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Can Electroencephalographic Analysis Be Used to Determine Sedation Levels in Critically Ill Patients?

Roustan, Jean-Paul, MD; Valette, Sarah, MD; Aubas, Pierre, MD; Rondouin, Gérard, MD, PhD; Capdevila, Xavier, MD, PhD

doi: 10.1213/01.ane.0000167782.47957.e1
Critical Care and Trauma: Research Report

Prolonged use of sedative drugs frequently leads to oversedation of intensive care patients. Clinical assessment scales are not reliable in deeply sedated patients. Parameters obtained from spectral and bispectral analysis of electroencephalogram (EEG) records have been combined to create an index (BIS®) to monitor anesthesia depth. The role of such parameters in monitoring the depth of the sedation in intensive care unit (ICU) patients has yet to be determined. We designed the present prospective study to redefine and calculate available spectral and bispectral parameters from raw EEG records and estimate their clinical relevance for the diagnosis of under- or oversedation levels in ICU patients. Forty adult patients receiving continuous midazolam and morphine sedation were included. We obtained 167 clinical evaluations of sedation level using Ramsay and COMFORT scales along with an EEG record of 300 s. Six spectral parameters—relative power of 4 frequency bands (β, α, Θ, and δ), 95th percentile of the power spectrum (SEF95), and 50th percentile of the power spectrum (SEF50) and four bispectral parameters, real triple product, bispectrum (Bispectrum), bicoherence, and ratio 10—were calculated. The relevance of each of these parameters and combinations in predicting too light (Ramsay 1 and 2) or deep (Ramsay 5 and 6) sedation levels was assessed. These calculations were performed before and after exclusion of the agitated patients, whose COMFORT 4 score was above 2. The most relevant parameters for predicting levels of deep sedation (Ramsay 5 and 6) were ratio 10 (area under the curve = 0.763; 95% confidence interval, 0.679–0.833) and SEF95 (area under the curve = 0.687; 95% confidence interval, 0.597–0.767). The most relevant parameters for predicting light levels of sedation (Ramsay 1 and 2) were also ratio 10 (area under the curve = 0.829; 95% confidence interval, 0.695–0.917) and SEF95 (area under the curve = 0.798; 95% confidence interval, 0.650—0.898). There is a modest improvement in relevance of their linear combination in predicting sedation level. Results were similar after exclusion of agitated patients. We conclude that various calculated EEG descriptive parameters exhibited large interindividual variability. There was a strong correlation between EEG spectral and bispectral parameters. Bispectral analysis slightly improves the predictive power of simple spectral analysis in distinguishing too light or deep sedation levels in ICU patients.

IMPLICATIONS: Spectral edge frequency 95 and Ratio 10 are the most relevant electroencephalogram (EEG) indexes for monitoring the level of sedation in intensive care unit patients but calculated EEG values exhibited large interindividual variability. Bispectral analysis of EEG provides a slight improvement over simple spectral analysis.

Department of Anesthesiology and Intensive Care Medicine, Neurologic Explorations Laboratory and Department of Biostatistics, Lapeyronie University hospital, Montpellier, France

Accepted for publication April 7, 2005. Presented, in part, at the SFAR annual meeting, Paris, September 15, 2003.

Supported by CHU de Montpellier, Clinical Research Office and from la Fondation pour la Recherche Clinique, Languedoc Roussillon.

