Provides parameters indicating loss of consciousness in patients under anesthesia

Abstract

The present invention relates to a method and apparatus for providing a parameter indicative of loss of consciousness of a patient under anesthesia. The method comprises the following steps: detecting (301) at least one EEG signal on the head of the patient; continuously determining (302) a spectrum cutoff frequency within a current time window of the EEG signal; determining (303) a curve of the spectrum cutoff frequency within a time period of the EEG signal starting before administration of anesthesia-inducing drugs and ending after induction of anesthesia-induced loss of consciousness; determining (304) an absolute minimum of the spectrum cutoff frequency within the time period, wherein a negative peak of the spectrum cutoff frequency is at the absolute minimum, and providing (305) information related to a time point at which the absolute minimum is reached as a parameter indicatively notifying the patient of the loss of consciousness.

Description

[0001] The present invention relates to a method and apparatus for providing a parameter indicative of loss of consciousness in a patient under anesthesia.

[0002] The derivation of brain activity as an electroencephalogram (EEG) from surface electrodes has been known since the 1930s. Since the 1990s, the depth of anesthesia has often been monitored by frontal EEG derivation, where analysis of the EEG data was limited to calculation of an index calculated by a stored algorithm and was intended to provide the anesthesiologist with a simple method of estimating 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.

[0003] In the daily work of an anesthesiologist, it is advantageous for the anesthesiologist to be able to accurately determine the onset of loss of consciousness (LOC) when anesthesia has been administered, because (1) it can avoid intubation too early, which may cause the patient to feel pain later, and (2) it can avoid intubation too late, which may bring the risk of complications such as aspiration due to deep anesthesia and loss of defensive reflexes. The above EEG index shows a decrease at the time point of loss of consciousness (LOC). Therefore, it is also impossible to determine the exact time point of loss of consciousness.

[0004] It is an object of the present invention to provide a method and apparatus that automatically provide information that allows an anesthesiologist to more accurately determine the onset of loss of consciousness in a patient when an anesthetic has been administered.

[0005] This object is achieved by a method having the characteristics described in claim 1 and a device having the characteristics described in claim 12. Embodiments of the invention are indicated in the dependent claims.

[0006] In a first aspect, the present invention then provides a method for providing a parameter indicative of loss of consciousness of a patient under anesthesia. According to the method of the invention, at least one EEG signal is recorded on the patient's head. In addition, a spectral cutoff frequency is continuously determined within a current time frame of the EEG signal. In this case, the spectral cutoff frequency is defined as the frequency indicating that 95% of the total power is contained in the power spectrum. In the power spectrum, the squared amplitude is plotted against the frequency. It reflects a specific proportion of the individual frequency regions in the proportion of the total power of the original signal. Therefore, the spectral cutoff frequency is above the frequency at which a specific proportion of the energy of the total spectrum is located. According to the invention, a proportion of 95% is taken into account in this case.

[0007] The time frame considered takes into account the EEG signal within a time period of a defined time length from the current time back, such as the EEG signal of the last minute or the last 30 seconds or the last 20 seconds. This is a time frame that migrates over time. In this case, the spectrum cutoff frequency is determined by spectrum analysis. In particular, a discrete Fourier transform, such as a fast Fourier transform (FFT), is performed. In this case, the time frame considered indicates the time frame that forms the basis for the spectrum analysis.

[0008] According to the method of the present invention, the response of the spectral cutoff frequency of the EEG signal is additionally determined before the administration of an anesthetic and in a time period starting before the occurrence of anesthetic-induced loss of consciousness of the patient and ending after the occurrence of anesthetic-induced loss of consciousness. An absolute minimum of the spectral cutoff frequency within the time period considered is determined, wherein a negative peak of the spectral cutoff frequency exists at an absolute minimum, and information related to the point in time at which the absolute minimum has been reached is provided or output as a parameter for indicatively indicating loss of consciousness of the patient.

[0009] It is pointed out here that the length of the time period is not necessarily predefined. For example, once the existence of an absolute minimum value is determined, the time period can be ended.

