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BackgroundPrecise monitoring of the Depth of Anesthesia (DoA) is essential to prevent intra-operative awareness (in case of underdosage) or increased post-operative morbi-mortality (in case of overdosage). The recording of a high- frequency multimodal monitoring during general anesthesia (GA) and the capability of classification of dynamic networks should have the potential to help predicting the DoA in a clinical practice. In this study, we aimed at predicting the DoA according four levels (Awake, Loss of Consciousness (LOC), Anesthesia, Return of Consciousness (ROC), Emergence) thanks to a Hidden Markov Model (HMM) relying on four common physiologic variables: Mean Blood Pressure (MBP), Heart Rate (HR), Respiratory Rate (RR), and end-expiratory concentration of sevoflurane (AAEt).MethodsAfter induction by sufentanil and propofol, the anesthesia was maintained by sevoflurane. We recorded the physiological variables at a high frequency during all the procedure [cardiopulmonary variables, AAEt, 2- channel ElectroEncephaloGraphy (EEG) data, and BIS values]. In the training phase, the different states (Awake, LOC, Anesthesia, ROC, Emergence) were identified according to the reading of the spectrograms of the two EEG channels. However, the prediction with the HMM were only based on the four physiological variables.ResultsOn a dataset consisting of 60 patients under general anaesthesia, results suggested that the HMM had a true positive rate (TPR) for identifying Awake, Anesthesia and Emergence of 88%, 72% and 58%, respectively.ConclusionTo our knowledge, this is the first application of such a model to identify the DoA without relying on EEG data. We suggest that a HMM can help the anesthetist monitoring the DoA out of a set of current physiologic variables without necessity of brain monitoring. The model could be improved by increasing the number of patients in the database and accuracy would probably benefit from adding in the model the data of a single EEG channel.

Conventional methods for estimating cardiopulmonary variables usually require complex gas analyzers and the active co-operation of the patient. Therefore, they are not compatible with the crowded environment of the intensive care unit (ICU) or operating theatre, where patient co-operation is typically impossible. However, it is these patients that would benefit the most from accurate estimation of cardiopulmonary variables, because of their critical condition. This paper describes the results of a collaborative development between an anesthesiologists and biomedical engineers to create a compact and non-invasive system for the measurement of cardiopulmonary variables such as lung volume, airway dead space volume, and pulmonary blood flow. In contrast with conventional methods, the compact apparatus and non-invasive nature of the proposed method allow it to be used in the ICU, as well as in general clinical settings. We propose the use of a non-invasive method, in which tracer gases are injected into the patient's inspired breath, and the concentration of the tracer gases is subsequently measured. A novel breath-by-breath tidal ventilation model is then used to estimate the value of a patient's cardiopulmonary variables. Experimental results from an artificial lung demonstrate minimal error in the estimation of known parameters using the proposed method. Results from analysis of a cohort of 20 healthy volunteers (within the Oxford University Hospitals NHS Trust) show that the values of estimated cardiopulmonary variables from these subjects lies within the expected ranges. Advantages of this method are that it is non-invasive, compact, portable, and can perform analysis in real time with less than 1 min of acquired respiratory data.