Address correspondence and reprint requests to Xavier Capdevila, MD, Department of Anesthesiology and Intensive Care Medicine, Lapeyronie University Hospital, 295 Avenue du Doyen G Giraud, 34000 Montpellier, France. Address e-mail to

Sedation is used in more than 50% of patients of intensive care units (ICU) in Europe (1) and in up to 90% in the United States (2). This practice is used more broadly in traumatic and postoperative ICU than in medical ICU (3). The sedation protocol typically includes the association of a hypnotic drug (midazolam or propofol) and continuous IV administration of an opioid (morphine, fentanyl, or sufentanil) (4). In the absence of monitoring of the level of sedation, the dosage of these drugs is in most cases determined empirically, as needed, by the nursing staff, exposing the patient to potential overdosing, which can prolong mechanical ventilation and duration of stay in the ICU (5). Consequently, monitoring the level of sedation is indispensable (6). This monitoring calls for subjective clinical methods based on various scales. Unfortunately, these scoring systems require stimulation of the patient and they are ineffective in patients receiving neuromuscular blockers or in deeply sedated patients who are unresponsive to external stimulation. In this context, physicians turn to objective instrumental methods, primary among which is the electroencephalogram (EEG).

The EEG is a simple, noninvasive technique, but the interpretation of EEG signal is complex. The advent of digital recording devices and the development of effective mathematical algorithms for signal processing has led to automated EEG interpretation. Fourier transformation is used to calculate the power spectrum with its various descriptive parameters: relative power of frequency bands (β, α, θ, δ), 95% spectral edge frequency (SEF95), and 50% spectral edge frequency (SEF50) (7). Administration of anesthetics results in an offset of the power of the signal toward low frequencies (bands θ and δ). The power spectrum can thus be used to monitor this change in the state of the central nervous system during anesthesia or sedation (8). Unfortunately, large interindividual variability and, depending on the anesthetic drugs, the variability of spectral parameters have limited widespread clinical use of the techniques of monitoring based on spectral analysis of the EEG (9). More recently, the bispectral index (BIS®; Aspect Medical Systems, Natick, MA) has been introduced. This involves a nondimensional index obtained by combination of three EEG subparameters: a subparameter quantifying the degree of burst suppression, a subparameter derived from the power spectrum, and a subparameter derived from bispectral analysis of the EEG. The originality of the BIS® lies in this third subparameter, the calculation of which calls for complex, high-order statistics. The determination of the equation (proprietary algorithm), which combines these subparameters to give the BIS®, was achieved statistically from an extensive bank of EEG records collected from patients subjected to various types of anesthesia. A multivariate regression model was used to establish the relative contribution of each subparameter providing the best correlation between the BIS calculated in this manner and the clinical level of sedation of these patients (9,10). Unfortunately, the BIS®, initially intended for monitoring in the operating room proved to have little usefulness for monitoring the sedation of ICU patients because of a large interindividual variability of BIS® and interference between the BIS® and the electromyogram (EMG) signal (11–16).

In the present study, we prospectively evaluated the relevance of various parameters calculated from EEG spectral and bispectral analysis as an aid to diagnosing too light or deep sedation levels in ICU patients. The relevance of the EEG parameters was determined by comparison to a standard, which is the use of scales of the level of clinical sedation. A linear combination of these parameters into a multivariate index was performed with the aim of improving relevance.

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This prospective exploratory study in consecutive patients was performed in a 20-bed ICU.

The protocol was approved by the ethical committee of our institution and informed consent was previously obtained by a legal representative of each patient. All the included patients had been hospitalized for more than 48 h. They received mechanical ventilation and sedation for more than 24 h. The number of patients included and the number of protocols per patient were only limited by the availability of the EEG equipment.

Exclusion criteria were as follows:

  • Extreme ages (prepubertal children and patients over 80 yr of age).
  • Neurological, medical, or traumatic disorders that might alter EEG interpretation: cranial trauma; metabolic or hypoxic encephalopathy, meningoencephalitis, stroke, tumors and perinatal neurologic disorders, renal failure (blood creatinine concentration >220 μmol/L and/or blood urea nitrogen >20 mmol/L; hypothermia (≪36.0°C).
  • Administration of neuromuscular blockers.
  • Surgical procedure requiring general anesthesia or analgesia for <48 h.

All included patients received the same sedation protocol. Midazolam was administered continuously at 0.1 to 0.2 mg·kg−1·h−1 associated with morphine administered continuously at 0.018 to 0.066 mg·kg−1·h−1.