[0010] The present invention relates to the surprising knowledge, which is supported by studies, that the point of loss of consciousness LOC is accompanied by a brief drop in the spectral cut-off frequency. It has been shown that when consciousness is lost, the spectral cut-off frequency drops briefly at a precise point in time and then rises again. In this case, a clear negative peak is formed in the spectral cut-off frequency, which is evaluated and its absolute minimum is determined. The value of the spectral cut-off frequency drops before the absolute minimum and increases again after the absolute minimum. Therefore, the absolute minimum can be clearly determined. The spectral cut-off frequency drops briefly at the time of loss of consciousness, and the re-increase usually occurs within a period of about 1 to 3 minutes, in particular within a period of about 2 minutes.

[0011] By determining the time point of the negative peak, i.e. the time point of the absolute minimum of the frequency spectrum cutoff frequency within the time period considered, a parameter indicative of the patient's loss of consciousness when the anesthetic is administered can thus be provided and, if necessary, the anesthesiologist can take said parameter into account along with other parameters. This can improve anesthetic management.

[0012] It should be noted that, within the period considered, the absolute minimum is determined and the point in time at which it occurs is given as a parameter. Local minima may also occur, in particular during the fall of the signal, which, however, have a minimum value significantly greater than the absolute minimum indicating the negative peak in the spectrum cutoff frequency. The negative peak containing the absolute minimum is significantly larger than any potential local minimum in terms of both width and depth, and is therefore easy to detect.

[0013] According to the invention, a forehead EEG signal is preferably picked up, i.e. a forehead derivation is performed, wherein the EEG signal is measured at at least two electrodes arranged at different positions on the patient's forehead. In this case, a plurality of forehead EEG signals may be picked up which are averaged before determining the spectral cutoff frequency. In the commonly used 10-20 system, the signals are derived from electrodes located, for example, at positions F7, F8, Fp1, Fp2 and Fpz.

[0014] Either a bipolar derivation (difference between two active electrodes) or a unipolar derivation (difference of multiple active electrodes relative to a common reference) can be performed.

[0015] One embodiment of the present invention provides that the spectral cut-off frequency is continuously determined by re-determining at least every 30 seconds, in particular at least every 10 seconds, in particular at least every 2 seconds. Obviously, the more frequently the spectral cut-off frequency is determined, the more accurately the time point of the minimum value of the spectral cut-off frequency can be determined.

[0016] Another embodiment of the invention provides that once the minimum has been determined, information about the point in time at which the absolute minimum has been reached is provided. Once the presence of the absolute minimum can be reliably determined, the point in time at which the absolute minimum has been reached is output as a parameter. Since this information is important for the anesthesiologist as an indication of the occurrence of loss of consciousness, it should be provided as quickly as possible.

[0017] In order to analyze whether the spectral cutoff frequency comprises an absolute minimum, data analysis and curve analysis methods known per se can be used. For example, if the spectral cutoff frequency has dropped to a value below 10 Hz, in particular below a value below 9 Hz and increases again, information about the point in time at which the minimum has been reached can be provided. Obviously, the local minimum in the response of the spectral cutoff frequency is usually greater than this frequency, and therefore a drop in the response of the spectral cutoff frequency below a value of 10 Hz or 9 Hz (or even up to 8 Hz) indicates the presence of an absolute minimum.

[0018] An alternative evaluation method provides that if, for a defined number of measured values, the measured value of the spectral cut-off frequency is greater than the previous measured value, information about the point in time at which a minimum value has been reached is provided. The number of measured values ​​established as an indication that the spectral cut-off frequency has increased again and has therefore reached an absolute minimum value depends of course on the frequency with which the spectral cut-off frequency is determined.

[0019] Another embodiment provides that the spectral cut-off frequency of the EEG signal is determined after the signal has been filtered by a bandpass filter. The bandpass filter is designed such that it only allows signals to pass, for example, within a frequency range of 0.5-40 Hz.

[0020] One embodiment of the invention provides that the spectral cut-off frequency is determined by spectral analysis, in each case evaluating parallel time frames of the EEG signal. For example, the spectral analysis is performed by means of an FFT algorithm. However, there are other possible ways of spectrally decomposing the EEG signal which can be used alternatively, such as discrete cosine transform, discrete wavelet transform or signal decomposition by means of a bandpass filter bank.

