EP3773189A1

EP3773189A1 - Providing a parameter which indicates a loss of consciousness of a patient under anesthesia

Info

Publication number: EP3773189A1
Application number: EP19721581.7A
Authority: EP
Inventors: Susanne Koch; Claudia SPIES
Original assignee: Covidien LP
Current assignee: Covidien LP
Priority date: 2018-04-27
Filing date: 2019-04-26
Publication date: 2021-02-17
Also published as: US20210244353A1; DE102018110275A1; WO2019207130A1; CN112399826A

Abstract

The invention relates to a method and a device for providing a parameter which indicates a loss of consciousness of a patient under anesthesia. The method has the steps of: detecting (301) at least one EEG signal on the head of the patient; continuously determining (302) the spectral cut-off frequency in a current time window of the EEG signal; determining (303) the curve of the spectral cut-off frequency of the EEG signal in a period of time which begins before an anesthesia-inducing medication is administered and ends after the anesthesia-induced loss of consciousness is initiated; determining (304) the absolute minimum of the spectral cut-off frequency in the period of time, wherein a negative peak of the spectral cut-off frequency lies in the absolute minimum, and providing (305) information regarding at which point in time the absolute minimum was reached as a parameter for an indicative notification of the loss of consciousness of the patient.

Description

PROVISION OF A PARAMETER INDICATING A CONSUMER LOSS OF A PATIENTS UNDER NARCOSIS

description

The invention relates to a method and a device for providing a parameter indicative of a loss of consciousness of a patient under anesthesia.

The derivation of brain activity using surface electrodes as an electroencephalogram (EEG) has been known since the 1930s. The regular monitoring of the depth of anesthesia by means of frontal EEG conduction has been the 21st since the 90s. The EEG data analysis is limited to the calculation of an index calculated using a stored algorithm and intended to provide the anaesthesiologist with a simple estimate of the depth of anesthesia, see Sebel PS, Lang E, Rampil IJ, White PF, Cork R, Joplin M, Smith NT, Glass PSA, Manberg P: "A Multicenter Study of Bispectral Electroencephalogram Analysis for Monitoring Anesthetic Effect", Anesth Analg 1997; 84 (4): 891-899.

For an anesthetist, it is advantageous in his daily work to be able to pinpoint the onset of loss of consciousness in the case of anesthesia (LOC), since (1) a too early intubation with subsequent pain perception of the patient can be avoided and (2) too late intubation due to the deep anesthetic stage with loss of protective reflexes the danger of complications such as aspiration can be avoided. The above-mentioned EEG index shows a decrease in the time of loss of consciousness (LOC). However, this is the exact time of loss of consciousness can not be determined.

It is an object of the present invention to provide a method and apparatus that automatically provide information that allows an anesthetist to more accurately determine in a patient the precise determination of the onset of loss of consciousness during anesthesia.

This object is achieved by a method having the features of patent claim 1 and a device having the features of patent claim 12. Embodiments of the invention are specified in the dependent claims.

Thereafter, in a first aspect of the invention, the invention provides a method of providing a parameter indicative of a patient's loss of consciousness under anesthesia. According to the method according to the invention, at least one EEG signal is detected at the head of the patient. Furthermore, the spectral cutoff frequency in a current time window of the EEG signal is continuously determined. The spectral cutoff frequency is defined as indicating the frequency for which it includes 95% of the total power in the power spectrum. In the power spectrum, the amplitude square is plotted against the frequency. It reflects the respective proportion of the individual frequency ranges in the total power component of the raw signal. The spectral cutoff frequency is therefore the frequency below which a certain proportion of the energy of the total spectrum lies. According to the invention, a proportion of 95% is considered.

