Methods and devices for providing parameters indicating an increased probability of postoperative delirium

Abstract

The present invention relates to a method and apparatus for providing parameters indicating an increased probability of postoperative delirium. According to a first invention aspect, there are the steps of: detecting (401) at least one EEG signal in the patient's head; determining (402) the intraoperative alpha peak frequency of the EEG signal, wherein the alpha peak frequency in the power spectrum of the EEG signal is the frequency with the highest power in the alpha frequency band; checking (403) whether the measured intraoperative alpha peak frequency is significantly lower than a preset reference value of the alpha peak frequency; and, for the situation, providing (404) corresponding information as a parameter indicating an increased probability of postoperative delirium. A second invention aspect evaluates the power change of the alpha frequency band after the use of anesthesia-inducing drugs. A third invention aspect relates to the determination of the average amplitude of the "DC" EEG signal and its curve at the beginning of the anesthesia-induced unconsciousness.

Description

Technical Field

[0001] The present invention relates to methods and apparatus for providing parameters indicative of an increased probability of developing postoperative delirium. Background Art

[0002] Postoperative delirium (POD) is a common postoperative complication that occurs more frequently in the elderly population and is associated with prolonged hospital stays, cognitive loss, decreased quality of life after surgery, and increased mortality up to one year after surgery. Risk profiles (older age, preoperative cognitive decline, etc.) are evaluated to predict which patients are at greater risk of developing POD. However, the creation of risk profiles is not specific and does not correspond to each patient's actual individual risk.

[0003] The use of surface electrodes in electroencephalography (EEG) to derive brain waves was first proposed at the beginning of the last century. Early visual assessment of EEG oscillations focused on the evaluation of epilepsy potential, sleep stage classification and coma depth, as well as brain death detection. The introduction of computer-based EEG analysis software has greatly improved the evaluation and interpretation of diagnoses. The use of EEG data for anesthesia control is common practice.

[0004] In the electroencephalogram, a distinction is made between different EEG frequency bands. In this patent application, the following definitions are applied to the EEG frequency bands: direct current EEG: <0.5 Hz; sub-delta band: 0.5-1.5 Hz; delta band: 1.5 to 4 Hz; theta band: 4 to <8 Hz; alpha band: 8 to 12 Hz; beta band: >12 to 35 Hz; gamma band: >35 Hz.

[0005] In addition, it is known that each alpha frequency can be characterized by measuring the so-called alpha peak frequency (aFP). The peak frequency refers to the frequency with the maximum power in the alpha band, and in the power spectrum of the EEG signal, it refers to a frequency at which the power or amplitude squared in the alpha band is the largest. Gu Y, Chen J, Lu Y, Pan S. reported in "Integrative Frequency power of EEG correlates with progression of mild cognitive impairment to dementia in Parkinson's disease" in Volume 47, Issue 2 of ClinEEG Neurosci in 2016, pages 113-117, that the alpha peak frequency is related to cognitive ability.

[0006] F.M.Radtke, M.Franck, J.Lendner, S.Krüger, K.D.Wernecke, and CD.Spies described in "Monitoring depth of anaesthesia in a randomized trial decreases the rate of postoperative delirium but not postoperative cognitive dysfunction" in the supplement to Volume 110 of BJA: British Journal of Anaesthesia on June 1, 2013, pages i98-i105 that neurological monitoring of anesthetic depth supported by EEG can significantly reduce the frequency of postoperative delirium. In this case, the depth of anesthetic is measured with the help of EEG evaluation so that anesthesia can be implemented more accurately, thereby reducing the frequency of postoperative delirium. Summary of the invention

[0007] It is an object of the present invention to provide methods and devices which provide information enabling a physician or anesthesiologist to better assess the probability of the occurrence of postoperative delirium.

[0008] The present invention provides a method and a device to achieve the above-mentioned purpose.

[0009] In view of this, the present invention provides a method in a first inventive aspect, which provides parameters indicating an increased probability of postoperative delirium. According to the method, at least one EEG signal is detected in the patient's head. The intraoperative alpha peak frequency of the EEG signal is measured, wherein the alpha peak frequency in the power spectrum of the EEG signal is the frequency with the highest power in the alpha frequency band. In addition, it is checked whether the measured intraoperative alpha peak frequency is significantly (i.e. in a defined manner) lower than a preset reference value of the alpha peak frequency. As will be explained below, the reference value refers, for example, to the average alpha peak frequency of a reference patient group that has not developed postoperative delirium.

[0010] In the case where the intraoperative alpha peak frequency is significantly lower than a preset reference value, corresponding information is provided as a parameter, wherein the parameter indicates an increased probability of postoperative delirium. The doctor can use the parameter in combination with other parameters to decide whether to provide early treatment to the patient.

[0011] It is noted that the step of checking whether the intraoperative alpha peak frequency is significantly below a preset reference value for the alpha peak frequency includes finding a difference between the preset reference value and the intraoperative alpha peak frequency. If the difference meets certain criteria, such as being greater than a certain preset value, then the intraoperative alpha peak frequency is "significantly" below the preset reference value. It is also noted that the parameter that indicates an increased probability of postoperative delirium also indicates that the intraoperative alpha peak frequency is significantly below the preset reference value.

[0012] It should also be pointed out that according to an embodiment of the present invention, the check on whether the measured intraoperative alpha peak frequency is significantly lower than a preset reference value can be implemented by machine learning, wherein the artificial system learns from examples of significantly lower intraoperative alpha peak frequencies and can generalize these examples after the learning phase, wherein patterns and regularities in the learning data can be identified.

[0013] The present invention is based on the surprising recognition that the intraoperatively measured alpha peak frequency or its deviation from a reference value can be used to provide a parameter that can be used to predict or at least better assess the development of postoperative delirium. This allows for early therapeutic interventions and thus prevents or reduces some of the serious subsequent complications seen in patients with postoperative delirium.

[0014] This is an instrument-based examination that can be easily implemented in daily clinical work, so that the diagnostic reliability can be greatly increased by the invention. According to the invention, an alarm can be issued even during the operation, so that auxiliary treatment measures can be introduced. The patient can also be treated accordingly in the recovery room and other nursing stations, or further curves can be carefully monitored.

