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

Permutation Entropy: Too Complex a Measure for EEG Time Series?

Permutation entropy (PeEn) is a complexity measure that originated from dynamical systems theory. Specifically engineered to be robustly applicable to real-world data, the quantity has since been utilised for a multitude of time series analysis tasks. In electroencephalogram (EEG) analysis, value changes of PeEn correlate with clinical observations, among them the onset of epileptic seizures or the loss of consciousness induced by anaesthetic agents. Regarding this field of application, the present work suggests a relation between PeEn-based complexity estimation and spectral methods of EEG analysis: for ordinal patterns of three consecutive samples, the PeEn of an epoch of EEG appears to approximate the centroid of its weighted power spectrum. To substantiate this proposition, a systematic approach based on redundancy reduction is introduced and applied to sleep and epileptic seizure EEG. The interrelation demonstrated may aid the interpretation of PeEn in EEG, and may increase its comparability with other techniques of EEG analysis

A period of immobility after remifentanil administration protects from nausea: an experimental randomized cross-over study

Background: The opioid remifentanil induces a decrease of vestibulo-ocular reflex function, which has been associated with nausea and vomiting when the subjects are moved. The study investigates in healthy female volunteers if immobility after remifentanil administration protects from nausea and vomiting. Methods: In volunteers, a standardized movement intervention (a manually applied head-trunk movement forward, backward and sideward) was started 5 min (session A), 35 min (session B) or 60 min (session C) after cessation of a remifentanil infusion (0.15 mu g.kg(-1).min(-1)). In a cross-over design, 16 participants were randomized to the early (sessions A and B) or the late intervention group (sessions A and C). Nausea was assessed using a 11-point numerical rating scale before and after each movement intervention. Differences within and between groups were assessed with non-parametric tests for paired and unpaired data. Results: Comparing sessions A, B and C, intensity of nausea was time-dependent after cessation of remifentanil administration (p = 0.015). In the early intervention group, nausea decreased from median 5.0 [IQR 1.5;6.0] in session A to 2.0 [1.0;3.0] in session B (p = 0.094);in the late intervention group nausea decreased from 3.5 [2.0;5.0] in session A to 0.5 [0.0;2.0] in session C (p = 0.031). Conclusions: In summary, in young healthy women, immobility after remifentanil administration protects from nausea and vomiting in a time-dependent manner. In analogy to motion sickness, opioid-induced nausea and vomiting in female volunteers can be triggered by movement

Fronto-parietal connectivity is a non-static phenomenon with characteristic changes during unconsciousness.

BACKGROUND: It has been previously shown that loss of consciousness is associated with a breakdown of dominating fronto-parietal feedback connectivity as assessed by electroencephalogram (EEG) recordings. Structure and strength of network connectivity may change over time. Aim of the current study is to investigate cortico-cortical connectivity at different time intervals during consciousness and unconsciousness. For this purpose, EEG symbolic transfer entropy (STEn) was calculated to indicate cortico-cortical information transfer at different transfer times. METHODS: The study was performed in 15 male volunteers. 29-channel EEG was recorded during consciousness and propofol-induced unconsciousness. EEG data were analyzed by STEn, which quantifies intensity and directionality of the mutual information flow between two EEG channels. STEn was computed over fronto-parietal channel pair combinations (10 s length, 0.5-45 Hz total bandwidth) to analyze changes of intercortical directional connectivity. Feedback (fronto → parietal) and feedforward (parieto → frontal) connectivity was calculated for transfer times from 25 ms to 250 ms in 5 ms steps. Transfer times leading to maximum directed interaction were identified to detect changes of cortical information transfer (directional connectivity) induced by unconsciousness (p<0.05). RESULTS: The current analyses show that fronto-parietal connectivity is a non-static phenomenon. Maximum detected interaction occurs at decreased transfer times during propofol-induced unconsciousness (feedback interaction: 60 ms to 40 ms, p = 0.002; feedforward interaction: 65 ms to 45 ms, p = 0.001). Strength of maximum feedback interaction decreases during unconsciousness (p = 0.026), while no effect of propofol was observed on feedforward interaction. During both consciousness and unconsciousness, intensity of fronto-parietal interaction fluctuates with increasing transfer times. CONCLUSION: Non-stationarity of directional connectivity may play a functional role for cortical network communication as it shows characteristic changes during propofol-induced unconsciousness