The dosage of morphine was maintained constant during the study. Only the dosage of midazolam was modified by the intensive care specialist to obtain the intended level of clinical sedation (typically Ramsay score, 2 or 3).

These patients had conventional monitoring with at least electrocardiographic monitoring, arterial blood pressure monitoring, pulse oximetry and capnography (end-tidal CO2).

Patients were studied in the absence of stimulation when they were clinically stable. Two types of data were then collected.

1. Clinical evaluation of the level of sedation. Patients were assessed by the same physician (JPR) following an observation of 1 min. The level of sedation was quantified according to two scales: Ramsay scale (Table 1) (17) and the COMFORT scale (Table 2) (18).

Table 1

Table 1

Table 2

Table 2

2. EEG data. The EEG was recorded using central frontal dipoles (Fz-C3 or Fz-C4) of the international 10-20 system nomenclature (19). Silver scalp electrodes were placed 1 h before recording and the circuit impedance verified to be lower than 2 K Ω. The EEG was recorded for 300 s. After amplification and analog antialiasing filtering (6th order elliptic filter: SCXI-1141; National Instruments®, Austin, TX; passband frequency, 0.3 to 40 Hz), the signal was digitized (analog-to-digital converter: AI-16XE-50, National Instruments®; 16 bits amplitude resolution; sampling frequency, 128 Hz;) then stored on a hard drive for off-line processing.

Clinical evaluation was performed several minutes before the EEG recording. Every measurement included both sets of parameters (clinical and electrophysiologic) corresponding to the same state of the patient.

The computer processing of the EEG signal was performed by a program developed for the study on LabVIEW™ software, version 5.1 (National Instruments®). This program included three successive phases:

Phase of artifact rejection: This phase was designed to detect and reject peaks of voltage corresponding to interfering movements (essentially head, eyeball, and eyelid movements) and eliminate the phenomenon of wandering of the isoelectric line. The signal of the records was separated into 4-s epochs (512 points). Each epoch overlapped 3 s of the preceding epoch. This division of the 300 s of raw signal provided 297 epochs. After filtering, only the complete epochs containing no eliminated fragments were included in the analysis.

Spectral analysis of the signal. Spectral analysis was performed on the remaining epochs provided that the rate of rejection of the signal by the filtering did not exceed 50% of the totality (300 s) of raw signal and that more than 100 valid epochs remained. Then the power spectrum of the temporal signal is calculated using Fourier transformation. Figure 1A shows an epoch of 512 points (4 s) of signal, and Figure 1B shows the power spectrum calculated from this epoch (Fig. 1).

Figure 1.

Figure 1.

Bispectral analysis of the signal (Appendix). Based on Fourier transformation, the bispectrum of the temporal signal x(t) is a function of two frequencies (f1 and f2), which should be represented in three dimensions (Fig. 1C).

The first 100 periods of the filtered EEG were treated providing 100 spectra. After linear RMS averaging these spectra, the spectral and bispectral parameters usually considered in the literature were calculated. After this analysis of the EEG signal, a set of 10 parameters was obtained (6 spectral and 4 bispectral parameters) corresponding to the observation of a given state of sedation.

  • Spectral parameters:
    • - Relative power in the bands:
      • β (13 – 30 Hz)
      • α (8 – 13 Hz)
      • θ (4 – 8 Hz)
      • δ (1 – 4 Hz).
    • - SEF95 (95th percentile of the power spectrum)
    • - SEF50 (50th percentile of the power spectrum).
  • Bispectral parameters:
    • - Real Triple Product (RTP)
    • - Bispectrum (Bispectrum)
    • - Bicoherence (Bic)
    • - Ratio 10 (Appendix)

The objective of the analysis was to evaluate the relevance of EEG parameters for the prediction of the level of sedation of ICU patients defined after transformation of Ramsay scale into two Boolean variables:

  • - “light” sedation defined by Ramsay scores of 1 or 2,
  • - “deep” sedation defined by Ramsay scores of 5 or 6.