[0021] The method according to the present invention is automatically executed, in particular, by a computer program. The computer program contains program code, and the program code is used to execute the method according to technical solution 1 when the computer program is run on a computer.

[0022] In another aspect of the invention, the present invention relates to a device for providing a parameter indicative of loss of consciousness of a patient under anesthesia. The device comprises:

[0023] - a member designed to record at least one EEG signal on the head of a patient,

[0024] - means designed to continuously determine a spectral cutoff frequency within a current time frame of the EEG signal, wherein the spectral cutoff frequency indicates a frequency at which 95% of the total power is contained in the power spectrum,

[0025] - means designed to determine the response of the spectral cut-off frequency of the EEG signal within a time period starting before the administration of anesthesia inducing drugs and ending after the occurrence of anesthesia-induced loss of consciousness,

[0026] - means designed to determine an absolute minimum value of the spectral cut-off frequency within said time period, wherein a negative peak of the spectral cut-off frequency is present at an absolute minimum value, and

[0027] - means designed to provide information about the point in time at which an absolute minimum has been reached as a parameter for indicatively indicating a patient's loss of consciousness.

[0028] The components may be formed by a microprocessor in combination with program codes executed by the microprocessor.

[0029] In another aspect, the present invention relates to an EEG anesthesia monitor comprising the device according to technical solution 12. Therefore, the device according to the present invention is integrated in an EEG anesthesia monitor, wherein the monitor is intended and designed to analyze and present EEG data in real time.

[0030] The invention will be explained in more detail below based on several embodiments with reference to the figures in the accompanying drawings, in which:

[0031] Figure 1 The EEG signals in the awake state and after anesthesia-induced loss of consciousness are shown by way of example in the form of time-correlated signals and power spectra;

[0032] Figure 2 The time course of the spectrum cutoff frequency over a time period starting before the occurrence of anesthesia-induced loss of consciousness in an anesthesia-induced patient and ending after the occurrence of anesthesia-induced loss of consciousness is shown by way of example;

[0033] Figure 3 is a flow chart of a method according to the present invention;

[0034] Figure 4 By way of example, the Figure 3 an apparatus for the method; and

[0035] Figure 5 Shown are the positioning points of the EEG electrodes according to the 10-20 system.

[0036] First, based on Figure 1 and Figure 2 To explain the relationship between the time course of the spectral cut-off frequencies and the occurrence of loss of consciousness when an anesthetic has been administered, which relationship has been confirmed by studies, first identified according to the invention.

[0037] To explain the background of the present invention, Figure 1 The upper diagram ("Output signal") shows an EEG signal occurring in an awake patient. After spectral analysis, the signal is plotted in the form of a time signal (left) and a power spectrum (right). In the power spectrum, the power (squared amplitude) in db is plotted against the frequency in Hz. The power spectrum shows the relative proportions of the total power of the original signal made up by the individual frequency ranges. The spectral cutoff frequency SEF is also shown. This is defined as the frequency above which 95% of the energy of the total spectrum lies. The median frequency F50 is also plotted, but this is not important in the present case.

[0038] Figure 1 The bottom diagram of ("Anesthesia") shows the EEG signal under anesthesia. Obviously, the spectral cutoff frequency SEF is shifted to the left relative to the value when the patient is awake.

[0039] Therefore, the spectral cutoff frequency (SEF) provides information about the patient's level of wakefulness. Since the EEG signal in the awake state contains higher frequencies, the spectral cutoff frequency will appear at higher values. In the sleeping state or under anesthesia, slow frequencies dominate the EEG, resulting in lower values ​​of the spectral cutoff frequency.

[0040] Now observe the introduction of the anesthetic. When the anesthetic is introduced, the patient is initially awake. High SEF values ​​of approximately 17-20 Hz occur. The patient will enter a deep loss of consciousness under anesthesia. Purdon PL, Pierce ET, MukamelEA, Prerau MJ, Walsh JL, Wong KFK, Salazar-Gomez AF, Harrell PG, Sampson AL, Cinemser A, Ching S, Kopell NJ, Tavares-SToeckel C, Habeeb K, Merhar R, Brown E: "Electroencephalogram signatures of loss and recovery of consciousness from propofol", Proc Natl Acad Sci USA 2013;110(12):E1142-1151 Anesthetic-induced deep loss of consciousness has been shown to activate the frontal alpha-band through GABA activation. Therefore, relatively high SEF values ​​of 12-17 Hz were also shown during surgery under deep loss of consciousness.