The considered time window looks at the EEG signal in a time period that goes back a defined period of time from the current time, for example the EEG signal of the last minute or the last 30 seconds or the last 20 seconds. In this respect, it is a time window wandering with time. The spectral cutoff frequency is determined via a spectral analysis. In particular, a discrete Fourier transformation is carried out, for example a fast Fourier transformation (FFT - "Fast Fourier Transformation"). The considered time window indicates the time window, which is used as the basis for the spectral analysis. In accordance with the method according to the invention, furthermore, the course of the spectral cut-off frequency of the EEG signal is determined in a period which begins before application of the anesthetic and the onset of anesthesia-induced unconsciousness of the patient and ends after the onset of anesthesia-induced loss of consciousness. The absolute minimum of the spectral cut-off frequency is determined in the considered time period, with a negative peak of the spectral cut-off frequency in the absolute minimum, and information on when the absolute minimum has been reached, provided as a parameter for the indication of a patient's loss of consciousness or issued.

It should be noted that the mentioned period is not necessarily predefined in its length. For example, it may be provided that the period ends as soon as the existence of an absolute minimum has been determined.

The invention is based on the surprising and verified by a study finding that the time LOC of the loss of consciousness is accompanied by a brief drop in the spectral cutoff frequency. It has been recognized that the spectral cutoff decreases momentarily at the moment of loss of consciousness, with a subsequent reentry. In this case, a significant negative peak of the spectral cutoff frequency is formed, which is evaluated and its absolute minimum is determined. The values of the spectral cut-off frequency drop off before the absolute minimum and rise again behind the absolute minimum. This allows the absolute minimum to be determined unambiguously. The short-term drop in the spectral cut-off frequency at the time of loss of consciousness and its reentry is typically over a period of approximately 1 to 3 minutes, more particularly over a period of approximately 2 minutes.

By determining the timing of the negative peak, i. H. of the absolute minimum of the spectral cut-off frequency in the considered time period, a parameter can thus be provided which indicates, indi- cally, a loss of consciousness of the patient during the anesthesia and can be taken into account by the anaesthesiologist, possibly together with further parameters. This allows for improved anesthesia management.

It should be noted that in the considered period, the absolute minimum is determined and the time of its presence is communicated as a parameter. Local minima may also occur, especially during the fall of the signal, however have significantly higher minimum values than the absolute minimum indicating the negative peak of the spectral cutoff frequency. The negative peak, which contains the absolute minimum, is formed significantly stronger in terms of both its width and its depth than any local minima and therefore easily detectable.

According to the invention, a frontal EEG signal is preferably recorded, that is to say a frontal discharge, wherein the EEG signal is measured at at least two electrodes which are arranged at different locations on the forehead of the patient. It can be provided that several frontal EEG signals are recorded, which are averaged before a determination of the spectral cutoff frequency. For example, in the typical 10-20 system, signals are derived from electrodes positioned at positions F7, F8, Fp1, Fp2, and Fpz.

It can be a bipolar derivative (difference between two active electrodes) or a unipolar derivative (difference of several active electrodes against a common reference).

An embodiment of the invention provides that the spectral cutoff frequency is continuously determined such that it is redetermined at least every 30 seconds, in particular at least every 10 seconds, in particular at least every 2 seconds. It is obvious that the more frequently the spectral cutoff frequency is determined, the more accurate the time of the minimum of the spectral cutoff frequency can be determined.

A further embodiment of the invention provides that the information as to when the absolute minimum has been reached is provided as soon as it has been determined. Once the presence of an absolute minimum can be determined safely, the time at which the absolute minimum has been reached is output as a parameter. Since this information is important to the anesthesiologist as an indication of the loss of consciousness, the information will be provided as soon as possible.

For the analysis of whether there is an absolute minimum of the spectral cutoff frequency, known methods of data and curve analysis can be used. For example, it may be provided that the information about the time at which the minimum has been reached is provided when the spectral cut-off frequency has fallen below a value of 10 hertz, in particular below a value of 9 hertz, and rises again. Thus, it has been found that local minima are typically above these frequencies in the course of the spectral cutoff so that a drop in the spectral cutoff below 10 Hertz or 9 Hertz (or even to 8 Hertz) indicates the presence of the absolute minimum ,

An alternative evaluation method provides that the information about the time at which the minimum has been reached is provided if, for a defined number of measured values, the measured value of the spectral cutoff frequency is higher than the previous measured value. The number of measured values, which is determined as an indication that the spectral cutoff frequency increases again and thus the absolute minimum has been reached, naturally depends on how often the spectral cutoff frequency is determined.