[0015] According to a technical solution of the present invention, the preset reference value is measured by taking the average value of the intraoperative alpha peak frequency of multiple patients who have not developed postoperative delirium. Among them, the reference value can be preset according to the age group of the patient. Therefore, for example, a first reference value is determined for a first age group (for example, 65-70 years old), specifically, the average value of the intraoperative alpha peak frequency measured for multiple patients in this first age group is taken. For example, a second reference value is determined for a second age group (for example, 70-80 years old), specifically, the average value of the intraoperative alpha peak frequency measured for multiple patients in this second age group is taken. The probability of correctly predicting the occurrence of postoperative delirium is increased by selecting a reference value according to a specific age group. However, it should be pointed out that the determination of the reference value according to the age group is only one embodiment of the present invention.

[0016] In other technical solutions, the preset reference value can be determined by considering the intraoperative alpha peak frequencies measured for a plurality of patients who did not develop postoperative delirium and additionally considering the intraoperative alpha peak frequencies measured for a plurality of patients who developed postoperative delirium. In this way, the typical frequency interval of the alpha peak frequencies between patients who developed postoperative delirium and patients who did not develop postoperative delirium can be taken into account when determining the reference value.

[0017] According to another technical solution of the present invention, the intraoperative alpha peak frequency is measured at a time point when the patient is under stable anesthesia. For example, the intraoperative alpha peak frequency is measured 15 minutes after the intubation or loss of consciousness. However, these values ​​are only for example.

[0018] The intraoperative alpha peak frequency of a patient can be measured multiple times within a defined intraoperative time window and averaged.

[0019] As described above, according to the method of the present invention, it is checked whether the intraoperative alpha peak frequency is significantly lower than the preset reference value of the alpha peak frequency, and corresponding information is output as a parameter for this situation. For example, in the case where the difference between the preset reference value and the intraoperative alpha peak frequency exceeds the defined deviation calculated as a percentage from the reference value or the defined absolute difference between the intraoperative alpha peak frequency and the reference value, there is a situation where the alpha peak frequency is significantly lower. In the first case, for example, it is checked whether the intraoperative alpha peak frequency deviates from the reference value by at least X%, such as at least 10%. In the second case, it is checked whether the frequency difference between the intraoperative alpha peak frequency and the reference value exceeds a predefined value, such as 1 Hz.

[0020] According to the invention, it is preferred to record the EEG signals of the frontal lobe, i.e. to implement a frontal derivation, wherein the EEG signals are measured at at least two electrodes arranged at different positions on the patient's forehead. A plurality of frontal lobe EEG signals can be recorded and their average value is taken to determine the alpha peak frequency. In the commonly used 10-20 system, the signals are derived from electrodes positioned at positions F7, F8, Fp1, Fp2 and Fpz.

[0021] Either a bipolar derivation (difference of two active electrodes) or a monopolar derivation (difference of multiple active electrodes relative to a common reference) can be implemented.

[0022] According to another embodiment, the intraoperative alpha peak frequency is determined on the EEG signal after the EEG signal has been filtered by a bandpass filter. The bandpass filter is designed, for example, so that only signals in the frequency range of 0.5-40 Hz can pass through it.

[0023] According to another technical solution, when determining the intraoperative alpha peak frequency of the EEG signal, the frequency with the largest power in the EEG signal power spectrum in the alpha band and the theta band is determined. The intraoperative alpha peak activation is usually performed in the alpha band. However, in patients who develop postoperative delirium after surgery (POD patients), the peak of the frequency is also in the upper theta band, where the theta band extends between 4 and 8 Hz. Within the scope of the present invention, this peak of the frequency in the upper theta band is also called the alpha peak.

[0024] The present invention provides a method in a second inventive aspect, which provides another parameter indicating an increased probability of postoperative delirium. According to the method, at least one EEG signal is detected in the patient's head. The power of the alpha band of the EEG signal is measured, wherein the power of the alpha band is defined as the integral of the power in all frequency ranges in the alpha band in the power spectrum of the EEG signal. That is, the power of the EEG signal in all frequency ranges in the alpha band is measured.

[0025] A first power in the alpha frequency band is measured at a preoperative time point before the administration of anesthesia inducing drugs, and a second power in the alpha frequency band is measured at an intraoperative time point after the onset of anesthesia-induced unconsciousness. Subsequently, it is checked whether the increase in the power in the alpha frequency band from the first power to the second power is less than a predefined amplitude. In this case, corresponding information is provided as a parameter showing an increased probability of postoperative delirium.

[0026] The second inventive aspect is mainly based on the following basic fact: during anesthesia, the power of the alpha band increases. This is mainly related to the intraoperative activation of GABA neurons in the frontal lobe area, which leads to alpha activation. The present invention now reveals the following unexpected correlation: the increase in the power of the alpha band under anesthesia is smaller in patients who develop postoperative delirium than in patients who do not develop postoperative delirium. In other words, in patients who develop postoperative delirium, the increase in the alpha band power from the first power (preoperative) to the second power (intraoperative) is smaller than the predefined amplitude (also in extreme cases, the alpha band power may even decrease from preoperative to intraoperative).

[0027] It should also be pointed out that, according to an embodiment of the present invention, the check whether the increase in alpha band power from a first power to a second power is less than a predefined amplitude can be implemented by machine learning, wherein the artificial system learns from examples of reduced increase amplitudes in alpha band power, and these examples can be generalized after the learning phase, wherein patterns and regularities in the learning data can be identified.

[0028] For the second invention aspect, since it is an instrument-based examination that can be easily implemented in daily clinical work, the diagnostic reliability can be greatly improved. An alarm can be issued during the operation, so that auxiliary treatment measures can be introduced. The patient can also be treated accordingly in the recovery room and other nursing stations, or further curves can be carefully monitored.

[0029] The second inventive aspect may be implemented independently of the first inventive aspect, or in combination therewith.

