Stefanie Pilge

Time delay of index calculation: analysis of cerebral state, bispectral, and narcotrend indices.

Background: On the basis of electroencephalographic analysis, several parameters have been proposed as a measure of the hypnotic component of anesthesia. All currently available indices have different time lags to react to a change in the level of anesthesia. The aim of this study was to determine the latency of three frequently used indices: the Cerebral State Index (Danmeter, Odense, Denmark), the Bispectral Index (Aspect Medical Systems Inc., Newton, MA), and the Narcotrend Index (MonitorTechnik, Bad Bramstedt, Germany). Methods: Artificially generated signals were used to produce up to 14 constant index values per monitor that indicate “awake state,” “general anesthesia,” and “deep anesthesia” and smaller steps in between. The authors simulated loss of and return to consciousness by changing between the artificial electroencephalographic signals in a full-step and two stepwise approaches and measured the time necessary to adapt the indices to the particular input signal. Results: Time delays between 14 and 155 s were found for all indices. These delays were not constant. Results were different for decreasing and increasing values and between the full-step and the stepwise approaches. Calculation time depended on the particular starting and target index value. Conclusions: The time delays of the tested indices may limit their value in prevention of recall of intraoperative events. Furthermore, different latencies for decreasing and increasing values may indicate a limitation of these monitors for pharmacodynamic studies.

Electroencephalographic order pattern analysis for the separation of consciousness and unconsciousness: an analysis of approximate entropy, permutation entropy, recurrence rate, and phase coupling of order recurrence plots.

Background:Nonlinear electroencephalographic parameters, e.g., approximate entropy, have been suggested as measures of the hypnotic component of anesthesia. Compared with linear methods, they may detect additional information and quantify the irregularity of a dynamical system. High dimensionality of a signal and disturbances may affect these parameters and change their ability to distinguish consciousness from unconsciousness. Methods of order pattern analysis, in this investigation represented by permutation entropy, recurrence rate, and phase coupling of order recurrence plots, are suitable for any type of time series, whether deterministic or noisy. They may provide a better estimation of the hypnotic component of anesthesia than other nonlinear parameters. Methods:The current analysis is based on electroencephalographic data from two similar clinical studies in adult patients undergoing general anesthesia with sevoflurane or propofol. The study period was from induction until patients followed command after surgery, including a reduction of the hypnotic agent after tracheal intubation until patients followed command. Prediction probability was calculated to assess the parameters ability to separate consciousness from unconsciousness at the transition between both states. Results:Parameters of order pattern analysis provide a prediction probability of maximal 0.85 (training study) and 0.78 (evaluation study) with frequencies from 0 to 30 Hz, and maximal 0.87 (training study) and 0.83 (evaluation study) including frequencies up to 70 Hz, both higher than 0.77 (approximate entropy). Conclusions:Parameters of the nonlinear method order pattern analysis separate consciousness from unconsciousness and are grossly independent of high-frequency components of the electroencephalogram.

A combination of electroencephalogram and auditory evoked potentials separates different levels of anesthesia in volunteers.

BACKGROUND: It has been shown that the combination of electroencephalogram (EEG) and auditory evoked potentials (AEP) allows a good separation of consciousness from unconsciousness. In the present study, we sought a combined EEG/AEP indicator that allows both separation of consciousness from unconsciousness and discrimination among different levels of sedation and hypnosis over a wider range of anesthesia. METHODS: Fifteen unpremedicated volunteers received mono-anesthesia with sevoflurane or propofol in a randomized crossover design in two consecutive sessions. Loss of consciousness (LOC) and EEG burst suppression (BSP) defined end-points from the upper and lower range of general anesthesia. In addition to those two extremes, the difference between anesthetic concentration at BSP and LOC was divided into three equal intervals, resulting in two intermediate levels which divided the concentration from LOC (minimum) to BSP (maximum) into three equal steps. This data set was used to test whether a previously described combined EEG/AEP indicator “detector of consciousness” can also discriminate among degrees of anesthetic effects from the awake state to BSP. Furthermore, a new improved combined EEG/AEP indicator was developed on the basis of the data from the current study, and subsequently this new indicator was tested for its ability to separate consciousness from unconsciousness with the patient data set. RESULTS: The former “detector of consciousness” showed a prediction probability (PK) of 0.77 to separate different levels of anesthesia from the current study, whereas for the new combined EEG/AEP indicator, PK was 0.94. The new indicator was further applied to the previous study and achieved a PK of 0.89. CONCLUSIONS: These results show that with the new indicator presented here, a combination of EEG and AEP parameters can be used to differentiate degrees of anesthetic effects over a wide range of hypnosis, from the conscious state to deep anesthesia (i.e., BSP).