A further embodiment provides that the spectral cutoff frequency at the EEG signal is determined after it has been filtered by a bandpass filter. The bandpass filter is designed, for example, such that it only passes signals in the frequency range from 0.5 to 40 Hz.

An embodiment of the invention provides that the spectral cut-off frequency is determined by means of a spectral analysis, wherein in each case a running time window of the EEG signal is evaluated. The spectral analysis is done for example by an FFT algorithm. However, other possibilities exist for the spectral decomposition of the EEG signal, which can alternatively be used, for example the discrete cosine transformation, a discrete wavelet transformation or a signal decomposition via a bandpass filter bank.

The method according to the invention is automated, in particular carried out by a computer program. The computer program contains program code for carrying out the method according to claim 1, when the computer program is executed on a computer.

In a further aspect of the invention, the invention relates to a device for providing a parameter indicative of a loss of consciousness of a patient under anesthesia. The device comprises:

Means adapted to detect at least one EEG signal at the patient's head, Means adapted to continuously determine the spectral cutoff frequency in a current time window of the EEG signal, the spectral cutoff frequency indicating the frequency for which it includes 95% of the total power in the power spectrum,

Means adapted to determine the course of the spectral cut-off frequency of the EEG signal in a period beginning before the administration of an anesthetic drug and ending after the onset of anesthesia-induced unconsciousness,

Means adapted to determine the absolute minimum of the spectral cut-off frequency in the time period, wherein in the absolute minimum there is a negative peak of the spectral cut-off frequency, and

Means adapted to provide information as to when the absolute minimum has been reached as a parameter for indicative indication of loss of consciousness of the patient.

Said means may be implemented by a microprocessor in conjunction with program code executed by the microprocessor.

In a further aspect of the invention, the invention relates to an EEG narcosis monitor having a device according to claim 12. The device according to the invention is thus integrated into an EEG narcosis monitor, wherein it is provided and designed to analyze and display EEG data in real time.

The invention will be explained in more detail with reference to the figures of the drawing with reference to several embodiments. Show it:

FIG. 1 shows exemplary EEG signals in the awake state and after anesthesia-induced unconsciousness, both as a time-dependent signal and in the power spectrum;

Figure 2 shows an example of the time course of the spectral cutoff frequency in one

The period of time before the onset of anesthesia-induced unconsciousness of a patient begins and ends after the onset of anesthesia-induced unconsciousness;

FIG. 3 is a flowchart of the method according to the invention; FIG. 4 shows by way of example a device for carrying out the method of FIG. 3; and

Figure 5 Positioning points for EEG electrodes according to the 10-20 system.

The relationship between the temporal course of the spectral cutoff frequency and the occurrence of the loss of consciousness during anesthesia, which was first recognized according to the present invention, will first be explained with reference to FIGS. 1 and 2, which has been confirmed on the basis of a study.

FIG. 1 shows an EEG signal for explaining the background of the invention in the upper illustration ("output signal"), as occurs in a patient in the awake state. The signal is shown both as a time signal (left) and after a spectral analysis as a power spectrum (right). In the power spectrum the power (the amplitude square) in db is plotted against the frequency in Hz. The power spectrum reflects the respective proportion of the individual frequency ranges in the total power component of the raw signal. Also shown is the spectral corner frequency SEF. This is defined as the frequency below which a share of 95% of the energy of the total spectrum lies. The median frequency F50 is also shown, but in this case it does not matter.

The lower representation ("anesthesia") of Figure 1 shows an EEG signal under anesthesia. It can be seen that the spectral cut-off frequency SEF is shifted to the left compared with the value in the patient being awake.

The spectral cut-off frequency (SEF) thus provides information about how awake a patient is. Since higher frequencies are contained in the EEG signal during wakefulness, high values of the spectral neck frequency result. In sleep state or under anesthesia slow frequencies dominate in the EEG, so that lower values of the spectral cutoff frequency are present.