[0030] According to a technical solution of the second invention, the increase of the alpha band power from the first power to the second power must be lower than a predefined amplitude to provide information, and the predefined amplitude is determined by the following method:

[0031] - for a plurality of patients who did not develop postoperative delirium, comparing the average first power in the alpha frequency band with the average second power in the alpha frequency band, wherein a first reference value is formed,

[0032] - comparing, for a plurality of patients who developed postoperative delirium, the average first power in the alpha frequency band with the average second power in the alpha frequency band, wherein a second reference value is formed,

[0033] - forming the predefined amplitude based on a difference or a ratio between the first reference value and the second reference value.

[0034] Therefore, the difference in alpha band power between preoperative and intraoperative in the observed patients was evaluated based on the reference value to see if there were conditions that increased the probability of postoperative delirium, and a relevant "predefined amplitude" was formed on the basis of the reference value. The reference value was determined in patients who did not develop postoperative delirium and those who developed postoperative delirium.

[0035] To form the first and second reference values, the average first power of the alpha band may be compared with the average second power of the alpha band, for example by taking the difference or quotient of these values.

[0036] According to one technical solution, the predefined amplitude is formed by the difference between the first reference value and the second reference value plus a percentage-based or absolute tolerance value. The percentage-based tolerance value may correspond to a standard deviation, for example.

[0037] According to another technical solution, the predefined amplitude is determined according to the age group of the patient. For example, a reference value for a first age group (e.g., 65-70 years old) and a reference value for a second age group (e.g., 70-80 years old) are determined for two groups. The reference value of the age group is used for the observed patient, thereby increasing the probability of correctly predicting the occurrence of postoperative delirium.

[0038] According to another embodiment, the first power in the alpha band, the second power in the alpha band and the increase in the alpha band from the first power to the second power are determined in dB, and the increase in the alpha band power from the first power to the second power must be below a certain amplitude in order to provide and check the information, which amplitude is also given in dB. The corresponding dB values ​​of the power are directly output by modern EEG anesthesia monitors and can therefore be obtained in a simple manner.

[0039] According to one embodiment, the increase in the alpha band power from the first power to the second power must be less than 15 dB, in particular less than 12 dB, in order to provide information or generate a parameter.

[0040] Similarly, for the second invention aspect, according to a technical solution, after the EEG signal is filtered by a bandpass filter, the power of the alpha frequency band is determined on the signal. Preferably, at least one frontal lobe EEG signal is detected on the patient's head, wherein multiple frontal lobe EEG signals of the patient can be detected and the average value is taken to determine the power of the alpha frequency band. For example, the signal is derived from electrodes located at positions F7, F8, Fp1, Fp2 and Fpz in the 10-20 system.

[0041] The present invention provides a method in a third inventive aspect, which provides another parameter indicating an increased probability of postoperative delirium. According to the method, at least one EEG signal is detected in the patient's head, wherein only the EEG signal frequency range less than 0.5 Hz is detected or observed. Signals within this frequency range are also called "direct current EEG" or "DC-EEG" because the signal changes very slowly. According to one technical solution, all signals with a frequency less than 0.5 Hz are detected. In an alternative technical solution, the observed frequency range can be further limited, for example to signals with a frequency less than 0.1 Hz.

[0042] According to the method of the third inventive aspect, the average amplitude of the EEG signal in the current time window of the EEG signal is determined. The observed time window observes the EEG signal within a time period spanning a defined time span from the current time, such as the EEG signal within a time period of two minutes or one minute or within the last 30 seconds, 20 seconds, 10 seconds or 2 seconds. In this regard, the scheme relates to the determination of amplitude measurements in a time window that varies with time or within a certain time range. The determination of the average amplitude can be performed continuously or at specific intervals.

[0043] A curve of the mean amplitude of the EEG signal measured between a preoperative time point before the administration of anesthesia-inducing drugs and an intraoperative time point after the onset of anesthesia-induced unconsciousness. In particular, the mean amplitude of the EEG signal is measured at the onset of anesthesia-induced unconsciousness. For example, the onset of loss of consciousness (LOC) can be accurately detected during anesthesia by means of the corneal reflex.

[0044] In addition, it is checked whether the average amplitude of the EEG signal increases by more than a predefined amplitude at the onset of anesthesia-induced unconsciousness. In this case, corresponding information is provided or output as a parameter showing an increased probability of postoperative delirium.

[0045] Subsequently, ie after the onset of anesthesia-induced unconsciousness, the mean amplitude decreases further, so that the mean amplitude of the EEG signal has a peak at the onset of anesthesia-induced unconsciousness and is accordingly easily detectable by the measuring technique.

[0046] The third inventive aspect is based on the surprising realization that patients whose average amplitude of the "direct current" EEG signal (i.e., the signal component of the EEG signal with a frequency less than 0.5 Hz) at the time of the onset of loss of consciousness have a significantly higher probability of developing postoperative delirium, whereas in patients who do not develop postoperative delirium, the average amplitude of the "direct current" EEG signal does not increase or does not increase significantly at the time of the onset of loss of consciousness.

[0047] The third inventive aspect may be implemented independently of the first and second inventive aspects, or in combination with one or both of them.

[0048] To detect “DC” EEG signals, the EEG signal can be passed through a low-pass filter with a cutoff frequency of 0.5 Hz.

[0049] According to a technical solution of the third invention aspect, the increase in the average amplitude of the EEG signal at the onset of anesthesia-induced unconsciousness must be higher than a certain predefined amplitude to provide information, and the predefined amplitude is determined in the following manner: for a plurality of patients who have not developed postoperative delirium, the average amplitude of the EEG signal at the onset of anesthesia-induced unconsciousness is determined, wherein a first reference value is formed. In addition, for a plurality of patients who have developed postoperative delirium, the average amplitude of the EEG signal at the onset of anesthesia-induced unconsciousness is determined, wherein a second reference value is formed. The predefined amplitude is formed based on the difference or ratio between the first reference value and the second reference value. For example, the second reference value must be greater than the first reference value by at least a factor of 3, in particular by at least a factor of 5, in particular by at least a factor of 10, thereby exceeding the predefined amplitude.

