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Background: A high dose of opioids associated with a low dose of propofol has become a popular anesthetic technique. However, the influence of opioids on the electroencephalographic phenomenon related to induction of anesthesia and, thereby, on the quantitative parameters used in the depth-of-anesthesia estimation is not well known., Methods: Twenty-seven patients were divided into three groups to receive saline, low-dose remifentanil (7.5 microg x kg x h) or high-dose remifentanil (30 microg x kg x h) during induction of anesthesia with propofol (30 mg . kg . h). Electroencephalogram was recorded from Fz electrode, and its time-frequency properties in the patient groups were analyzed from the induction of anesthesia to the occurrence of burst suppression pattern. The group differences in 14 quantitative spectral parameters used in the depth-of-anesthesia estimation were examined as well., Results: The time-frequency properties of electroencephalogram were different between groups. The high-frequency (greater than 14 Hz) activity during light anesthesia was decreased in remifentanil groups; whereas, increased activity in extended alpha band (7-14 Hz) and decreased activity in delta band (0.5-4 Hz) was observed during deep anesthesia. This resulted in statistically significant changes in all 14 quantitative parameters., Conclusions: The effect of remifentanil on the spectrum and quantitative parameters of electroencephalogram is significant and strongly dependent on the level of anesthesia. Coadministration of opioids therefore challenges the reliability of the spectral properties of electroencephalogram in the depth-of-anesthesia estimation by using a frontal montage. Furthermore, the finding has implications for design of opioid coadministration studies.

Functional magnetic resonance imaging (fMRI) induces coarse electromagnetic artifacts into the simultaneously recorded electroencephalogram (EEG). The problem in the signal processing framework is to model the underlying artifact, which is time-continuous, as a discretely sampled waveform. To build up an artifact template, the EEG sampling in relation to the phase of the imaging artifacts should be known. If the MR scanner and EEG sampling are not synchronized, this relation is not constant and a time adjustment of the template with the individual slice artifacts becomes essential. However, lack of synchrony opens up the possibility for approximating a high-precision and continuous artifact template by using the samples acquired from slightly different phases of the induced artifact. In this work, methodology for reconstructing such a template was developed using EEG data recorded simultaneously with fMRI at 3 T. A time-continuous cubic spline approximation was used as the slice artifact model. To overcome the problem of non-synchronized clocks, two methods were proposed to find the starting times of the slice artifacts at sub-sample precision. This approach yielded efficient imaging artifact reduction: the amplitude at the dominant frequency was attenuated by 55-70 dB (the median values over EEG channels) and the residual signal, at its best, was practically free from sharp transients even with 5000 Hz sampling frequency and without further residual artifact reduction algorithms. The presented methods may reduce the need for post-processing of the residual signal after the template subtraction and may help to preserve the EEG bandwidth.

Nonlinear electroencephalographic entropy parameters have been proposed for the assessment of depth of anesthesia. The influence of remifentanil, a commonly used intraoperative opioid, on these parameters, namely approximate entropy (ApEn), sample entropy (SampEn), and permutation entropy (PeEn), during induction of propofol anesthesia was studied. Remifentanil was shown to reduce the propofol-induced changes in ApEn and SampEn throughout the transition from awake to burst suppression state. Coadministration of opioids therefore challenges the reliability of these parameters as indicators of depth of anesthesia. No consistent influence on PeEn was observed. However, this may have been due to strong interindividual variation in PeEn values.

A method for detailed description of the time-frequency characteristics of electroencephalogram during induction of anesthesia is proposed. The method, based on averaging of time-normalized smoothed pseudo-Wigner-Ville distributions, is applied to data recorded from nine patients undergoing propofol anesthesia. An extensive representation of the frequency progression pattern related to the induction of anesthesia is given and the time-frequency characteristics that are consistent/not consistent between patients are determined. It is also illustrated how four different clinical end-points, generally used in the assessment of the depth of anesthesia, can be related to different phases of the frequency progression pattern. The method presented has importance in providing information about the neurophysiological phenomenon during induction of anesthesia and can therefore be used in the development of new monitoring algorithms.