[0041] In the present case, a prospective observational study was able to show that there is a very brief decrease in the SEF value just at the time of anesthetic-induced loss of consciousness, which is associated with a subsequent re-increase. The minimum value of the resulting negative peak here indicates the time point of anesthetic-induced loss of consciousness.

[0042] A total of 37 elderly patients were studied, in which anesthesia was introduced with one of the most commonly used anesthetics, specifically with propofol. It can be assumed that in young people, the spectral cutoff frequency drops even more significantly during anesthetic-induced loss of consciousness.

[0043] The measurement is performed as follows:

[0044] a)EEG derivation:

[0045] In the perioperative EEG determination, the patient is still awake before the anesthesiologist first administers the drug, and the EEG electrodes are fixed to the patient. For this purpose, the forehead and temples are thoroughly disinfected and the skin oil is removed. This measure improves the conductivity of the skin and thus ensures that the derivation of the EEG signal is not disturbed. Then the ready-made EEG self-adhesive electrodes manufactured by Masimo (4248RD SEDLine sensor, single patient use, non-sterile) are fixed to the forehead of the prepared skin area, where the EEG electrodes are located at F7, F8, FP1 and FP2 respectively according to the 10 / 20 system, with Pfz as the reference electrode. Figure 5 The corresponding positions are shown in . During derivation, the impedance of each electrode was below 5 kΩ and the sampling rate was 250 Hz. The bandpass filter was preset to 0.5-40 Hz.

[0046] After connecting the self-adhesive electrodes to an EEG-based brain function monitor ("SEDLine Monitor" by Masimo Corporation, Irvine, California), a continuous 4-channel EEG was derived and recorded. The patient was still awake at this time, and therefore, the first derived value corresponded to baseline activity. In order to determine specific time points during the EEG derivation, "event markers" were manually entered into the EEG during the EEG recording. During this process, the anesthesiologist began to administer medication. This time point was marked as the event marker for "start of anesthesia." All patients were given an intravenous injection of the drug propofol to start anesthesia. The patient's state of consciousness was continuously checked by the eyelid reflex, and in the absence of the eyelid reflex, loss of consciousness was assumed and an event marker for "loss of consciousness" was set. This process allows the data to be accurately evaluated to the second.

[0047] b)EEG assessment

[0048] The following data were recorded by the SEDLine monitor: spectral cutoff frequency (SEF), anesthetic index (PSI), artifact level, and electromyographic activity. These EEG data were manually exported from the SEDLine monitor and displayed in an Excel sheet on a computer in digital format.

[0049] With a SEDLine recording rate of 30 values / minute, all measured values ​​are available every two seconds. First, each patient is checked to see whether the examined "baseline", "start of anesthesia" and "loss of consciousness" time points have been completely plotted and are free of artifacts. For this purpose, the artifact level calculated by the instrument and the EMG artifacts plotted in the same way are checked. The available data record is divided into 20-second time frames, one for "loss of consciousness". In each time frame lasting from 200 seconds before "loss of consciousness" to 200 seconds after "loss of consciousness", the artifact-free spectral cutoff frequencies of the right and left cerebral hemispheres of each patient are averaged.

[0050] The power spectrum for determining the spectral cutoff frequency is thus determined within a 20-second EEG signal time frame, with an update every 2 seconds. The calculation is performed by digital computer-assisted EEG signal processing. The basis for this calculation is a spectral analysis of the raw EEG by means of a fast Fourier transform, from which the power proportions for each time frame currently to be analyzed can be calculated.

[0051] To first determine if there were differences between the examination of the right and left hemispheres, the values ​​of all right-handed patients (n=36) before drug administration (-200 seconds), at the time point of loss of consciousness (0 seconds), and after "introduction" (+200 seconds) were used for a two-sided t-test on dependent samples. Since there were no significant differences at any time point (p=0.26 before drug administration, p=0.940 during loss of consciousness, p=0.44 after introduction), in another process, the relevant means were applied to the right and left hemispheres.