Time delay of monitors of the hypnotic component of anesthesia: analysis of state entropy and index of consciousness.

Monitors evaluating the hypnotic component of anesthesia by analyzing the electroencephalogram (EEG) may help to decrease the incidence of intraoperative awareness with recall. To calculate an index representing the anesthetic level, these monitors have different time delays until the correct index is displayed. In previous studies, intraoperatively recorded real and simulated EEG signals were used to determine time delays of cerebral state and Narcotrend and Bispectral indices. In the present study, we determined time delays of state entropy and index of consciousness. For this purpose, recorded real and simulated EEG sequences representing different anesthetic levels were played back to the tested monitors.Simulated and real perioperatively recorded EEG signals indicating stable states “awake,” “general anesthesia,” and “cortical suppression” were used to evaluate the time delays. Time delays were measured when switching from one state to another and were defined as the required time span of the monitor to reach the stable target index. Comparable results were obtained using simulated and real EEG sequences. Time delays were not constant and ranged from 18 to 152 seconds. They were also different for increasing and decreasing values. Time delays were dependent on starting and target index values. Time delays of index calculation may limit the investigated monitors ability to prevent interoperative awareness with recall. Different time delays for increasing and decreasing transitions could be a problem if the monitors are used for pharmacodynamic studies.

Sevoflurane-induced Epileptiform Electroencephalographic Activity and Generalized Tonic-Clonic Seizures in a Volunteer Study.

447 August 2013 A FTER approval of ethics committee, a healthy, unpremedicated man (21 yr) without history of seizures took part in a volunteer study1 of the effects of anesthesia on electroencephalogram (figs. A–C). During sevoflurane induction, the patient showed convulsions without epileptogenic electroencephalographic activity (fig. A, corresponding endtidal gas concentration 1.24 vol%; effect-site concentration 1.01 for Keo T1/2 of 150 s). At further increase of end-tidal gas concentration to 4.15 vol% (effect-site concentration 3.96), a generalized tonic–clonic seizure was observed as defined by clin ical diagnosis and electroencephalographic pattern (fig. B). It ceased after 35 s when sevoflurane was stopped and propofol was given as rescue medication. Consecutively, the transition from seizure activity to suppressed electroencephalographic activity was observed (fig. C; end-tidal gas concentration 3.08 vol%; effect-site concentration 3.94). The patient recovered completely. Further examinations remained without findings. Convulsions during sevoflurane anesthesia have been reported with an incidence of 5%.2 Subclinical electroencephalographic activity during induction of anesthesia with sevoflurane has been demonstrated in 20% of children2 and in 47% of adults3 with spontaneous breathing, increasing with controlled hyperventilation and hypocapnia to 88% and 100%, respectively. Our case represents the first documentation of a generalized tonic–clonic seizure in a nonepileptic patient triggered by sevoflurane with ongoing recording of confirming electroencephalographic pattern. Indices derived from processed electroencephalogram may show aberrant values during seizure activity, mostly indicating falsely high index values because of high-frequency epileptiform activity. Therefore, it is crucial for anesthesiologists to directly assess the raw signal when electroencephalogram-based monitors are used.

Transcranial motor evoked potentials during anesthesia with desflurane versus propofol--A prospective randomized trial.