Background: Depth-of-anesthesia monitoring with the electroencephalogram has become widely used in anesthesia practice. Generally, the methods presented are based on the spectral changes of the electroencephalogram. In this study, the authors evaluate the influence of remifentanil on the relation of timely occurrence of clinical endpoints and the spectral behavior of the electroencephalogram., Methods: Twenty-seven patients scheduled to undergo a surgical procedure were randomly assigned to three groups. Patients blindly received equal volumes of saline or remifentanil (7.5 or 30 microg.kg.h) 1 min before induction of anesthesia with infusion of propofol (30 mg.kg.h). The occurrence of loss of counting, loss of obeying verbal command, and loss of reaction to tetanic stimulation was assessed. The electroencephalogram was recorded from electrode Fz referenced to the common average, and an iterative algorithm was applied to solve the underlying frequency progression pattern. The positions of the clinical endpoints on the pattern were analyzed., Results: The administration of remifentanil during induction of anesthesia with propofol led to an earlier occurrence of the clinical endpoints on the frequency progression pattern. A significant difference (P < 0.05) was observed between the saline and high-dose patient groups in all three endpoints. The effect of remifentanil was proportional to the infusion rate., Conclusions: The infusion of remifentanil during propofol anesthesia significantly modifies the mutual relations of the electroencephalographic spectral characteristics and the endpoints in a predictable and quantifiable manner. This finding suggests that the electroencephalographic phenomena and the endpoints may not be identical but rather to some extent separate manifestations of hypnotic drug effect.

The removal of ballistocardiogram (BCG) artifact from the EEG recorded in the MRI scanner is challenging. Few studies have utilized independent component analysis (ICA) in this task. A drawback of ICA has been the proper selection of the BCG related components. The key idea in this work is to use the difference between the power spectrum of the artifact-processed data and the spectrum of data recorded outside the scanner as a cost function in the selection of the BCG related independent components. Forward floating selection algorithm was implemented to find the components to minimize this criterion. Also, the typical component selection criteria based on the correlation with electrocardiogram (ECG) signal and on explained variance were compared in this respect. The correlation criterion was least successful leaving considerable residual artifact in the signal. With the first few removed components the variance criterion performed as well as the minimum spectral difference criterion. With the variance criterion alone, however, the number of the components to be removed cannot be determined. The suggested methods may provide objective means to validate residual artifact or the possible loss of physiological signal due to artifact removal and to help selecting the proper artifact-related components.

This paper studies how remifentanil, a commonly used intraoperative opioid, affects the relation of the onset of burst suppression pattern (BSP) and the spectral changes of EEG during anesthesia. The onsets of BSP were detected using both manual and the automatic method proposed from the EEGs of twenty-seven patients who had received different amount of remifentanil with the anesthetic. The spectral changes were determined by calculating the frequency progression patterns of the EEGs. The results showed that remifentanil significantly affects the relation of EEG spectral changes and the onset of BSP. The finding is important since the current EEG-based assessment of the depth of anesthesia basically relies on the analysis of the spectral features and BSP.

The anesthetic infusion with propofol influences EEG activity rather smoothly by changing the amplitude activity in different frequency bands. This results in a frequency progression pattern (FPP) which can be related to the depth of anesthesia. An iterative algorithm is proposed for the estimation of the shape of this pattern. The presented method is applied to the data recorded from the start of the propofol anesthetic infusion to the onset of the burst suppression pattern (BSP) with nine patients. The results reveal the underlying FPP and how the onset of the BSP is related to it. The proposed method offers potential for the development of automatic assessment systems for the depth of anesthesia.