The processes at the beginning of anesthesia are now considered. When initiating anesthesia, the patient is initially awake. There are high SEF values of about 17-20Hz. The patient goes under anesthesia in a deep unconsciousness. Purdon PL, Pierce ET, Mukamel EA, Prerau MJ, Walsh JL, Wong KFK, Salazar-Gomez AF, Harrell PG, Sampson AL, Cimenser A, Ching S, Kopell NJ, Tavares-SToeckel C, Habeeb K, Merhar R, Brown E .: "Electroencephalogram signatures of loss and recovery of consciousness from propofol ", PNAS 2013; 1 10 (12): E1 142-1 151, have shown that a deep unconsciousness induced by GABA activating anesthetics leads to a frontal alpha-band activation. As a result, relatively higher SEF values of 12-17 Hz also appear in intraoperatively low levels of unconsciousness.

In the present case, it could be shown in a prospective observational study that at the exact time of the anesthesia-induced loss of consciousness there is a very short-term decrease in the SEF values, combined with a subsequent recovery. The minimum of the resulting negative peak indicates the time of anesthesia-induced loss of consciousness.

The study was conducted in a group of 37 elderly patients with anesthesia induction with one of the most commonly used anesthetics, propofol. It is expected that in younger adults there will be a more significant drop in the baseline spectral frequency at the time of anesthesia-induced loss of consciousness.

The measurements were carried out as follows: a) EEG derivative:

In the perioperative EEG determination, the EEG electrodes were placed on the patient who was still awake before the anesthesiologist administered the first drugs. For this purpose, the forehead and temples were thoroughly disinfected and freed from skin fats. This measure improved the conductivity of the skin and therefore ensured a more trouble-free derivation of the EEG signals. The prefabricated EEG adhesive electrodes made by Masimo (4248RD SEDLine Sensor, Single Patient Use, Non-Sterile) were then placed on the prepared skin areas on the forehead, with the EEG electrodes in each case at the positions F7, F8, FP1 and FP2 corresponding to the 10 / 20 system systems, with Fpz as reference electrode. The corresponding positions are shown in FIG. The impedance of the individual electrodes was less than 5 kQ during the derivation, the sampling rate was 250 Hz. A bandpass filter is preset to 0.5 - 40 Hz.

After connecting the adhesive electrodes to an EEG-based brain function monitor (the "SEDLine Monitor" from Masimo Corporation, Irvine, California), the derivation and recording of a continuous 4-channel EEG was started. The Patients were still awake at this point, so the initial baseline values were in line with baseline activity. In order to determine defined times during the EEG derivation, "Event Markers" were entered manually during the EEG recording in the EEG. In the course of the drug administration was initiated by the anesthetist. This time was noted as an event marker "Start anesthesia". All patients received the drug propofol intravenously for anesthesia induction. The patient's state of consciousness was continuously checked on the basis of the eyelid reflex, loss of consciousness was assumed in the absence of eyelid reflex, and the event marker "loss of consciousness" was set. This procedure enables a second-exact evaluation of the data. b) EEG evaluation

The following data were recorded by the SEDLine Monitor: the spectral cut-off frequency (SEF), the anesthesia index (PSI), the artifact level and the electromyographic activity. These EEG data were manually exported from the SEDLine Monitor and displayed in numerical format on the computer in Excel spreadsheets.

Due to the SEDLine recording rate of 30 values per minute, all measured values were every two seconds. For each patient, it was first verified that the baseline, start anesthesia, and loss of consciousness timepoints were fully recorded and free of artifacts. For this purpose, both the artifact levels calculated by the device and the EMG artifacts also recorded were inspected. Usable records have been subdivided into "loss of consciousness" into time slots of 20 seconds each. In each time window, ranging from 200 seconds before "loss of consciousness" to 200 seconds after "loss of consciousness", the artifact-free spectral cutoff frequency of each patient was averaged for the right and left hemispheres.