Two analyses were performed:

  1. When the levels of sedation were separated into light versus not light sedation and
  2. When the levels of sedation were separated into deep versus not deep sedation.

The relevance of each parameter to distinguish between these two levels of sedation was expressed by the area under the receiver operating characteristic (ROC) curve (20). In the absence of transformation of variables, a generalization of the Mann-Whitney U-test was used to calculate the AUC and confidence interval because the EEG parameters did not exhibit a normal distribution (21). The correlation between EEG parameters was studied using the Spearman rank correlation test.

Linear combinations of several EEG parameters that maximize the area under the ROC curve were calculated assuming unequal variance (22). The results include the linear combinations that provide the best area under the ROC curve using:

  1. Two among all EEG parameters,
  2. All EEG spectral parameters,
  3. All EEG bispectral parameters,
  4. All the EEG parameters.

To eliminate possible errors owing to EEG artifacts involving patient movements, all the previous calculations were performed by eliminating the agitated patients having COMFORT 4 score (evaluation of patient movements) of more than 2.

The statistical analysis was performed using NCSS™ software (Kaysville, UT) and m-ROC (CRLC Val d’Aurelle, Montpellier, France). P < 0.05 was considered significant.

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The study population of 40 patients consisted of 30 men and 10 women. Patients ranged in age from 14 to 78 yr (mean ± sd, 50 ± 19 years) and the S.A.P.S.II score from 15 to 58 (mean ± sd, 33 ± 12) with a theoretical risk of in-hospital death of 14% (23), similar to the death rate of 17% observed in the present group. The studied population included a majority of surgical patients (polytrauma and postoperative patients) with no neurological disorders (Table 3).

Table 3

Table 3

From this population were obtained 167 observations, each consisting of a clinical evaluation and a simultaneous EEG recording of 300-s duration. Figure 2 shows the distribution for these 167 observations of the level of sedation in terms of the clinical stages proposed by Ramsay. The digital filtering of these 167 records eliminated 16 records that contained <100 valid epochs. Consequently, the calculation of spectral and bispectral parameters was performed on 151 records.

Figure 2.

Figure 2.

The coefficients of correlation between various parameters taken two by two are presented in Table 4. Coefficient values more than 0.7 indicate a strong correlation among the variables.

Table 4

Table 4

There was strong correlation between the parameters of spectral analysis, particularly between SEF95 and relative power in the four spectral bands. There was also strong correlation between the parameters of bispectral analysis (with the exception of the RTP, which is a mathematical calculation without corresponding electrophysiological significance). Moreover, there was a strong correlation between the spectral and bispectral parameters. For example, the coefficient of correlation between SEF95 and Bic was 0.90 and that between SEF95 and Ratio 10 was 0.77.

Thus there is redundancy among these parameters, explaining the weak improvement of the relevance when they are linearly combined in a multivariate index (Tables 5–8).

Table 5

Table 5

Table 6

Table 6

Table 7

Table 7

Table 8

Table 8

The distribution of the values of SEF95 and Ratio 10 in all of the records according to Ramsay scores is shown in Figure 3. There was a wide dispersion of the values of these parameters for a given clinical level of sedation resulting in an overlapping of values between the “deep sedation” and “light sedation” populations.

Figure 3.

Figure 3.

The results of the relevance assessment of each parameter regarding the prediction of levels of sedation are presented in Tables 5 through 8. These results are presented as the area under the ROC curve (AUC) with the 95% confidence interval. These calculations were performed for every parameter, for the best linear combination of two parameters, for the best linear combination of all the spectral parameters, for the best linear combination of all the bispectral parameters, and for the best linear combination of all the parameters. Table 5 shows the results of comparison of records corresponding to a light clinical level of sedation (Ramsay 1 and 2) with those corresponding to the other levels (Ramsay 3 to 6). Table 6 shows the results of comparison of recordings corresponding to a deep clinical level of sedation (Ramsay 5 and 6) with those corresponding to the other levels (Ramsay 1 to 4).