The objective of this study is to model the association between the electroencephalogram (EEG) spectral features and the novel r scale representing the sedative effects of the propofol anesthetic drug. On the basis of the r scale, the unresponsiveness to the verbal command (LVC) is forecasted. EEG recordings are taken from a 16-patient study population undergoing propofol anesthetic induction. EEG was filtered into consecutive 4-Hz passbands up to 28 Hz. Of these time-series, the amplitude envelopes were extracted and used as input features to the first and the second-order polynomial multiple linear regression models. The values r epsilon [0.4, 1] were predicted with the R2 value of 0.775 with a cross validation. The LVC times were forecasted with the median error of 5%-7% or equivalently 10-13 s. In contrast, using the median of the measured LVC times of the training population as a forecast, the corresponding error was 12% or 26 s. The results suggest an acceptable correlation between the r scale and the EEG spectrum in the studied range. Moreover, the r values of an individual can be predicted using a population model. The suggested framework enables forecasting the LVC, which may open new possibilities for steering the drug administration.

The ability to monitor the physiological effects of sedative medication accurately is of interest in clinical practice. During the anesthetic agent driven transition to unresponsiveness, nonstationary changes such as signal amplitude variations appear in electroencephalography. In this paper, it is studied whether the application of the approximate entropy (ApEn) method to electroencephalographic (EEG) signal produces a monotonic response curve during the transition from awareness to unresponsiveness. Data from fourteen patients, undergoing propofol anesthetic induction were studied. To optimize the ApEn performance, different parameter choices were carefully evaluated. It was assumed with our protocol, that the level of anesthesia changes monotonically with the elapsed induction time. The monotonicity of the ApEn change was assessed with the prediction probability statistic (PK). The monotonicity of the ApEn time-series depends on the parameters employed in the algorithm and the varying signal amplitude. Depending on the parameter values, the median PK value ranged from 0.886 to 0.527. Thus, a good directionality and concordance was observed, but the nonstationarity of the signal affected the results. In conclusion, EEG-based ApEn measure shows a nonlinear response during propofol induction. With a judicious choice of parameters, a monotonic response is confirmed using PK statistic.

Objective: Our purpose is to find out whether the Loss of obeying a Verbal Command (LVC) and the preceding progression of the EEG frequency patterns are mutually related during an anesthetic induction., Methods: EEG was analyzed from sixteen patients, anesthetized with a fixed rate infusion of propofol (30 mg/kg/h). An artifact-purified EEG from electrode Fz referenced to the average of mastoid signals was filtered to consecutive 4 Hz frequency passbands with a filter bank. Signal envelope time-series were computed for all the passbands and studied as a function of the elapsed induction time t and as a function of the relative time r, which is t divided by the time of the LVC., Results: A frequency band specific biphasic activity pattern progressed from high towards low frequencies systematically and uniformly in the population studied when presented on the relative time scale r, irrespective of individual responses to the dosage., Conclusions: The grouping of the individual progression patterns on the r scale indicates a strong relation between the EEG spectral behavior and the LVC., Significance: EEG may provide phenomenological grounds for a continuous control variable of a sedative drug effect, even in between the discrete clinical end-points.

Background: During EEG suppression with isoflurane or sevoflurane anaesthesia, median nerve stimulation causes cortical responses of two kinds: an N20 wave with a latency of 20 ms and an EEG burst with a latency of 200 ms. We tested the possibility that median nerve stimulation during EEG suppression with propofol would cause an EEG response that was consistent enough to be of use for neuromonitoring., Methods: Eight patients were anaesthetized with propofol to allow burst suppression. Electrical stimulation of the median nerve was applied during general anaesthesia and the EEG was measured., Results: The EEG response to a painful stimulus had four successive components: (i) N20 and P22 potentials, reflecting activation of fast somatosensory pathways; (ii) a high-amplitude negative wave, possibly reflecting activation of the somatosensory cortex SII bilaterally; (iii) a burst (i.e. a negative slow wave with superimposed 10 Hz activity, probably reflecting an arousal mechanism); and (iv) a 13-15 Hz spindle, probably originating from the thalamus, similar to sleep spindles. These could be seen separately and in different combinations. Bursts and spindles during burst suppression were also seen without stimulation. In deepening propofol anaesthesia, spindles were seen in the continuous EEG before burst suppression was achieved. In deep anaesthesia, spindles were seen when bursts had ceased, and painful stimuli evoked sharp waves without subsequent bursts., Conclusion: In addition to SSEP (somatosensory evoked potentials), three different evoked responses are noted that could be useful for clinical monitoring.