The power spectrum for determining the spectral cutoff frequency was thus determined in time windows of the EEG signal of 20 seconds, with an update every 2 seconds. The calculation was carried out by means of digital, computer-assisted EEG signal processing. The basis for this is the spectral analysis of the RohEEG by means of Fast Fourier Transformation, by means of which power components can be calculated for the currently analyzed time window. To first determine if there is a difference between viewing the right and left hemispheres, the values before medication (-200 seconds), loss of consciousness (0 seconds), and after initiation (+200 Seconds) of all patients with a dominant right hand (n = 36) performed a 2-sided t-test for dependent samples. Since there was no significant difference at any of the time points (before drug administration p = 0.26, loss of consciousness p = 0.940, after initiation p = 0.44), later on the respective mean values of right and left hemispheres were determined worked.

All variables were checked for their normal distribution. For this purpose, the histogram and the Q-Q diagram were examined visually and the data were analyzed using the Lilliefors test, the Shapiro-Wilks test and the values for skewness and kurtosis.

To illustrate the procedure of initiation, surgery and diversion, the timing of the event markers of the EEG lead were evaluated and descriptive statistics were used to examine means and standard deviations.

The course of the spectral cutoff was studied to determine the onset of anesthesia-induced unconsciousness as part of a one-way oneway ANOVA (ANOVA = analysis of variance) and the associated post-hoc test.

In order to examine the spectral cutoff within 200 seconds before the onset of anesthesia-induced unconsciousness up to 200 seconds after the onset of anesthesia-induced unconsciousness to a possible influence of the time of intubation, the start of intubation with the Pearson correlation with the time points 20 seconds after " loss of consciousness "up to 200 seconds after" loss of consciousness ". To illustrate the course without the possible influence of intubation, the study population was divided into two groups. Patients who started intubation within 200 seconds of "loss of consciousness", one group, patients with onset of intubation after more than 200 seconds presented the other group. These were then tested for variance with the Levene test, and then tested for significant differences between groups through a T-test for independent samples. By the representation of only such cases in which the intubation after the observed period, it was possible to eliminate intubation as a possible interfering factor.

FIG. 2 shows the mean values of the spectral cutoff frequency ("SEK") determined in the study as a function of time. The mean of the spectral cut-off frequency in Hz is shown over the time of the loss of consciousness in the period of 200 seconds before the loss of consciousness until 280 seconds after the loss of consciousness. Loss of Consciousness (LOC) is characterized as "00" on the timeline. The following table gives the numerical values of the diagram of FIG. 2 including the respective standard deviation:

There is a clear negative peak, which has its minimum at the time of loss of consciousness (time "00"). The mean value of the spectral frequency drops from values of approx. 12-14 Hz to approx. 8 Hz and then increases to approx. 12-13 Hz. The width of the negative peak is about 2 minutes. By determining the minimum of the negative peak, the onset of loss of consciousness can be determined to an accuracy of 20 seconds or even less.

According to the invention, the correlations determined are evaluated electronically or computer-based and used to determine the course of the spectral cut-off frequency of the EEG signal and in this the absolute minimum of the spectral cut-off frequency as a parameter for the occurrence of anesthesia-induced unconsciousness. The associated program can be integrated as a software tool into an EEG-based brain function monitor or electroencephalograph.

FIG. 3 shows the method for determining a parameter which indicates a loss of consciousness of a patient under anesthesia. According to step 301, at least one frontal EEG signal is detected on a patient. For example, four EEG signals are recorded via electrodes at positions F7, F8, FP1 and FP2 according to the 10/20 system, with Fpz as the reference electrode and averaged over these signals.

According to step 302, the spectral cutoff frequency is continuously determined in a current time window of the EEG signal. The determination is carried out continuously, for example, in the sense that the spectral frequency is currently determined every 2 seconds or every 5 seconds. The current time window has, for example, a length of 20 seconds, the values mentioned being to be understood as examples only.