OBJECTIVE This study aimed to evaluate differences in transcranial electrical motor evoked potential (tcMEP) amplitudes between desflurane/remifentanil and propofol/remifentanil anesthesia treatment plans in patients without preexisting motor deficits (PMDs) undergoing carotid endarterectomy (CEA). METHODS This prospective trial included 21 patients who were randomly assigned to an effect group (Group(DESFLURANE); n=14) or a control group (Group(STANDARD-PROPOFOL); n=7). tcMEP amplitudes were measured 35 min post-induction (T1) either with desflurane or propofol. Treatment was then changed to propofol in Group(DESFLURANE). After an additional 35 min, the tcMEP amplitudes were reevaluated (T2). Differences in amplitudes (DW) between T1 and T2 were calculated for each patient, and the means of these differences were compared between groups. RESULTS tcMEPs were recorded in all 21 patients. At T1, the mean amplitude was 840.1 (SD 50.3) μV and 358.9 (SD 74) μV for Group(STANDARD-PROPOFOL) and Group(DESFLURANE), respectively. The absolute mean difference (T1-T2) between groups was -496.75 μV (p=0.0006). CONCLUSION Desflurane reduces the tcMEP amplitude significantly more than propofol in patients without PMDs undergoing CEA. SIGNIFICANCE TcMEPs were recorded in all patients regardless of the anesthesia regimen. In patients with initially small amplitudes, desflurane may limit tcMEP recording because it produces a remarkable amplitude reduction, even in patients without PMDs.

Construction of the Electroencephalogram Player: A Device to Present Electroencephalogram Data to Electroencephalogram-Based Anesthesia Monitors

BACKGROUND:Recently, an increasing number of electroencephalogram (EEG)-based monitors of the hypnotic component of anesthesia has become available. Most of these monitors calculate a numerical index reflecting the hypnotic component of anesthesia. Most of the underlying algorithms are proprietary. Therefore, a quality check or comparison of different indices is very complex. METHODS:Because there is limited information about the algorithms used for index calculation of the different monitors, a reliable comparison or test of the monitors is possible only if the same set of EEG data are presented to each monitor. RESULTS:Parallel EEG monitoring during surgery is limited to two or three monitors because the space for electrode placement on the head is limited. This problem can be solved by using the EEG player to play back recorded EEG data to different monitors. CONCLUSIONS:The output of the player corresponds to the original EEG signal. A comparison of different indices based on identical EEGs is therefore possible. The index reproducibility can also be checked, if the same signal is presented to different monitors.

Intraoperative multimodal evoked potential monitoring during carotid endarterectomy: a retrospective study of 264 patients.

BACKGROUND:Methods for detecting intraoperative cerebral ischemia arising from internal carotid artery (ICA) cross-clamping during carotid endarterectomy (CEA) should be sensitive, specific, and rapid to prevent intraoperative stroke. We had 3 objectives pertaining to this: (1) investigation of the rates of success of multimodal evoked potential (mEP) monitoring using a combination of median nerve (m) somatosensory (SS) EPs, tibial nerve SSEPs (tSSEPs), and transcranial electrical stimulated motor EPs (tcMEPs); (2) evaluation of the rates of false-negative mEP results; and (3) analysis of the relationship between different time periods associated with ICA cross-clamping and the postoperative outcome of motor function in patients with significant changes in mEP monitoring. METHODS:Two hundred sixty-four patients undergoing CEA using general anesthesia with monitoring of bilateral mSSEPs, tSSEPs, and tcMEPs were retrospectively reviewed between 2009 and 2012. The rates of successful assessment of mEPs were investigated, and the rate of false-negative mEP results was analyzed. Different time periods (T1—time of clamping, T2—clamping to significant mEP changes, T3—significant mEP change to intervention, and T4—intervention to recovery of EP) were tested using Welch t test for significant association with postoperative motor deficit. RESULTS:(1) Multimodal EP monitoring was achieved in 241 patients (91.3%, point estimate [PE] 0.91, confidence interval [CI] 0.87 to 0.94), whereas none of the modalities were recordable in one case (PE 0.0038, CI 0.0002 to 0.019). Additionally, tSSEP was not recordable in 21 patients (PE 0.08, CI 0.05 to 0.12), and we found one case of isolated failure of tcMEP recording (PE 0.0038, CI 0.0002 to 0.019). (2) False-negative mEP results were found in 1 patient (0.4%; PE 0.0038, CI 0.0002 to 0.019). Significant mEP changes occurred in 32 patients (12.1%), and thus, arterioarterial shunt was performed in 17 (6.4%) patients. Eleven patients (4.2%) showed transient and 1 showed permanent postoperative motor deficit. (3) There was no significant difference regarding any of the time periods associated with ICA cross-clamping and postoperative alteration of motor function (T1: P = 0.19, CI −30.1 to 6.8 minutes; T2: P = 0.38, CI −23 to 9.5 minutes; T3: P = 0.25, −9.7 to 2.8 minutes; T4: P = 0.42, CI to −15.5 to 7.0 minutes). CONCLUSIONS:Multimodal EP monitoring is applicable during CEA. The 0.4% false-negative rate suggests an advantage of mEP monitoring when compared with isolated mSSEP monitoring. Our data suggest that periods of time during cross-clamping were not significantly associated with postoperative motor deficit. However, the small number of patients limits the conclusiveness of these findings. mEP monitoring could not prevent a postoperative motor deficit in all patients, but our results suggest that it is a useful adjunct to mSSEP monitoring.

