EP3773189A1 - Bereitstellung eines parameters, der auf einen bewusstseinsverlust eines patienten unter narkose hinweist - Google Patents
Bereitstellung eines parameters, der auf einen bewusstseinsverlust eines patienten unter narkose hinweistInfo
- Publication number
- EP3773189A1 EP3773189A1 EP19721581.7A EP19721581A EP3773189A1 EP 3773189 A1 EP3773189 A1 EP 3773189A1 EP 19721581 A EP19721581 A EP 19721581A EP 3773189 A1 EP3773189 A1 EP 3773189A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- spectral
- frequency
- cutoff frequency
- eeg signal
- absolute minimum
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
- 208000003443 Unconsciousness Diseases 0.000 title claims abstract description 65
- 206010002091 Anaesthesia Diseases 0.000 title claims abstract description 46
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Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4821—Determining level or depth of anaesthesia
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/25—Bioelectric electrodes therefor
- A61B5/279—Bioelectric electrodes therefor specially adapted for particular uses
- A61B5/291—Bioelectric electrodes therefor specially adapted for particular uses for electroencephalography [EEG]
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
- A61B5/372—Analysis of electroencephalograms
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
- A61B5/372—Analysis of electroencephalograms
- A61B5/374—Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
- A61B5/384—Recording apparatus or displays specially adapted therefor
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
Definitions
- the invention relates to a method and a device for providing a parameter indicative of a loss of consciousness of a patient under anesthesia.
- EEG electroencephalogram
- anesthesia for an anesthetist, it is advantageous in his daily work to be able to pinpoint the onset of loss of consciousness in the case of anesthesia (LOC), since (1) a too early intubation with subsequent pain perception of the patient can be avoided and (2) too late intubation due to the deep anesthetic stage with loss of protective reflexes the danger of complications such as aspiration can be avoided.
- LOC anesthesia
- the above-mentioned EEG index shows a decrease in the time of loss of consciousness (LOC). However, this is the exact time of loss of consciousness can not be determined.
- the invention provides a method of providing a parameter indicative of a patient's loss of consciousness under anesthesia.
- at least one EEG signal is detected at the head of the patient.
- the spectral cutoff frequency in a current time window of the EEG signal is continuously determined.
- the spectral cutoff frequency is defined as indicating the frequency for which it includes 95% of the total power in the power spectrum.
- the amplitude square is plotted against the frequency. It reflects the respective proportion of the individual frequency ranges in the total power component of the raw signal.
- the spectral cutoff frequency is therefore the frequency below which a certain proportion of the energy of the total spectrum lies. According to the invention, a proportion of 95% is considered.
- the considered time window looks at the EEG signal in a time period that goes back a defined period of time from the current time, for example the EEG signal of the last minute or the last 30 seconds or the last 20 seconds. In this respect, it is a time window wandering with time.
- the spectral cutoff frequency is determined via a spectral analysis. In particular, a discrete Fourier transformation is carried out, for example a fast Fourier transformation (FFT - "Fast Fourier Transformation").
- FFT fast Fourier transformation
- the course of the spectral cut-off frequency of the EEG signal is determined in a period which begins before application of the anesthetic and the onset of anesthesia-induced unconsciousness of the patient and ends after the onset of anesthesia-induced loss of consciousness.
- the absolute minimum of the spectral cut-off frequency is determined in the considered time period, with a negative peak of the spectral cut-off frequency in the absolute minimum, and information on when the absolute minimum has been reached, provided as a parameter for the indication of a patient's loss of consciousness or issued.
- the mentioned period is not necessarily predefined in its length. For example, it may be provided that the period ends as soon as the existence of an absolute minimum has been determined.
- the invention is based on the surprising and verified by a study finding that the time LOC of the loss of consciousness is accompanied by a brief drop in the spectral cutoff frequency. It has been recognized that the spectral cutoff decreases momentarily at the moment of loss of consciousness, with a subsequent reentry. In this case, a significant negative peak of the spectral cutoff frequency is formed, which is evaluated and its absolute minimum is determined. The values of the spectral cut-off frequency drop off before the absolute minimum and rise again behind the absolute minimum. This allows the absolute minimum to be determined unambiguously.