[0050] The predefined amplitude may be formed by subtracting a percentage-based or absolute tolerance value from the difference between the first reference value and the second reference value. In addition, the predefined amplitude may be determined according to the age group of the patient.

[0051] In said third inventive aspect, according to one embodiment, the EEG signal is derived at an electrode positioned at position Cz (i.e., center) in the 10-20 system and at a reference electrode, e.g., at the ear. To implement this central EEG derivation, the optimal signal is detected experimentally. Alternatively, however, the derivation can also be implemented in the frontal lobe, e.g., by electrodes positioned at positions F7, F8, Fp1, Fp2, and Fpz.

[0052] The method of the present invention is implemented automatically, in particular, by a computer program. When the computer program is run on a computer, the computer program contains, for example, program code for implementing the method according to the present invention and / or program code for implementing the method according to the present invention and / or program code for implementing the method according to the present invention.

[0053] In another aspect, the present invention relates to a device for providing a parameter indicative of an increased probability of postoperative delirium, wherein the device comprises:

[0054] - means for detecting at least one EEG signal in the head of a patient,

[0055] - means for determining an intraoperative alpha peak frequency of an EEG signal, wherein the alpha peak frequency in the power spectrum of the EEG signal is the frequency with the highest power in the alpha frequency band,

[0056] - means for checking whether the measured intraoperative alpha peak frequency is significantly lower than a preset reference value of the alpha peak frequency,

[0057] - means for providing corresponding information as a parameter showing an increased probability of occurrence of postoperative delirium.

[0058] In another aspect, the present invention relates to a device for providing a parameter indicative of an increased probability of postoperative delirium, wherein the device comprises:

[0059] - means for detecting at least one EEG signal in the head of a patient,

[0060] - means for determining the power of the alpha band of the EEG signal, wherein the power of the alpha band is defined in the power spectrum of the EEG signal as the integral of the power in all frequency ranges in the alpha band,

[0061] - wherein the first power in the alpha frequency band is measured at a preoperative time point before the administration of anesthesia inducing drugs, and the second power in the alpha frequency band is measured at an intraoperative time point after the onset of anesthesia-induced unconsciousness,

[0062] - means for checking whether an increase in alpha band power from a first power to a second power is less than a predefined magnitude,

[0063] - means for providing corresponding information as a parameter showing an increased probability of occurrence of postoperative delirium.

[0064] In another aspect, the present invention relates to a device for providing a parameter indicative of an increased probability of postoperative delirium, wherein the device comprises:

[0065] - means arranged and configured to detect at least one EEG signal in the head of a patient, wherein only a frequency range of the EEG signal below 0.5 Hz is detected or observed,

[0066] - means for determining an average amplitude of the EEG signal in a current time window of the EEG signal,

[0067] - means arranged and configured to determine a curve of the mean amplitude of the EEG signal between a preoperative time point before the administration of anesthesia inducing drugs and an intraoperative time point after the onset of anesthesia-induced unconsciousness,

[0068] - wherein the mean amplitude of the EEG signal is determined at the onset of anesthesia-induced unconsciousness,

[0069] - means arranged and configured to check whether the average amplitude of the EEG signal increases by more than a predefined amplitude at the onset of anaesthesia-induced unconsciousness, and

[0070] - is arranged and configured to provide corresponding information for this situation as a means of indicating a parameter with an increased probability of occurrence of postoperative delirium.

[0071] The above components can be implemented by a microprocessor in combination with program codes run by the microprocessor. For example, the above parameters can be displayed on a monitor.

[0072] Furthermore, the device is designed and configured to carry out the specified method steps and has corresponding components.

[0073] In another aspect of the invention, the present invention relates to an EEG anesthesia monitor having a device according to the invention. Thus, the device according to the invention is integrated in an EEG anesthesia monitor, wherein the EEG anesthesia monitor is configured and constructed to analyze EEG data in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] The present invention is described in detail below with reference to the accompanying drawings and in combination with a plurality of embodiments.

[0075] Figure 1 exemplarily showing EEG signals in the awake state and after anesthesia-induced unconsciousness as time-dependent signals and in the power spectrum;

[0076] Figure 2 The intraoperative alpha peak frequency of the patient group that did not develop postoperative delirium (nonPOD) and the patient group that developed postoperative delirium (POD) in the power spectrum is exemplarily shown;

[0077] Figure 3 exemplarily showing the difference between the power of the alpha frequency band before and during surgery measured in dB, wherein the power of the alpha frequency band is measured both for a group of patients who did not develop postoperative delirium (nonPOD) and for a group of patients who developed postoperative delirium (POD);

[0078] Figure 4 A first method of providing a parameter indicative of an increased probability of postoperative delirium is shown;

[0079] Figure 5 A second method of providing a parameter indicative of an increased probability of postoperative delirium is shown;

[0080] Figure 6 The average amplitude of the direct current EEG signal at the time point before induction of anesthesia (1), the time point when loss of consciousness begins (2), and the time point after loss of consciousness begins (3) are exemplarily shown, wherein the average amplitude of the patient group that develops postoperative delirium (POD) increases significantly at the time point when loss of consciousness begins;

[0081] Figure 7 A third method of providing a parameter indicating an increased probability of postoperative delirium is shown;

[0082] Figure 8 An example is shown for implementing Figure 4 and / or an apparatus of the method of 5 and / or 7; and

[0083] Fig. 9 Shown are positioning points for EEG electrodes in the 10-20 system. DETAILED DESCRIPTION

[0084] The present invention includes three inventive schemes, one of which observes the measurement of the patient's intraoperative alpha peak frequency, the second inventive scheme observes the measurement of the difference between the patient's preoperative and intraoperative alpha frequency band power, and the third inventive scheme observes the measurement of the average amplitude and curve of the "DC" EEG signal at the beginning of anesthesia-induced unconsciousness.

[0085] Combination Figure 1 and 2 The association first discovered in the first invention scheme is explained, and the association has been proven by research. Figure 1 and 3 The association first discovered in the second invention scheme is explained, and the association has been proven by research. Figure 6 The connection discovered for the first time in the third invention scheme is described, and this connection has been confirmed by research.