Analyses reported in Tables 5 and 6 concerned all patient records. Analyses reported in Tables 7 and 8 were performed after exclusion of the records made during periods when the COMFORT 4 score was more than 2 (n = 12).

These tables show AUC values, in some cases, higher than 0.8 for certain parameters reflecting good discriminating power. Unfortunately, this must be tempered by wide 95% confidence intervals. For all the records (n = 151), the most relevant bispectral parameter for predicting the light levels of sedation was Ratio 10 and the most relevant spectral parameter was the SEF95. Their predictive power were similar (AUC = 0.829 for Ratio 10 versus 0.798 for SEF95). Bispectral analysis does not so seem to bring more precision than simple spectral analysis in the diagnosis of states of light sedation in ICU patients.

The most relevant parameters for predicting the deep levels of sedation were also Ratio 10 and SEF95. The predictive power of ratio 10 was better than that of SEF95 (AUC = 0.763 versus 0.687, respectively). Bispectral analysis provided better diagnosis of the states of deep sedation than spectral analysis.

Both types of analysis, spectral and bispectral, were better correlated to the light levels of sedation than to the deep levels of sedation. In fact, Ramsay scale became ineffective when patients were nonreactive and these patients, who were heavily sedated (Ramsay 6), varied widely in EEG status.

When the agitated patients (n = 16) were excluded, results remained unchanged. Ratio 10 and SEF95 were highly similar in predictive power for the diagnosis of lightly sedated patients (AUC = 0.804 versus 0.801, respectively). The relevance of Ratio 10 in predicting the stage of deep sedation was also more than that of SEF95 (AUC = 0.756 versus 0.684) after exclusion of the agitated patients.

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The originality of our work consisted in mathematically calculating spectral and bispectral descriptors from raw EEG data to test their relevance for assessing too light or deep sedation levels in ICU patients, with the exception of those presenting a neurological disorder. The design of our study does not allow the determination of each level of Ramsay score by a linear combination of various EEG parameters. The goal was to make the EEG diagnosis of two particular clinical situations: too light, or oversedated.

The present study yielded three primary results. First, the various calculated EEG spectral descriptors exhibited large interindividual variability. Second, bispectral analysis provided the same precision as spectral analysis in distinguishing between the stages of sedation of ICU patients. Third, there was a strong correlation between spectral and bispectral parameters of the EEG, reflecting in some way the redundant nature of the two methods. Despite the absence of statistical significance, the analysis of AUC (0.804 for Ratio 10 to 0.919 for all the EEG parameters) shows an improvement in discriminative power of a linear combination of these EEG spectral descriptors into a multivariate index.

Among various spectral parameters, the SEF95 was the most relevant for predicting the level of sedation. SEF95 exhibited a high AUC (0.687) corresponding to the prediction of deep sedation levels and an even higher AUC (0.798) correlating it with states of light sedation. This result is consistent with different studies performed in patients who were sedated by midazolam (24,25). Spencer et al. (26) found no consistent correlation between the level of sedation and any single EEG spectral parameter. By performing multivariate analysis using parameters obtained from the power spectrum (e.g., tenth percentiles, mean frequency), those authors obtained a better correlation among these variables and clinical levels of sedation. They observed substantial overlap between the stages of sedation. They concluded that the substantial variability of EEG parameters among the patients and within individual patients would explain why simple spectral parameters of EEG cannot discriminate between levels of sedation. Similarly, in the present study various EEG parameters showed marked variability with an overlapping of values between the stages of light and deep sedation. This variability of EEG parameters constitutes a limitation of the EEG technique for monitoring anesthesia or sedation (9,25–27).