In step 303, the course of the spectral cut-off frequency of the EEG signal is evaluated. This occurs in a period of time that begins before the administration of an anesthetic drug (eg, propofol) and ends after the onset of anesthesia-induced unconsciousness. The period may be fixed or not fixed with regard to its duration. In the second case, the period ends, for example, as soon as the minimum of the spectral cutoff frequency could be determined. In step 304, the absolute minimum of the spectral cutoff frequency in the considered period is determined. It can be done, for example, by evaluating whether the spectral cutoff frequency has fallen below a value of 10 hertz, in particular below a value of 9 hertz, and is rising again. Alternatively or additionally, it can additionally be evaluated whether, for a defined number of measured values, the measured value of the spectral cutoff frequency is higher than the preceding measured value. Thus, a negative peak of the spectral cutoff frequency is evaluated, the absolute minimum of the spectral cutoff frequency lying in the negative peak of the negative peak. Further methods of data analysis and curve discussion can be used to determine the absolute minimum of the spectral cutoff frequency with the greatest possible accuracy.

Once the absolute minimum of the spectral frequency has been determined, this information is provided as a parameter for indicating indefinitely a patient's loss of consciousness, such as acoustically and / or on the display of an EEG monitor.

According to step 306, a physician or anesthetist can use this information to adapt an intubation to be made precisely to the individual state of consciousness of the patient and thus to perform the intubation neither too early nor too late. This leads to a higher safety for patients during the induction of anesthesia.

To carry out the method, an EEG-based brain function monitor or, in general, a computer can be used. The method steps for determining and evaluating the spectral cutoff frequency and for determining the absolute minimum of the spectral cutoff frequency are carried out by a program code which is executed in a processor. The program code is stored in a memory of the processor or is loaded into it before execution. The processor executing the program code may be the main processor of the EEG monitor or a separate processor.

FIG. 4 shows an example of a possible implementation of such an EEG-based brain function monitor 1. The EEG monitor 1 comprises a microprocessor 2, a memory 3, a control device 4, an output unit 5 and an interface 7 for connecting EEG cables. Via the interface 7, EEG cables with EEG electrodes 61, 62 can be connected to the EEG monitor 1. By way of example, two EEG cables are shown which receive an EEG signal, whereby further EEG cables may be provided for receiving a multi-channel EEG signal.

The EEG signal is supplied to the microprocessor 2. The program code is stored in the memory 3 or a program code can be loaded into the memory 3 which, when executed in the microprocessor 2, carries out the method explained with reference to FIG. The sequence can be controlled via the control device 4 and this can be set up to receive corresponding input commands. The control device 4 may be a main processor of the EEG monitor 1 or contain such. Alternatively, the functionality of the microprocessor 2 can be taken over by the control unit 4. In this case, further functionalities of the EEG monitor 1 can be realized via the control device 4 and / or other modules, not shown.

The microprocessor 2 thus determines when executing the loaded program code, the absolute minimum of the spectral cutoff frequency and the time at which this absolute minimum is present. The corresponding information is transmitted to the output unit 5 and outputted thereto. This can be done for example via a monitor 51 and / or an acoustic unit 52.

It should be understood that the invention is not limited to the embodiments described above and various modifications and improvements may be made without departing from the concepts described herein. Any of the features may be used separately or in combination with any other features unless they are mutually exclusive, and the disclosure extends to and includes any combinations and subcombinations of one or more features described herein. As far as areas are defined, these include all values within these areas as well as all subareas that fall into one area.

Claims

claims

1 . A method of providing a parameter indicative of a patient's loss of consciousness under anesthesia, the method comprising the steps of:

Detecting (301) at least one EEG signal at the head of the patient, continuously determining (302) the baseline spectral frequency in a current time window of the EEG signal, the baseline spectral frequency indicating the frequency for which it is 95% of the power spectrum Includes overall performance,

Determining (303) the course of the spectral cutoff frequency of the EEG signal in a time period beginning before the administration of an anesthetic drug and ending after the onset of anesthesia-induced unconsciousness, determining (304) the absolute minimum of the spectral cutoff frequency in the time period, wherein absolute minimum is a negative peak of the spectral cutoff frequency, and

Providing (305) information as to when the absolute minimum has been reached as a parameter for indicating indefinitely a patient's loss of consciousness.

2. The method according to claim 1, characterized in that at least one frontal EEG signal is recorded.

3. The method according to claim 2, characterized in that a plurality of frontal EEG signals are recorded, which are averaged before a determination of the spectral cutoff frequency.