[0086] To illustrate the background of the invention, Figure 1 The upper diagram shows the EEG signal that occurs in a patient in the awake state ("baseline"). The signal is shown both as a time signal (left) and as a power spectrum after spectral analysis (right). In the power spectrum, the power (amplitude squared) μV is plotted. 2 The relationship curve between frequency Hz. The power spectrum reflects the corresponding component of each frequency range in the total power component of the original signal.

[0087] Figure 1 The lower view ("anesthesia") shows the EEG signal under anesthesia. It can be seen that the spectrum is overall shifted to the left relative to the value of awake patients. Specifically, under anesthesia, the frequencies of delta, theta and alpha bands increase, while beta and gamma waves decrease. Among them, Purdon PL, Pierce ET, MukamelEA, Prerau MJ, Walsh JL, Wong KFK, Salazar-Gomez AF, Harrell PG, SampsonAL, Cimenser A, Ching S, Kopell NJ, Tavares-SToeckel C, Habeeb K, Merhar R, Brown E. pointed out in the 2013 PNAS Volume 110, Issue 12, "Electroencephalogram signatures of loss and recovery of consciousness from propofol" E1142-1151 that in the case of deep unconsciousness induced by GABA-activated anesthetics, characteristic frontal alpha band activation is mainly caused.

[0088] Figure 2The intraoperative alpha peak frequency for the group of patients who did not develop postoperative delirium and the group of patients who developed postoperative delirium is shown in a power spectrum plotting the relationship between power dB and frequency. The alpha peak frequency is defined as the frequency with the highest power in the alpha band. Alternatively, the theta band or the upper part of the theta band (between 6 and 8 Hz) can also be observed, that is, the alpha peak frequency is defined as the frequency with the highest power in the alpha band and the theta band (or the upper region of the theta band).

[0089] In a prospective observational study, the following association was shown: the intraoperative alpha peak frequency was higher in patients who did not develop postoperative delirium (non-POD) after surgery than in patients who developed postoperative delirium (POD) after surgery. P1 is 10.1 Hz. In POD patients, the alpha peak frequency α P2 The standard deviation of the value 10.1 Hz is 0.77 Hz. The standard deviation of the value 8.8 Hz is 0.87 Hz.

[0090] This study was conducted in an age-matched patient cohort, including 11 POD patients and 11 non-POD patients. As a result, it can be seen that the intraoperative alpha peak frequency in POD patients was significantly lower than that in non-POD patients.

[0091] Figure 3 The difference between the power of the alpha band before and during surgery, measured in dB, is shown in a graph for a non-POD patient group and a POD patient group. In a prospective observational study, the following association was shown: in POD patients, the increase in the power of the alpha band from pre-operative to intra-operative was smaller than in non-POD patients. Figure 3 As shown in Figure 2, in non-POD patients, the average value of the difference Δ from intraoperative to preoperative was about 21 dB. In POD patients, this average value was about 10 dB and significantly lower. The standard deviation of non-POD patients was about 13 dB. The standard deviation of POD patients was about 11.5 dB. This standard deviation was also Figure 3 Shown in.

[0092] The study was conducted in a patient group including 19 POD patients and 35 non-POD patients aged over 65 years, in which anesthesia was induced with propofol, one of the most widely used anesthetics. Thus, it can be seen that the power increase of the EEG signal in the alpha band from pre-operative to intra-operative was significantly reduced in POD patients, and correspondingly, the power difference in the alpha band from intra-operative to pre-operative was significantly smaller in POD patients.

[0093] Figure 2 and 3 The measurements in are performed as follows:

[0094] a)EEG export:

[0095] Continuous intraoperative EEG was recorded from the start to the end of anesthesia using an EEG-based brain function monitor (“SEDLine Monitor” by Masimo, Irvine, CA). Surface EEG adhesive electrodes (4248RD SEDLine Sensor by Masimo, Single Use, Non-sterile) were installed at positions F7, F8, FP1, and FP2 corresponding to the 10 / 20 system, with Fpz as the ground electrode and a reference approximately 1 cm above Fpz, referred to Figure 8 To this end, the patient's forehead and temples were thoroughly disinfected and skin oils were removed. The impedance of each electrode was less than 5 kΩ, and the scanning frequency was 250 Hz.

[0096] After connecting the adhesive electrodes to the EEG-based brain function monitor, the extraction and recording of a continuous 4-channel EEG was started. The patient was still awake at this time, so that the first value corresponded to the extraction of baseline activity. In order to determine the clear time points during the EEG extraction, "event markers" were manually placed in the EEG during the EEG recording. Event markers: "Baseline" = awake patient before the use of anesthetics, "Start of anesthetics" = start of anesthetics, "Loss of consciousness" = lack of eyelid reflex, "ITN" = intubation of the patient, "OP" = intraoperative stabilization phase 15-30 minutes after ITN. All patients received intravenous drug propofol for induction of anesthesia, which was maintained by intravenous propofol or by inhaled anesthetics desflurane or sevoflurane. The recorded EEG data were exported from the SEDLine monitor.

[0097] b)EEG evaluation:

[0098] The original EEG data was equipped with a bandpass filter of 0.5-40 Hz (Brain Visual Analyzer Software). Subsequently, visual EEG data analysis was implemented, wherein a 10-second artifact-free EEG time window was selected at the time points "baseline" and "OP". The EEG data was segmented into "baseline" and "intraoperative" EEG. Other data analysis was implemented with the Chronux toolbox (Bookil et al., 2010) of Matlab (The MathWorks, Inc., Natick, Massachusetts, USA). The power spectrum within the range of all frequency bands (slow and fast delta, theta, alpha, beta) was calculated with the help of the Multitaper method with a time window of 2 seconds, an overlap of 1.9 seconds, a time-bandwidth product TW=3, a taper number k=5, and a spectral resolution of 2W=3Hz. This calculation was implemented with the help of computer-controlled digital EEG signal processing. Its basis is the original EEG spectrum analysis implemented with the help of fast Fourier transform, and the power component of the current time window to be analyzed can be calculated by fast Fourier transform.