[0052] All variables were tested for normal distribution. To this end, histograms and quantile (QQ) plots were each visually examined and data were analyzed by Lilliefors test, Shapiro-Wilk test and values ​​were analyzed for skewness and kurtosis.

[0053] To describe the introduction, manipulation, and removal, descriptive statistics were evaluated for mean and standard deviation and examined for the times indicated by EEG-derived event markers.

[0054] To determine the occurrence of anesthesia-induced loss of consciousness with one-way ANOVA ("analysis of variance") and associated post hoc tests, the responses of the spectral cutoff frequencies were examined.

[0055] In order to examine the spectrum cutoff frequency of the time period from 200 seconds before the occurrence of anesthesia-induced loss of consciousness to 200 seconds after the occurrence of anesthesia-induced loss of consciousness for the possible influence of the intubation time point, the start of intubation was related to the time point from 20 seconds after "loss of consciousness" to 200 seconds after "loss of consciousness" using the correlation according to Pearson. In order to show the response without the possible influence of intubation, the research population was divided into two groups. Patients who started intubation within 200 seconds after "loss of consciousness" represented one group, and patients who started intubation after more than 200 seconds represented another group. Then the variance was checked for equality using the Levene test, and then the significant differences between the groups were tested using the t-test for independent samples. Since only those situations in which intubation was performed only after the time period considered were presented, intubation may no longer be a possible interference factor.

[0056] Figure 2 The average value determined during the study for the spectral cutoff frequency (SEK) as a function of time is shown. In this case, the average value of the spectral cutoff frequency is given in Hz at the time of loss of consciousness during the time period from 200 seconds before loss of consciousness to 280 seconds after loss of consciousness. Loss of consciousness (LOC) is indicated here as "00" on the time axis.

[0057] The following table indicates Figure 2 Values ​​of a chart in , with a specific standard deviation:

[0058]

[0059] A clear negative peak can be seen, which has a minimum value at the time point of loss of consciousness (time point "00"). In this case, the spectrum frequency average value drops from a value of about 12-14 Hz to about 8 Hz, and then increases to about 12-13 Hz. The width of the negative peak is about 2 minutes. By determining the minimum value of the negative peak, the occurrence of loss of consciousness can be determined with an accuracy of 20 seconds or less.

[0060] According to the invention, the determined correlation is evaluated electronically or using a computer and the response of the spectral cutoff frequency of the EEG signal is determined using the correlation and the absolute minimum of the spectral cutoff frequency in the response is determined as a parameter for the occurrence of anesthesia-induced loss of consciousness. In this case, the associated program can be integrated as a software tool in an EEG-based brain function monitor or electroencephalograph.

[0061] Figure 3A method for determining parameters indicative of loss of consciousness in a patient under anesthesia is shown. According to step 301, at least one forehead EEG signal is recorded on the patient. For example, according to the 10 / 20 system, four EEG signals are picked up by electrodes at positions F7, F8, FP1 and FP2, and the average value of these signals is given, with Fpz as the reference electrode.

[0062] According to step 302, the spectral cut-off frequency is continuously determined within the current time frame of the EEG signal. The determination is performed continuously, for example, a new determination of the spectral frequency is performed every 2 seconds or every 5 seconds. The length of the current time frame is, for example, 20 seconds, wherein said value is understood only by way of example.

[0063] In step 303, the response of the spectral cutoff frequency of the EEG signal is evaluated. This is performed over a time period that starts before the administration of anesthesia inducing drugs (e.g., propofol) and ends after the occurrence of anesthesia-induced loss of consciousness. In this case, the time period may or may not be established with respect to its duration. In the second case, for example, once a minimum value of the spectral cutoff frequency can be determined, the time period is ended.

[0064] In step 304, the absolute minimum of the spectral cut-off frequency is determined within the considered time period. This can be done, for example, by evaluating whether the spectral cut-off frequency has dropped to a value below 10 Hz, in particular below 9 Hz, and has increased again. Alternatively or in addition, for a limited number of measured values, it can also be evaluated whether the measured value of the spectral cut-off frequency is greater than the previous measured value. Thus, the negative peak of the spectral cut-off frequency is evaluated, wherein the absolute minimum of the spectral cut-off frequency is at the negative tip of the negative peak. Additional data analysis and a curve sketching method can be used to determine the absolute minimum of the spectral cut-off frequency with the highest possible accuracy.