Bispectral analysis could improve the precision of monitoring because it theoretically provides a more complete analysis of the modifications of EEG signal in response to the administration of anesthetic drugs. We reported in our study that the most relevant bispectral parameter was Ratio 10 (AUC = 0.763 for prediction of deep sedation and 0.829 for light sedation). During the deepening of sedation, the value of the Bic function increases preferentially in low frequencies (10). Ratio 10 quantifies this tendency for relative variation in value of the Bic function in favor of low frequencies. However, the method consisting of correlating variations of sedation to variations in degree of phase coupling is not at present supported by any clearly established neurophysiologic mechanism (8,9). The first assessment of EEG bispectral analysis was performed using BIS® (Aspect Medical Systems, Natick, MA). The BIS® is a nondimensional index obtained from three EEG subparameters, the relative contributions of which are correlated to the level of anesthesia using a multivariate regression model (9,10). Studies comparing BIS® with conventional spectral parameters in the determination of the level of sedation (13,25,28–30) have established the superiority of BIS®, notably regarding the SEF95. However, the relevance of BIS® in intensive care is disappointing and the correlation between BIS® and clinical level of sedation remains weak to moderate (11–15). Moreover, all authors note large interindividual variability of the values of BIS® for a given clinical stage of sedation. Several authors (12,13) have reported that BIS® can be altered by the EMG. Vivien et al. (16) quantified the interference between EMG activity and BIS® in 45 heavily sedated ICU patients who had no neurological disorder. The administration of muscle relaxant to these patients significantly reduced the values of both BIS® (67 ± 19 versus 43 ± 10; P < 0.001) and EMG activity (37 ± 9 versus 27 ± 3 dB; P < 0.001). This interference of the BIS® by EMG activity fosters false diagnoses of insufficient sedation, leading to a risk of unjustified oversedation by the clinician. The overlap between the bandpasses of EEG signal (conventionally 0.5 to 30 Hz) and EMG activity (conventionally 30 to 300 Hz) results in artifactual contamination of the EEG signal processed by the BIS® monitor, overestimating the value of the BIS® in patients not receiving neuromuscular blockers. In the present study, we did not measure EMG activity, but exclusion of a subgroup of agitated patients did not change our results. This might be attributable to the efficacy of the analogical and digital filtering that we used before signal processing and because our analysis of EEG signal concerned a bandpass of which the superior limit of 30 Hz was lower than that used with BIS® software (47 Hz). These studies question the validity of BIS® in ICU patients in some cases, even when the most recent software versions (BIS® XP monitor) are used (16). Our method is similar to the one used to develop the BIS® except that, in our calculations, we did not include parameters accounting for the degree of burst suppression. Indeed, this level of depression of cortical activity is excessive in the framework of simple sedation. In addition, visual inspection of the raw EEG records of our study showed no isoelectric period.

The sedation protocol of our patients associated a benzodiazepine and an opioid. This type of bimodal sedation is the most used in European ICUs (1). Benzodiazepines have an EEG action different from the action of other anesthetic drugs. Although anesthetic drugs have a biphasic EEG action (initial phase of acceleration of the EEG followed with a deceleration and synchronization dosage-dependent phase), benzodiazepines only accelerate the EEG (β activation) (31). However, Greenblatt et al. (32) found that the increased β power band was lost when the subject slept. The implication is that an optimal EEG spectral parameter (SEF95, SEF50) for midazolam-based sedation might well not be optimal for sedation with propofol or other anesthetics. However, there are no comparative data concerning EEG bispectral parameters to evaluate sedation with these drugs. EEG modifications of opiates look like those recorded during sleep: deceleration and appearance of slow long waves (synchronization). These effects are modest with morphine (33).