[0099] The data were then converted to a decibel scale [Power (dB) = 10log10 (Power (μV))]. In order to better map the frontal EEG power, a pooled frontal electrode was calculated, into which the signals of electrodes Fp1, Fp2, F7 and F8 were pooled with equal weight.

[0100] Determined based on the measured spectrum Figure 2 The peak frequency (Hz) in the alpha band (8-12 Hz) (aPF) is shown. In addition, the Figure 3 Shown are the signal power differences in the alpha band between intraoperative and preoperative (difference between OP alpha band power and baseline alpha band power).

[0101] c) Delirium Screening:

[0102] After surgery, delirium scores were regularly calculated starting from admission to the recovery room. Postoperative delirium was defined according to the criteria of DSM V (Diagnostic and Statistical Manual of Mental Disorders). Nursing examination scores (NuDESC) were regularly calculated during the stay in the recovery room. All patients with a NuDESC score ≥ 2 at a certain time point during their stay in the recovery room were identified as patients with postoperative delirium (POD group), and patients with a NuDESC score ≤ 1 were identified as patients without postoperative delirium (NonPOD group).

[0103] d) Statistical evaluation:

[0104] Statistical calculations of alpha peak frequency and the difference between OP alpha band power and baseline alpha band power were performed using SPSS version 24 (Copyright SPSS, Inc., Chicago, IL 60606, USA) with the help of Mann-Whitney U test and Kruskal-Wallis test.

[0105] According to the invention, the measured correlation is evaluated electronically or computer-based and used to determine the increase in the power of the EEG signal in the alpha frequency band from preoperative to intraoperative or the intraoperative alpha peak frequency of the patient, wherein the auxiliary program can be integrated as a software tool in an EEG-based brain function monitor or electroencephalogram.

[0106] Figure 4 A first method for determining a parameter indicating an increased probability of postoperative delirium is shown. In step 401, at least one frontal lobe EEG signal of the patient is detected. For example, four EEG signals are recorded with Fpz as the reference electrode by electrodes at positions F7, F8, FP1 and FP2 corresponding to the 10 / 20 system, and the signals are averaged. Another ground electrode is slightly above Fpz.

[0107] Subsequently, in step 402, the intraoperative alpha peak frequency of the EEG signal is determined. This is performed at a point in time when the patient is under stable anesthesia, for example, about 15-30 minutes after the onset of unconsciousness. Next, it is checked whether the measured intraoperative alpha peak frequency is significantly lower than a preset reference value of the alpha peak frequency. Therein, the preset reference value is determined in advance by taking the average of the measured intraoperative alpha peak frequencies of multiple patients who have not developed postoperative delirium. Therein, the preset reference value matches the age group of the patient. In this case, the currently measured intraoperative alpha peak frequency is, for example, significantly lower than the preset reference value when the difference between the preset reference value and the measured intraoperative alpha peak frequency exceeds a defined deviation from the reference value calculated as a percentage or a defined absolute difference between the intraoperative alpha peak frequency and the reference value. According to Figure 2 The reference value was 10.1 Hz, and significant deviations were considered in cases where the measured alpha peak frequency had a value below 9.5 Hz.

[0108] In this case, in step 404, corresponding information is provided as a parameter showing an increased probability of postoperative delirium.

[0109] Figure 5A second method for determining a parameter indicating an increased probability of postoperative delirium is shown. In step 501, at least one frontal lobe EEG signal of the patient is re-detected. For example, four EEG signals are recorded with Fpz as the reference electrode, using electrodes at positions F7, F8, FP1 and FP2 corresponding to the 10 / 20 system, and the signals are averaged.

[0110] Subsequently, in step 502, the power of the alpha band of the EEG signal is determined, wherein the power of the alpha band is defined as the integral of the power within all frequency ranges in the alpha band in the power spectrum of the EEG signal. Thus, in step 503, a first power of the alpha band is determined at a preoperative time point before the administration of anesthesia inducing drugs, and a second power of the alpha band is determined at an intraoperative time point after the onset of anesthesia-induced unconsciousness.

[0111] Then, it is checked whether the increase of the alpha band power from the first power to the second power is less than a predefined amplitude. The predefined amplitude is determined, for example, based on reference values ​​measured in POD patients and non-POD patients. The predefined amplitude can be, for example, a certain dB value, which is the maximum value that the alpha band power can increase from preoperative to intraoperative, so that there is significance. Figure 3 The predefined amplitude is, for example, 15 dB, that is, if the power of the alpha band increases by less than 15 dB from before surgery to during surgery, it is considered significant. In this case, in step 505, corresponding information is provided as a parameter showing the occurrence of postoperative delirium.

[0112] Figure 6 The measured values ​​of the third inventive solution are shown, which observes the determination of the average amplitude of the "direct current" EEG signal and its curve at the beginning of anesthesia-induced unconsciousness. In this regard, it is first necessary to point out that commercially available EEG-based brain function monitors have a high-pass filter that can filter out signal frequency ranges less than 0.5 Hz. In contrast, the third embodiment observes a frequency range less than 0.5 Hz, which is also called "direct current" EEG or DC-EEG. For this purpose, the measured signal can, for example, pass through a low-pass filter with a limit frequency of 0.5 Hz.

[0113] The amplitude of the measured DC-EEG signal is continuously evaluated, wherein the mean value of the amplitude is calculated in the currently synchronized time window. This amplitude mean value needs to be distinguished from the mean value of the signal used to measure the amplitude. For example, the mean value of the signal is determined in the same time window or in a longer time window or over the entire measuring time range, or is determined by an EEG-based brain function monitor.

[0114] Figure 6The average amplitude is measured at three time points 1, 2, and 3, where time point 1 is a preoperative time point before the use of anesthesia-inducing drugs, time point 2 represents the onset of anesthesia-induced unconsciousness, and time point 3 is a time point after the onset of anesthesia-induced unconsciousness. The average amplitude is plotted on the y-axis, which is derived at point Cz in the 10-20 system (see Fig. 9 ), and is therefore referred to as a "Cz shift", of which this is only an example.

