Prospective study comparing skin impedance with EEG parameters during the induction of anaesthesia with fentanyl and etomidate

<p>Abstract</p> <p>Objective</p> <p>Sympathetic stimulation leads to a change in electrical skin impedance. So far it is unclear whether this effect can be used to measure the effects of anaesthetics during general anaesthesia. The aim of this prospective study is to determine the electrical skin impedance during induction of anaesthesia for coronary artery bypass surgery with fentanyl and etomidate.</p> <p>Methods</p> <p>The electrical skin impedance was measured with the help of an electro-sympathicograph (ESG). In 47 patients scheduled for elective cardiac surgery, anaesthesia was induced with intravenous fentanyl 10 μg/kg and etomidate 0.3 mg/kg. During induction, the ESG (Electrosympathicograph), BIS (Bispectral IndeX), BP (arterial blood pressure) and HR (heart rate) values of each patient were recorded every 20 seconds. The observation period from administration of fentanyl to intubation for surgery lasted 4 min.</p> <p>Results</p> <p>The ESG recorded significant changes in the electrical skin impedance after administration of fentanyl and etomidate(p < 0.05). During induction of anaesthesia, significant changes of BIS, HR and blood pressure were observed as well (p < 0.05).</p> <p>Conclusions</p> <p>The electrical skin impedance measurement may be used to monitor the effects of anesthetics during general anaesthesia.</p

Acute pain intensity monitoring with the classification of multiple physiological parameters.

Current acute pain intensity assessment tools are mainly based on self-reporting by patients, which is impractical for non-communicative, sedated or critically ill patients. In previous studies, various physiological signals have been observed qualitatively as a potential pain intensity index. On the basis of that, this study aims at developing a continuous pain monitoring method with the classification of multiple physiological parameters. Heart rate (HR), breath rate (BR), galvanic skin response (GSR) and facial surface electromyogram were collected from 30 healthy volunteers under thermal and electrical pain stimuli. The collected samples were labelled as no pain, mild pain or moderate/severe pain based on a self-reported visual analogue scale. The patterns of these three classes were first observed from the distribution of the 13 processed physiological parameters. Then, artificial neural network classifiers were trained, validated and tested with the physiological parameters. The average classification accuracy was 70.6%. The same method was applied to the medians of each class in each test and accuracy was improved to 83.3%. With facial electromyogram, the adaptivity of this method to a new subject was improved as the recognition accuracy of moderate/severe pain in leave-one-subject-out cross-validation was promoted from 74.9 ± 21.0 to 76.3 ± 18.1%. Among healthy volunteers, GSR, HR and BR were better correlated to pain intensity variations than facial muscle activities. The classification of multiple accessible physiological parameters can potentially provide a way to differentiate among no, mild and moderate/severe acute experimental pain