[0065] Once the absolute minimum of the spectral frequencies has been determined, this information is provided as a parameter, for example acoustically and / or on a display of an EEG monitor, to indicatively indicate a loss of consciousness of the patient.

[0066] The doctor or anesthesiologist can use this information to accurately adapt the intubation to be performed to the individual state of consciousness of the patient, so that the intubation is not performed too early or too late, according to step 306. This provides greater safety for the patient when introducing the anesthetic.

[0067] To perform the method, an EEG-based brain function monitor or a computer can generally be used. The method steps for determining and evaluating the spectrum cut-off frequency and for determining the absolute minimum of the spectrum cut-off frequency are performed by a program code, which is run in a processor. The program code is stored in a memory of the processor or loaded in the memory before running. The processor running the program code can be the main processor of the EEG monitor, or an independent processor.

[0068] Figure 4 By way of example, a possible way of implementing such an EEG-based brain function monitor 1 is shown. 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 an EEG cable.

[0069] Via the interface 7, an EEG cable comprising EEG electrodes 61, 62 may be connected to the EEG monitor 1. Two EEG cables picking up EEG signals are depicted by way of example, whereby additional EEG cables may be provided to pick up multi-channel EEG signals.

[0070] The EEG signal is supplied to the microprocessor 2. The program code is stored in the memory 3 or can be loaded in the memory 3, and when the program code is run in the microprocessor 2, the program code executes the reference Figure 3 The method explained. The process can be controlled by a control device 4 and this process can be designed to obtain corresponding input commands. The control device 4 can be the main processor of the EEG monitor 1, or in this case can contain such a processor. Alternatively, the control unit 4 can take over the functions of the microprocessor 2. By means of the control unit 4 and / or additional modules (not shown), additional functions of the EEG monitor 1 can be implemented.

[0071] When executing the loaded program code, the microprocessor 2 thus determines the absolute minimum of the spectral cut-off frequency and the point in time at which this absolute minimum exists. The corresponding information is transmitted to the output unit 5 and output to the output unit. For example, this can be done by the monitor 51 and / or the acoustic unit 52.

[0072] Of course, the present invention is not limited to the above-described embodiments, and various modifications and improvements can be made without departing from the concepts described herein. Any feature can be used alone or in combination with any other feature when the feature is not mutually exclusive with any other feature, and the present disclosure extends to and includes all combinations and sub-combinations of one or more features described herein. Therefore, no matter how the range is defined, these ranges include all values ​​within these ranges and all sub-ranges within the range.

Claims

1. A memory having instructions stored thereon which, when executed, cause a processor to perform: - recording at least one EEG signal on the patient's head, wherein the EEG signal is recorded during a time period starting before an anesthesia inducing drug is administered to the patient and ending after the anesthetized patient loses consciousness, - determining a spectral cutoff frequency within the time period by spectral analysis based on the at least one EEG signal, wherein the spectral cutoff frequency indicates a frequency at which 95% of the total power is contained in the power spectrum, - determining an absolute minimum value of the spectral cut-off frequency within the time period, wherein a negative peak in the spectral cut-off frequency exists at the absolute minimum value, and - providing a parameter indicative of loss of consciousness of the patient based on the absolute minimum, wherein the parameter is related to a point in time within the time period in which the absolute minimum occurs, Therein once the absolute minimum has been determined, information is provided regarding the point in time at which the absolute minimum has been reached.

2. The memory according to claim 1, characterized in that: At least one frontal EEG signal is picked up.

3. The memory according to claim 2, characterized in that: A plurality of frontal EEG signals are picked up which are averaged before determining the spectral cutoff frequency.

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

5. The memory according to any one of claims 1 to 3, characterized in that: The spectral cut-off frequency is continuously determined by re-determination at least every 30 seconds.

6. The memory according to any one of claims 1 to 3, characterized in that: The spectral cutoff frequency is continuously determined by re-determination at least every 10 seconds.