[0115] Figure 6 A further distinction was made between POD patients and nonPOD patients.

[0116] The average amplitudes of the DC-EEG signals of both POD patients and nonPOD patients were relatively small at time point 1. Group statistics of a patient group including N=7 POD patients and N=9 nonPOD patients at time point 1 are given below.

[0117] Group statistics

[0118]

[0119] At time point 2, when anesthesia-induced unconsciousness began, the mean amplitude of the POD patients increased significantly, while the mean amplitude of the nonPOD patients increased only slightly. Group statistics for the patient group including N=7 POD patients and N=7 nonPOD patients at time point 2 are given below.

[0120] Group statistics

[0121]

[0122] The significance of the increase in the mean amplitude from time point 1 to time point 2 in POD patients was 0.003 (Kruskal-Wallis test), which means that there was clear significance.

[0123] At time point 3, i.e., after the onset of anesthesia-induced unconsciousness, the average amplitudes of POD patients and nonPOD patients decreased again. Therefore, the peak of the curve is only present in POD patients. The group statistics of the patient group including N=7 POD patients and N=10 nonPOD patients at time point 3 are given below.

[0124] Group statistics

[0125]

[0126] In the measurement, the DC-EEG signal is measured as described above. The derivation is performed at the electrode position Cz (see Fig. 9). However, the same dynamics occur across the entire cortex, so that frontal or hoch frontale derivations can also be implemented.

[0127] Figure 7 A corresponding method for determining a parameter indicating an increased probability of postoperative delirium is shown by way of example. In step 701, at least one EEG signal is detected at the patient's head, wherein only the EEG signal frequency range of less than 0.5 Hz, i.e., direct current EEG signals or DC-EEG signals, is observed. The derivation is performed, for example, via an electrode at position Cz or a reference electrode, for example, at the ear.

[0128] In step 702, the average amplitude of the EEG signal in the current time window of the EEG signal is continuously determined. This continuous determination of the average amplitude enables the determination of a curve of the average amplitude of the EEG signal between preoperative and intraoperative time points in step 703. Therein, in step 704, the average amplitude of the EEG signal is determined at the onset of anesthesia-induced unconsciousness.

[0129] In step 705, it is checked whether the average amplitude of the EEG signal increases at the onset of anesthesia-induced unconsciousness, and if so, whether the increase exceeds a predefined amplitude. The predefined amplitude is determined, for example, by two reference values, wherein a first reference value gives the average amplitude of the EEG signal of a plurality of nonPOD patients at the onset of anesthesia-induced unconsciousness, and a second reference value gives the average amplitude of the EEG signal of a plurality of POD patients at the onset of anesthesia-induced unconsciousness. These reference values ​​are measured in advance in a group of POD patients and a group of nonPOD patients.

[0130] For example, when the second reference value is greater than the first reference value by a certain factor, for example, by a factor of 3, the predefined amplitude is exceeded.

[0131] In this case, corresponding information is provided as a parameter showing the occurrence of postoperative delirium in step 706. Subsequently, the doctor or anesthesiologist uses this information in step 707. The doctor or anesthesiologist can use this information, for example, to implement lighter anesthesia according to other parameters of the patient and / or introduce auxiliary treatment measures immediately after anesthesia to suppress the development of postoperative delirium.

[0132] To implement Figure 4 , 5 The method shown in 7 can use an EEG-based brain function monitor or a computer together. Wherein, the method steps of determining the intraoperative alpha peak frequency and comparing it with a reference value are implemented by a program code running in a processor ( Figure 3), or a method step of determining the alpha band power of the EEG signal and checking whether the increase in the alpha band power from a first power to a second power is less than a predefined amplitude, or a method step of determining a curve of the average amplitude of a "direct current" EEG signal and evaluating the curve. The program code is stored in a memory of a processor and is loaded into the processor before execution. The processor that runs the program code can be the main processor of the EEG monitor or a separate processor.

[0133] Figure 7 By way of example, a possible embodiment of 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.

[0134] An EEG cable can be connected to the EEG monitor 1 with the EEG electrodes 61, 62 via the interface 7. Two EEG cables for receiving EEG signals are shown by way of example, but further EEG cables for receiving multi-channel EEG signals may be provided.

[0135] The EEG signal is input to the microprocessor 2. The program code is stored in the memory 3, or the program code can be loaded into the memory 3. When the program code is run in the microprocessor 2, the program code is implemented. Figure 4 and / or Figure 5 and / or Figure 7 The method described above is described for the present invention. The process can be controlled by a control device 4, and this control device is suitable for receiving corresponding input errors. The control device 4 can contain the main processor of the EEG monitor 1 or a similar processor. Alternatively, the functions of the microprocessor 2 can be assumed by the control device 4. Other functions of the EEG monitor 1 can be realized by the control device 4 and / or other modules not shown.

[0136] Therefore, when the microprocessor 2 runs the loaded program code, it measures the intraoperative alpha peak frequency, compares this frequency with a reference value, and determines whether the measured intraoperative alpha peak frequency is significantly lower than a preset reference value of the alpha peak frequency. The corresponding information is transmitted to the output unit 5 and outputted on the output unit. This can be implemented, for example, by the monitor 51 and / or the acoustic unit 52.

[0137] Alternatively or additionally, the microprocessor 2 executes the loaded program code as follows Figure 5As shown, the power of the alpha band is evaluated. In this case, it is checked whether the increase of the alpha band power from the preoperative power to the intraoperative power is less than a predefined amplitude. The corresponding information is transmitted to the output unit 5 and output on this output unit. This can be implemented by the monitor 51 and / or the acoustic unit 52.

[0138] Alternatively or additionally, the microprocessor 2 executes the loaded program code as follows Figure 7 As shown, the curve of the mean amplitude of the "direct current" EEG signal is evaluated. In this case, it is checked whether the mean amplitude of the EEG signal increases by more than a predefined amplitude at the beginning of the anesthesia-induced loss of consciousness. The corresponding information is transmitted to the output unit 5 and outputted on this output unit. This can be implemented by the monitor 51 and / or the acoustic unit 52.

