Machine learning for a combined electroencephalographic anesthesia index to detect awareness under anesthesia

Spontaneous electroencephalogram (EEG) and auditory evoked potentials (AEP) have been suggested to monitor the level of consciousness during anesthesia. As both signals reflect different neuronal pathways, a combination of parameters from both signals may provide broader information about the brain status during anesthesia. Appropriate parameter selection and combination to a single index is crucial to take advantage of this potential. The field of machine learning offers algorithms for both parameter selection and combination. In this study, several established machine learning approaches including a method for the selection of suitable signal parameters and classification algorithms are applied to construct an index which predicts responsiveness in anesthetized patients. The present analysis considers several classification algorithms, among those support vector machines, artificial neural networks and Bayesian learning algorithms. On the basis of data from the transition between consciousness and unconsciousness, a combination of EEG and AEP signal parameters developed with automated methods provides a maximum prediction probability of 0.935, which is higher than 0.916 (for EEG parameters) and 0.880 (for AEP parameters) using a cross-validation approach. This suggests that machine learning techniques can successfully be applied to develop an improved combined EEG and AEP parameter to separate consciousness from unconsciousness

Cross-approximate entropy of cortical local field potentials quantifies effects of anesthesia - a pilot study in rats

<p>Abstract</p> <p>Background</p> <p>Anesthetics dose-dependently shift electroencephalographic (EEG) activity towards high-amplitude, slow rhythms, indicative of a synchronization of neuronal activity in thalamocortical networks. Additionally, they uncouple brain areas in higher (gamma) frequency ranges possibly underlying conscious perception. It is currently thought that both effects may impair brain function by impeding proper information exchange between cortical areas. But what happens at the local network level? Local networks with strong excitatory interconnections may be more resilient towards global changes in brain rhythms, but depend heavily on locally projecting, inhibitory interneurons. As anesthetics bias cortical networks towards inhibition, we hypothesized that they may cause excessive synchrony and compromise information processing already on a small spatial scale. Using a recently introduced measure of signal independence, cross-approximate entropy (XApEn), we investigated to what degree anesthetics synchronized local cortical network activity. We recorded local field potentials (LFP) from the somatosensory cortex of three rats chronically implanted with multielectrode arrays and compared activity patterns under control (awake state) with those at increasing concentrations of isoflurane, enflurane and halothane.</p> <p>Results</p> <p>Cortical LFP signals were more synchronous, as expressed by XApEn, in the presence of anesthetics. Specifically, XApEn was a monotonously declining function of anesthetic concentration. Isoflurane and enflurane were indistinguishable; at a concentration of 1 MAC (the minimum alveolar concentration required to suppress movement in response to noxious stimuli in 50% of subjects) both volatile agents reduced XApEn by about 70%, whereas halothane was less potent (50% reduction).</p> <p>Conclusions</p> <p>The results suggest that anesthetics strongly diminish the independence of operation of local cortical neuronal populations, and that the quantification of these effects in terms of XApEn has a similar discriminatory power as changes of spontaneous action potential rates. Thus, XApEn of field potentials recorded from local cortical networks provides valuable information on the anesthetic state of the brain.</p

A mobile phone based alarm system for supervising vital parameters in free moving rats

Background: Study protocols involving experimental animals often require the monitoring of different parameters not only in anesthetized, but also in free moving animals. Most animal research involves small rodents, in which continuously monitoring parameters such as temperature and heart rate is very stressful for the awake animals or simply not possible. Aim of the underlying study was to monitor heart rate, temperature and activity and to assess inflammation in the heart, lungs, liver and kidney in the early postoperative phase after experimental cardiopulmonary bypass involving 45 min of deep hypothermic circulatory arrest in rats. Besides continuous monitoring of heart rate, temperature and behavioural activity, the main focus was on avoiding uncontrolled death of an animal in the early postoperative phase in order to harvest relevant organs before autolysis would render them unsuitable for the assessment of inflammation. Findings: We therefore set up a telemetry-based system (Data Science International, DSI™) that continuously monitored the rat’s temperature, heart rate and activity in their cages. The data collection using telemetry was combined with an analysis software (Microsoft excel™), a webmail application (GMX) and a text message-service. Whenever an animal’s heart rate dropped below the pre-defined threshold of 150 beats per minute (bpm), a notification in the form of a text message was automatically sent to the experimenter’s mobile phone. With

Anaesthesia Monitoring by Recurrence Quantification Analysis of EEG Data

Appropriate monitoring of the depth of anaesthesia is crucial to prevent deleterious effects of insufficient anaesthesia on surgical patients. Since cardiovascular parameters and motor response testing may fail to display awareness during surgery, attempts are made to utilise alterations in brain activity as reliable markers of the anaesthetic state. Here we present a novel, promising approach for anaesthesia monitoring, basing on recurrence quantification analysis (RQA) of EEG recordings. This nonlinear time series analysis technique separates consciousness from unconsciousness during both remifentanil/sevoflurane and remifentanil/propofol anaesthesia with an overall prediction probability of more than 85%, when applied to spontaneous one-channel EEG activity in surgical patients

