, in most of the groups of experimental recordings, a change in the synchronization inside of the whole system between deep and light anaesthesia or between resting state and anaesthesia was found. For the results on Ketamine-Xylazine (KX) anaesthetized rats, a decrease of this synchronization was so clear that we succeeded to detect the deep-light transition of the anaesthesia, and automatically