[0139] Of course, the present invention is not limited to the above-described embodiments, and various modifications and improvements may be made without departing from the solutions described herein. Any feature, as long as it is not mutually exclusive, may be used alone or in combination with any other feature, and the present disclosure extends to and includes all combinations and sub-combinations of one or more features described herein. Once a range is defined, it includes all values ​​within these ranges and all sub-ranges falling within a range.

Claims

1. A computer program product comprising program code for executing a method for providing a parameter indicating an increased probability of postoperative delirium, wherein the method comprises the steps of: - detecting (401) at least one EEG signal in the patient's head, - determining (402) an intraoperative alpha peak frequency of the EEG signal, wherein the alpha peak frequency in the power spectrum of the EEG signal is the frequency with the highest power in the alpha frequency band, - checking (403) whether the measured intraoperative alpha peak frequency is significantly lower than a preset reference value of the alpha peak frequency, the intraoperative alpha peak frequency being significantly lower than the preset reference value of the alpha peak frequency if the difference between the preset reference value and the intraoperative alpha peak frequency exceeds a defined deviation from the reference value calculated as a percentage or a defined absolute difference between the intraoperative alpha peak frequency and the reference value, - In the case where the measured intraoperative alpha peak frequency is significantly lower than a preset reference value of the alpha peak frequency, providing (404) corresponding information as a parameter indicating an increased probability of postoperative delirium.

2. The computer program product according to claim 1, characterized in that The preset reference value is measured by taking the average of intraoperative alpha peak frequencies of a plurality of patients who have not developed postoperative delirium.

3. The computer program product according to claim 2, characterized in that The preset reference value depends on the age group of the patient.

4. The computer program product according to any one of claims 1 to 3, characterized in that The intraoperative alpha peak frequency was measured at a time point when the patient was under stable anesthesia.

5. The computer program product according to any one of claims 1 to 3, characterized in that At least one frontal lobe EEG signal is detected in the patient's head.

6. The computer program product according to claim 5, characterized in that A plurality of frontal lobe EEG signals of the patient are detected and averaged to determine the alpha peak frequency.

7. The computer program product according to any one of claims 1 to 3, characterized in that The alpha peak frequency is determined on the EEG signal after the EEG signal is filtered by a bandpass filter.

8. The computer program product according to any one of claims 1 to 3, characterized in that When determining the intraoperative alpha peak frequency of the EEG signal, the frequency with the largest power in the power spectrum of the EEG signal in the alpha band and the theta band is determined, and the frequency with the largest power in the power spectrum of the EEG signal is the intraoperative alpha peak frequency.

9. The computer program product according to any one of claims 1 to 3, characterized in that Other steps are as follows: - determining the power of the alpha band of the EEG signal, wherein the power of the alpha band is defined in the power spectrum of the EEG signal as the integral of the power in all frequency ranges in the alpha band, - wherein the first power of the alpha frequency band is measured at a preoperative time point before the administration of anesthesia-inducing drugs, and the second power of the alpha frequency band is measured at an intraoperative time point after the onset of unconsciousness induced by the anesthesia, - checking whether the increase in the power of the alpha band from the first power to the second power is less than a predefined magnitude, - In the case where the increase in the power of the alpha frequency band from the first power to the second power is less than a predefined amplitude, providing corresponding information as a parameter indicating an increased probability of occurrence of postoperative delirium.

10. A device for providing a parameter indicating an increased probability of postoperative delirium, wherein: The device has: - means arranged and configured to detect at least one EEG signal in the head of a patient, - means for determining an intraoperative alpha peak frequency of the EEG signal, wherein the alpha peak frequency in the power spectrum of the EEG signal is the frequency with the highest power in the alpha frequency band, - means arranged and configured to check whether a measured intraoperative alpha peak frequency is significantly lower than a preset reference value of said alpha peak frequency, said means arranged and configured to determine that said intraoperative alpha peak frequency is significantly lower than said preset reference value of said alpha peak frequency if a difference between said preset reference value and said intraoperative alpha peak frequency exceeds a defined deviation calculated as a percentage from said reference value or a defined absolute difference between said intraoperative alpha peak frequency and said reference value, - a means for providing corresponding information as a parameter indicating an increased probability of occurrence of postoperative delirium when the measured intraoperative alpha peak frequency is significantly lower than a preset reference value of the alpha peak frequency.

11. The device according to claim 10, characterized in that The preset reference value is measured by taking the average of intraoperative alpha peak frequencies of a plurality of patients who have not developed postoperative delirium.

12. The device according to claim 10 or 11, characterized in that The preset reference value depends on the age group of the patient.

13. The device according to claim 10 or 11, characterized in that The means is configured and arranged to measure the intraoperative alpha peak frequency at a point in time when the patient is under stable anesthesia.

14. The device according to claim 10 or 11, characterized in that The component is configured and arranged to detect at least one frontal EEG signal in the patient's head.

15. The device according to claim 14, characterized in that The component is configured and arranged to detect a plurality of frontal lobe EEG signals of the patient and average them to determine the alpha peak frequency.

16. The device according to claim 10 or 11, characterized in that The means are configured and arranged to determine the alpha peak frequency on the EEG signal after the EEG signal is filtered by a bandpass filter.

17. The device according to claim 10 or 11, characterized in that also, - the component is arranged and configured to determine the power of the alpha band of the EEG signal, wherein the power of the alpha band is defined in the power spectrum of the EEG signal as the integral of the power within all frequency ranges in the alpha band, wherein, - the means are arranged and configured to measure a first power of the alpha frequency band at a preoperative time point before administration of anesthesia-inducing drugs, and to measure a second power of the alpha frequency band at an intraoperative time point after onset of unconsciousness induced by the anesthesia, - the means is arranged and configured to check whether the increase of the alpha band power from the first power to the second power is less than a predefined magnitude, The component is configured to provide corresponding information as a parameter indicating an increased probability of postoperative delirium in response to a situation in which an increase in the alpha band power from the first power to the second power is less than a predefined magnitude.

18. An EEG anesthesia monitor (1) having a device according to claim 10.

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