Opioid-Induced Nausea Involves a Vestibular Problem Preventable by Head-Rest

Background and Aims Opioids are indispensable for pain treatment but may cause serious nausea and vomiting. The mechanism leading to these complications is not clear. We investigated whether an opioid effect on the vestibular system resulting in corrupt head motion sensation is causative and, consequently, whether head-rest prevents nausea. Methods Thirty-six healthy men (26.6 +/- 4.3 years) received an opioid remifentanil infusion (45 min, 0.15 mu g/kg/min). Outcome measures were the vestibulo-ocular reflex (VOR) gain determined by video-head-impulse-testing, and nausea. The first experiment (n = 10) assessed outcome measures at rest and after a series of five 1-Hz forward and backward head-trunk movements during one-time remifentanil administration. The second experiment (n = 10) determined outcome measures on two days in a controlled crossover design: (1) without movement and (2) with a series of five 1-Hz forward and backward head-trunk bends 30 min after remifentanil start. Nausea was psychophysically quantified (scale from 0 to 10). The third controlled crossover experiment (n = 16) assessed nausea (1) without movement and (2) with head movement;isolated head movements consisting of the three axes of rotation (pitch, roll, yaw) were imposed 20 times at a frequency of 1 Hz in a random, unpredictable order of each of the three axes. All movements were applied manually, passively with amplitudes of about +/- 45 degrees. Results The VOR gain decreased during remifentanil administration (p<0.001),averaging 0.92 +/- 0.05 (mean +/- standard deviation) before, 0.60 +/- 0.12 with, and 0.91 +/- 0.05 after infusion. The average half-life of VOR recovery was 5.3 +/- 2.4 min. 32/36 subjects had no nausea at rest (nausea scale 0.00/0.00 median/interquartile range). Head-trunk and isolated head movement triggered nausea in 64% (p<0.01) with no difference between head-trunk and isolated head movements (nausea scale 4.00/7.25 and 1.00/4.5, respectively). Conclusions Remifentanil reversibly decreases VOR gain at a half-life reflecting the drug's pharmacokinetics. We suggest that the decrease in VOR gain leads to a perceptual mismatch of multisensory input with the applied head movement, which results in nausea, and that, consequently, vigorous head movements should be avoided to prevent opioid-induced nausea

Safe Brain Tumor Resection Does not Depend on Surgery Alone - Role of Hemodynamics

Aim of this study was to determine if perioperative hemodynamics have an impact on perioperative infarct volume and patients' prognosis. 201 cases with surgery for a newly diagnosed or recurrent glioblastoma were retrospectively analyzed. Clinical data and perioperative hemodynamic parameters, blood tests and time of surgery were recorded. Postoperative infarct volume was quantitatively assessed by semiautomatic segmentation. Mean diastolic blood pressure (dBP) during surgery (rho -0.239, 95% CI -0.11 - -0.367, p = 0.017), liquid balance (rho 0.236, 95% CI 0.1-0.373, p = 0.017) and mean arterial pressure (MAP) during surgery (rho -0.206, 95% CI -0.07 - -0.34, p = 0.041) showed significant correlation to infarct volume. A rank regression model including also age and recurrent surgery as possible confounders revealed mean intraoperative dBP, liquid balance and length of surgery as independent factors for infarct volume. Univariate survival analysis showed mean intraoperative dBP and MAP as significant prognostic factors, length of surgery also remained as significant prognostic factor in a multivariate model. Perioperative close anesthesiologic monitoring of blood pressure and liquid balance is of high significance during brain tumor surgery and should be performed to prevent or minimize perioperative infarctions and to prolong survival

Low Dose Isoflurane Exerts Opposing Effects on Neuronal Network Excitability in Neocortex and Hippocampus

The anesthetic excitement phase occurring during induction of anesthesia with volatile anesthetics is a well-known phenomenon in clinical practice. However, the physiological mechanisms underlying anesthetic-induced excitation are still unclear. Here we provide evidence from in vitro experiments performed on rat brain slices that the general anesthetic isoflurane at a concentration of about 0.1 mM can enhance neuronal network excitability in the hippocampus, while simultaneously reducing it in the neocortex. In contrast, isoflurane tissue concentrations above 0.3 mM expectedly caused a pronounced reduction in both brain regions. Neuronal network excitability was assessed by combining simultaneous multisite stimulation via a multielectrode array with recording intrinsic optical signals as a measure of neuronal population activity

NEWS AND NOTES 1988, VOL.19, NO.3

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