4. The method according to claim 3, characterized in that in the 10-20 system signals are derived from electrodes positioned at the positions F7, F8, Fp1, Fp2 and Fpz.

5. The method according to any one of the preceding claims, characterized in that the spectral cutoff frequency is determined continuously to the extent that it is redetermined at least every 30 seconds, in particular at least every 10 seconds, in particular at least every 2 seconds.

A method according to any one of the preceding claims, characterized in that the information as to when the absolute minimum has been reached is provided as soon as it has been determined.

7. The method according to claim 6, characterized in that the information about the time at which the minimum has been reached is provided when the spectral cut-off frequency has fallen below a value of 10 Hertz, in particular below a value of 9 Hertz and rises again.

8. The method according to claim 6 or 7, characterized in that the information, at which time the minimum has been reached, is provided if, for a defined number of measured values, the measured value of the spectral cutoff frequency is higher than the previous measured value ,

9. The method according to any one of the preceding claims, characterized in that the spectral cutoff frequency on the EEG signal, is determined after it has been filtered by a bandpass filter.

10. The method according to any one of the preceding claims, characterized in that the spectral cutoff frequency is determined by means of a spectral analysis, in each case a running time window of the EEG signal is evaluated.

1 1. Computer program with program code for carrying out the method according to claim 1, when the computer program is executed on a computer.

12. A device for providing a parameter indicative of a loss of consciousness of a patient under anesthesia, in particular for carrying out the method according to claim 1, wherein the device comprises:

Means (2) adapted to detect at least one EEG signal at the patient's head,

Means (2) adapted to continuously determine the spectral cut-off frequency in a current time window of the EEG signal, the spectral cut-off frequency indicating the frequency for which it includes 95% of the total power in the power spectrum,

- Means (2), which are adapted to determine the course of the spectral cut-off frequency of the EEG signal in a period of time prior to the administration of a narcotic-inducing drug begins and ends after the onset of anesthesia-induced unconsciousness,

Means (2) arranged to determine the absolute minimum of the spectral cutoff frequency in the time period, the absolute minimum being a negative peak of the spectral cutoff frequency, and

Means (2) arranged to provide information as to when the absolute minimum has been reached, as a parameter for indicative indication of loss of consciousness of the patient.

13. The device according to claim 12, characterized in that the means (2), which are adapted to continuously determine the spectral cutoff frequency in a current time window of the EEG signal, are adapted to the spectral cutoff frequency at least every 30 seconds, in particular at least every 10 seconds, in particular at least every 2 seconds to redetermine.

Device according to claim 12 or 13, characterized in that the means (2) adapted to provide information at which point in time the absolute minimum has been reached are arranged to provide the information as soon as the absolute minimum has been determined.

Apparatus according to claim 14, characterized in that the means (2) arranged to provide information at which point in time the absolute minimum has been reached are arranged to provide the information when the spectral cut-off frequency falls below a Value of 10 hertz, in particular, has fallen below a value of 9 hertz and is rising again.

Device according to claim 14 or 15, characterized in that the means (2) adapted to provide information at which point in time the absolute minimum has been reached are arranged to provide the information when for a defined time Number of measured values, the measured value of the spectral cutoff frequency is higher than the previous measured value.

17. Device according to one of claims 12 to 16, characterized in that the means (2), which are adapted to continuously determine the spectral cutoff frequency, have means which are adapted to a spectral analysis be carried out, each a running time window of the EEG signal is evaluated.

18. Device according to one of claims 12 to 17, characterized in that the means (2), which are adapted to determine the spectral cutoff frequency continuously, are adapted to determine the spectral cutoff frequency on the EEG signal, after this has been filtered by a bandpass filter.

19. Device according to one of claims 12 to 18, characterized in that the means (2) which are adapted to at least one EEG signal at the head of

Patients are set up to record at least one frontal EEG signal.

20. The device according to claim 19, characterized in that the means (2), which are adapted to detect at least one EEG signal at the head of the patient, are adapted to receive a plurality of frontal EEG signals, the before a determination of the be averaged spectral cutoff frequency.

21. EEG anesthesia monitor (1) with a device according to claim 12.