Objective: We studied the spectral characteristics of the EEG burst suppression patterns (BSP) of two intravenous anesthetics, propofol and thiopental. Based on the obtained results, we developed a method for automatic segmentation, classification and compact presentation of burst suppression patterns., Methods: The spectral analysis was performed with the short time Fourier transform and with autoregressive modeling to provide information of frequency contents of bursts. This information was used when designing appropriate filters for segmentation algorithms. The adaptive segmentation was carried out using two different nonparametric methods. The first one was based on the absolute values of amplitudes and is referred to as the ADIF method. The second method used the absolute values of the Nonlinear Energy Operator (NLEO) and is referred to as the NLEO method. Both methods have been described earlier but they were modified for the purposes of BSP detection. The signal was classified to bursts, suppressions and artifacts. Automatic classification was compared with manual classification. Results. The NLEO method was more accurate, especially in the detection of artifacts. NLEO method classified correctly 94.0% of the propofol data and 92.8% of the thiopental data. With the ADIF method, the results were 90.5% and 88.1% respectively., Conclusions: Our results show that burst suppression caused by the different anesthetics can be reliably detected with our segmentation and classification methods. The analysis of normal and pathological EEG, however, should include information of the anesthetic used. Knowledge of the normal variation of the EEG is necessary in order to detect the abnormal BSP of, for instance, seizure patients.

Objective: Phase coupling between EEG channel pairs in various frequency bands was evaluated during propofol anesthetic induction and recovery periods., Methods: Twenty-three patients participated in the study. Phase synchronization indices based on the Hilbert transform were investigated on frequency bands 0.05-1 Hz, 1-4 Hz, 4-8 Hz, 8-12 Hz and 12-16 Hz for all pairs of the 9 EEG channels covering midline and frontal areas. A straight line was used to approximate the index values as a function of time and the Sign Test statistics were applied to the slope parameters., Results: Systematic phase synchronization changes were detected. Generally, phase synchronization in the sub-delta band decreased during the induction and increased during the recovery, while the directions were reversed in the alpha band. The changes were dependent on the channel pair. In the delta, theta and beta bands, the changes were aligned more irregularly than in the sub-delta or in the alpha bands. Highly asymmetric behavior between the induction and the recovery periods was also observed in these bands., Conclusions: Induction and recovery from propofol anesthesia changes the phase synchronization between the EEG channels. The passband and location-specific behavior of these changes reveals the effects of the anesthetic to the different neural mechanisms.

The quantitative EEG (QEEG), regional cerebral blood flow (rCBF) and circulation time of 17 patients were examined semisimultaneously thrice during the first 3 months after a cerebral supratentorial infarction. The EEG was quantified according to normalized slope descriptor technique in nine patients and by means of a combined period and amplitude analysis in eight patients. Intravenously injected isotopes 133Xenon and 99TcmO4 were used for blood flow and circulation time measurements. The QEEG-values improved during the whole follow-up period. Cerebral blood flow stayed low for all 3 months and did not alter during this period, while initially prolonged circulation time to some extent improved within 2 weeks remaining, however, prolonged even thereafter. A tendency for a positive correlation between QEEG and rCBF values in the infarcted hemisphere could be seen.

Discusses the abstract of the article "Evoked EEG Burst During Anaesthesia, a New Tool for Cognitive Anesthesiology," by Eila Sonkajavari submitted at the 27th Congress of The Scandinavian Society of Anesthesiology and Intensive Care Medicine.