We have chosen, in our study, the Ramsay and the COMFORT scales for the clinical evaluation of the level of sedation. Although the Ramsay scale was the first published (1974), it remains relevant (34) and is the most widely used scale in studies on the sedation in ICUs. It shows good interrater reliability with the other validated sedation scales in adult intensive care (35). We transformed the Ramsay scale into 2 Boolean variables: Light sedation (Ramsay 1 or 2) versus other levels of sedation (Ramsay 3, 4, 5, or 6) and deep sedation (Ramsay 5 or 6) versus other levels of sedation (Ramsay 1, 2, 3, or 4). This transformation permitted more reliable statistical comparison between only two, more distinct groups. We used the COMFORT scale (Table 2) it in our work to assess the spontaneous movements of the patients and isolate a subgroup of patients with frequent movements (COMFORT 4 > 2).

There was a predominance of deep sedation in the 167 raw EEG records (Fig. 2). This reflected the fact that, irrespective of instrumental monitoring of sedation, diagnosis of levels of insufficient sedation is more straightforward (e.g., agitation, pulling at endotracheal tube). Consequently light levels of sedation are easier to manage than deep levels. Overly deep sedation is frequent in ICUs, especially in surgical or polytrauma patients. Clinical scores of sedation become ineffective when the patient is nonreactive and excessive sedation is often diagnosed by delayed recovery of consciousness when medication is discontinued (1,30) The exclusion of the 16 records with excessive interference from patient movements essentially eliminated records corresponding to light levels of sedation, aggravating the imbalance between the groups. Overall, among 151 records studied, 19 corresponded to light levels of sedation (Ramsay 1 and 2) and 93 to deep levels of sedation (Ramsay 5 and 6). This imbalance reduced the statistical power of the study.

Another methodological limitation of the present study is that the study population was a convenience sample (essentially involving the availability of the materials) and not a truly random population. However, the conditions of the study simulated usual and customary bedside recording by a caregiver. In addition, for reasons of convenience (reclining position of the subjects, often hairless frontal region) a single frontocentral dipole (Fz-C3 or Fz-C4) was used. Although regional variations in EEG changes during sedation or anesthesia have been reported, practical applications remain scarce (36).

In conclusion, our results show that 60% of the patients had over-sedation (Ramsay 5 or 6). The subjective clinical techniques of assessment of sedation are not able to detect this over-sedation. The objective techniques based on spectral analysis and, more recently, bispectral analysis of spontaneous EEG might offer a solution. SEF95 and Ratio 10 are, respectively, the most relevant spectral and bispectral parameters for monitoring the level of sedation. Our results show that bispectral analysis of the EEG provides a slight improvement over simple spectral analysis in terms of predictive power despite the higher order of calculations involved. In addition, we noted a hindrance that has long been reported in the use of EEG for monitoring in anesthesia and intensive care: the large interindividual variability of results. Further studies will be necessary to evaluate continuous neuromonitoring in intensive care, not by reasoning in terms of a general population but in terms of individual clinical courses over time using basic EEG. Neither recommendations nor goals for EEG-guided midazolam-based sedation of a given ICU patient may be drawn from the present study.

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The bispectrum of the temporal signal x(t) is a function of two frequencies (f1 et f2). The equation used to calculate the bispectrum B(f1,f2) is (10):

where the subscript i refers to the epoch number, L is the sum of the epochs, Xi(f) is the Fourier transformation of the ith epoch and Xi*(f1 + f2), the conjugate of Xi(f1 + f2).

The amplitude of the bispectrum B(f1,f2) is influenced by the degrees of phase coupling between each possible frequency pair f1, f2 and the frequency corresponding to their sum (f1 + f2), as well as by the power of the spectrum at frequencies f1, f2 and (f1 + f2). Consequently, it is necessary to normalize the bispectrum by calculation of the bicoherence: Bic(f1,f2). Bic(f1,f2) is obtained by dividing B(f1,f2) by the real triple product: RTP(f1,f2).

where P(f) is the power of the spectral component of frequency f.

This means that, independently of the signal power, Bic(f1,f2) expresses the degrees of phase coupling in a range from 0% (absence of phase coupling) to 100% (completely phase coupled signal).

The Ratio 10 represents the ratio of the bicoherence function in the band 1–10 Hz with respect to the total band (1– 30 Hz):

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