Does the cerebral state index separate consciousness from unconsciousness

BACKGROUND: The Cerebral State Monitor™ (CSM) is an electroencephalogram (EEG)-based monitor that is claimed to measure the depth of hypnosis during general anesthesia. We calculated the prediction probability (P K) for its ability to separate consciousness from unconsciousness in surgical patients with different anesthetic regimens. METHODS: Digitized EEG recordings of a previous study of 40 nonpremedicated, adult patients undergoing elective surgery under general anesthesia were replayed using an EEG player and reanalyzed using the CSM. Patients were randomly assigned to receive either sevoflurane-remifentanil or propofol-remifentanil. The study design included a slow induction of anesthesia and an episode of intended wakefulness. CSM values at loss and return of consciousness were compared. P K was calculated from values 30 seconds before and 30 seconds after loss and return of consciousness. RESULTS: The P K for the differentiation between consciousness and unconsciousness was 0.75 ± 0.03 (mean ± SE). For sevoflurane-remifentanil, P K was 0.71 ± 0.04. For propofol-remifentanil, P K was 0.81 ± 0.03. CONCLUSIONS: The ability of CSM for separation of consciousness and unconsciousness was comparable to other commercially available EEG-based indices.

Differences between state entropy and bispectral index during analysis of identical electroencephalogram signals: a comparison with two randomised anaesthetic techniques.

BACKGROUND It is claimed that bispectral index (BIS) and state entropy reflect an identical clinical spectrum, the hypnotic component of anaesthesia. So far, it is not known to what extent different devices display similar index values while processing identical electroencephalogram (EEG) signals. OBJECTIVE To compare BIS and state entropy during analysis of identical EEG data. Inspection of raw EEG input to detect potential causes of erroneous index calculation. DESIGN Offline re-analysis of EEG data from a randomised, single-centre controlled trial using the Entropy Module and an Aspect A-2000 monitor. SETTING Klinikum rechts der Isar, Technische Universität München, Munich. PATIENTS Forty adult patients undergoing elective surgery under general anaesthesia. INTERVENTIONS Blocked randomisation of 20 patients per anaesthetic group (sevoflurane/remifentanil or propofol/remifentanil). Isolated forearm technique for differentiation between consciousness and unconsciousness. MAIN OUTCOME MEASURES Prediction probability (PK) of state entropy to discriminate consciousness from unconsciousness. Correlation and agreement between state entropy and BIS from deep to light hypnosis. Analysis of raw EEG compared with index values that are in conflict with clinical examination, with frequency measures (frequency bands/Spectral Edge Frequency 95) and visual inspection for physiological EEG patterns (e.g. beta or delta arousal), pathophysiological features such as high-frequency signals (electromyogram/high-frequency EEG or eye fluttering/saccades), different types of electro-oculogram or epileptiform EEG and technical artefacts. RESULTS PK of state entropy was 0.80 and of BIS 0.84; correlation coefficient of state entropy with BIS 0.78. Nine percent BIS and 14% state entropy values disagreed with clinical examination. Highest incidence of disagreement occurred after state transitions, in particular for state entropy after loss of consciousness during sevoflurane anaesthesia. EEG sequences which led to false ‘conscious’ index values often showed high-frequency signals and eye blinks. High-frequency EEG/electromyogram signals were pooled because a separation into EEG and fast electro-oculogram, for example eye fluttering or saccades, on the basis of a single EEG channel may not be very reliable. These signals led to higher Spectral Edge Frequency 95 and ratio of relative beta and gamma band power than EEG signals, indicating adequate unconscious classification. The frequency of other artefacts that were assignable, for example technical artefacts, movement artefacts, was negligible and they were excluded from analysis. CONCLUSION High-frequency signals and eye blinks may account for index values that falsely indicate consciousness. Compared with BIS, state entropy showed more false classifications of the clinical state at transition between consciousness and unconsciousness.