Resumen de Development of new algorithms for depth of anesthesia monitoring integrating cerebral blood flow estimations
Cerebral blood flow (CBF) reflects the rate of delivery of arterial blood to the brain. Since no nutrients, oxygen or water can be stored in the cranial cavity due to space and pressure restrictions, a continuous perfusion of the brain is critical for survival. Anesthesia is known to affect cerebral hemodynamics , but CBF is only monitored in critical patients due to the lack of a continuous and affordable bedside monitor for this purpose. This Thesis propases a potential solution through bioelectrical impedance, also known as rheoencephalography (REG), that could fill the existing gap for a low-cost and effective CBF monitor. The underlying hypothesis is that REG signals carry information on CBF that might be recovered by means of the application of advanced signal processing techniques, allowing to track CBF changes during anesthesia.
Firstly, clinical and technical information related to other CBF monitoring methods and signal processing algorithms suitable for REG signals are provided. Subsequently, clinical data collected under the scope of this project and used to develop and validate the techniques applied to REG signals are described. As a first step in the analysis of REG signals, different filter options for noise removal are presented, in the linear and nonlinear domain. The nonlinear filler applied to the signal attractor showed a better accuracy, mainly in noisy environments, but its computational burden compromises its use in real time monitoring of physiological signals. The analysis of REG starts with the use of geometric features extracted from the time domain, which is the standard processin strategy for this type of data. Geometric features are tested for their ability to detect apneas in young healthy volunteers and to distinguish between different anesthetic depths. Their performance is poor in apnea detection, but they are capable oftracking cerebral hemodynamic changes during anesthesia.
Two new approaches are proposed, one based on the nonlinear dynamics of REG signals, extracting the descriptors ofthe attractors reconstructed form REG signals, and one based on entropy rates. Both show significant differences between apnea and baseline recordings, as well as between different anesthetic states. This is a key finding, providing an alternative to Standard processing of REG signals and supporting the hypothesis that REG signals carry CBF information. Among the three proposed techniques, the parameters allowing real time data processing are used to classify REG recordings collected in awake and anesthetized patients. An accuracy of 70% is reached, indicating that CBF changes in REG are related to the anesthetic state of the patient but presenting large variabilities. REG parameters should not aim at replacing depth of anesthesia monitors but should rather help maintaining cerebral hemodynamic stability during anesthesia. Hence, the relationship between global hemodynamics, cerebral hemodynamics and EEG parameters are analyzed, looking for causal relationships among them. lnteractions are detected during steady state anesthesia, anesthetic drug infusion and patient positioning, providing evidence of the coupling between hemodynamics and brain activity.
As a conclusion, alternative methods for REG processing that confirm the hypothesis that REG signals carry CBF information are provided. The simplicity of the technology and its low cost and easily interpretable outcomes should provide a new opportunity for REG to reach standard clinical practice. Moreover, causal relationships among the hemodynamic physiological signals and brain activity are assessed, suggesting that the inclusion of REG information in depth of anesthesia monitors could be of valuable use for an affordable, noninvasive bedside tool to prevent unwanted CBF alterations during anesthetic procedures.