7. The memory according to any one of claims 1 to 3, characterized in that: The spectral cutoff frequency is continuously determined by re-determination at least every 2 seconds.

8. The memory according to claim 1, characterized in that: If the spectral cut-off frequency has fallen to a value below 10 Hz and has increased again, the information about the point in time at which the absolute minimum has been reached is provided.

9. The memory according to claim 8, characterized in that: If the spectral cut-off frequency has fallen to a value below 9 Hz and has increased again, the information about the point in time at which the absolute minimum has been reached is provided.

10. The memory according to claim 8 or 9, characterized in that: If the measured value of the spectral cut-off frequency is greater than a previous measured value for a defined number of measured values, the information about the point in time at which the absolute minimum has been reached is provided.

11. The memory according to any one of claims 1 to 3, characterized in that: The spectral cut-off frequency of the EEG signal is determined after filtering this signal by a bandpass filter.

12. The memory according to any one of claims 1 to 3, characterized in that: The spectral cut-off frequency is determined by spectral analysis, in each case evaluating parallel time frames of the EEG signal.

13. An apparatus for providing a parameter indicative of loss of consciousness in a patient under anesthesia, wherein the apparatus comprises: - a member designed to record at least one EEG signal on the head of the patient, wherein the EEG signal is recorded during a time period starting before anesthesia inducing drugs are administered to the patient and ending after the anesthetized patient loses consciousness, - a means designed to determine, by spectral analysis, a spectral cutoff frequency within said time period based on at least one EEG signal, wherein said spectral cutoff frequency indicates a frequency at which 95% of the total power is contained in the power spectrum, - means designed to determine an absolute minimum value of said spectral cut-off frequency within said time period, wherein a negative peak of said spectral cut-off frequency exists at said absolute minimum value, and - means designed to provide, based on the absolute minimum, a parameter indicative of a patient's loss of consciousness, wherein the parameter is related to a point in time within a time period in which the absolute minimum occurs, Wherein said means (2) designed to provide information about said point in time at which said absolute minimum has been reached is designed to provide said information once said absolute minimum has been determined.

14. The device according to claim 13, characterized in that The means (2) designed to continuously determine the spectral cut-off frequency within a current time frame of the EEG signal is designed to redetermine the spectral cut-off frequency at least every 30 seconds.

15. The device according to claim 13, characterized in that The means (2) designed to continuously determine the spectral cut-off frequency within a current time frame of the EEG signal are designed to redetermine the spectral cut-off frequency at least every 10 seconds.

16. The device according to claim 13, characterized in that The means (2) designed to continuously determine the spectral cut-off frequency within a current time frame of the EEG signal is designed to redetermine the spectral cut-off frequency at least every 2 seconds.

17. The device according to claim 13, characterized in that The means (2) designed to provide information about the point in time at which the absolute minimum has been reached are designed to provide said information if the spectral cut-off frequency has dropped to a value below 10 Hz and has increased again.

18. The device according to claim 17, characterized in that The means (2) designed to provide information about the point in time at which the absolute minimum has been reached is designed to provide said information if the spectral cut-off frequency has dropped to a value below 9 Hz and has increased again.

19. The device according to claim 17 or 18, characterized in that The means (2) designed to provide information about the point in time at which the absolute minimum has been reached are designed to provide said information if, for a defined number of measured values, the measured value of the spectral cut-off frequency is greater than the previous measured value.

20. The device according to any one of claims 13 to 18, characterized in that The means (2) designed to continuously determine the spectral cut-off frequency comprise means designed to perform a spectral analysis, in each case evaluating parallel time frames of the EEG signal.

21. The device according to any one of claims 13 to 18, characterized in that The means (2) designed to continuously determine the spectral cut-off frequency is designed to determine the spectral cut-off frequency of the EEG signal after filtering this signal by means of a bandpass filter.

22. The device according to any one of claims 13 to 18, characterized in that The member (2) designed to record at least one EEG signal on the patient's head is designed to pick up at least one forehead EEG signal.

23. The device according to claim 22, characterized in that The means (2) designed to record at least one EEG signal on the patient's head is designed to receive a plurality of forehead EEG signals which are averaged before determining the spectral cut-off frequency.

24. An EEG anesthesia monitor (1) comprising the device according to claim 13.

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