- the short-term drop in the spectral cut-off frequency at the time of loss of consciousness and its reentry is typically over a period of approximately 1 to 3 minutes, more particularly over a period of approximately 2 minutes.
- a parameter By determining the timing of the negative peak, i. H. of the absolute minimum of the spectral cut-off frequency in the considered time period, a parameter can thus be provided which indicates, indi- cally, a loss of consciousness of the patient during the anesthesia and can be taken into account by the anaesthesiologist, possibly together with further parameters. This allows for improved anesthesia management.
- the absolute minimum is determined and the time of its presence is communicated as a parameter. Local minima may also occur, especially during the fall of the signal, however have significantly higher minimum values than the absolute minimum indicating the negative peak of the spectral cutoff frequency.
- the negative peak which contains the absolute minimum, is formed significantly stronger in terms of both its width and its depth than any local minima and therefore easily detectable.
- a frontal EEG signal is preferably recorded, that is to say a frontal discharge, wherein the EEG signal is measured at at least two electrodes which are arranged at different locations on the forehead of the patient. It can be provided that several frontal EEG signals are recorded, which are averaged before a determination of the spectral cutoff frequency. For example, in the typical 10-20 system, signals are derived from electrodes positioned at positions F7, F8, Fp1, Fp2, and Fpz.
- It can be a bipolar derivative (difference between two active electrodes) or a unipolar derivative (difference of several active electrodes against a common reference).
- An embodiment of the invention provides that the spectral cutoff frequency is continuously determined such that it is redetermined at least every 30 seconds, in particular at least every 10 seconds, in particular at least every 2 seconds. It is obvious that the more frequently the spectral cutoff frequency is determined, the more accurate the time of the minimum of the spectral cutoff frequency can be determined.
- a further embodiment of the invention provides that the information as to when the absolute minimum has been reached is provided as soon as it has been determined. Once the presence of an absolute minimum can be determined safely, the time at which the absolute minimum has been reached is output as a parameter. Since this information is important to the anesthesiologist as an indication of the loss of consciousness, the information will be provided as soon as possible.
- An alternative evaluation method provides that the information about the time at which the minimum has been reached is provided if, for a defined number of measured values, the measured value of the spectral cutoff frequency is higher than the previous measured value.
- the number of measured values which is determined as an indication that the spectral cutoff frequency increases again and thus the absolute minimum has been reached, naturally depends on how often the spectral cutoff frequency is determined.
- a further embodiment provides that the spectral cutoff frequency at the EEG signal is determined after it has been filtered by a bandpass filter.
- the bandpass filter is designed, for example, such that it only passes signals in the frequency range from 0.5 to 40 Hz.
- an embodiment of the invention provides that the spectral cut-off frequency is determined by means of a spectral analysis, wherein in each case a running time window of the EEG signal is evaluated.
- the spectral analysis is done for example by an FFT algorithm.
- FFT algorithm a discrete cosine transformation
- discrete wavelet transformation a discrete wavelet transformation
- signal decomposition via a bandpass filter bank a discrete wavelet transformation
- the method according to the invention is automated, in particular carried out by a computer program.
- the computer program contains program code for carrying out the method according to claim 1, when the computer program is executed on a computer.
- the invention relates to a device for providing a parameter indicative of a loss of consciousness of a patient under anesthesia.
- the device comprises:
- Means adapted to detect at least one EEG signal at the patient's head Means adapted to continuously determine the spectral cutoff frequency in a current time window of the EEG signal, the spectral cutoff frequency indicating the frequency for which it includes 95% of the total power in the power spectrum,
- Means adapted to determine the course of the spectral cut-off frequency of the EEG signal in a period beginning before the administration of an anesthetic drug and ending after the onset of anesthesia-induced unconsciousness
- Means adapted to determine the absolute minimum of the spectral cut-off frequency in the time period, wherein in the absolute minimum there is a negative peak of the spectral cut-off frequency
- Means adapted to provide information as to when the absolute minimum has been reached as a parameter for indicative indication of loss of consciousness of the patient.
- Said means may be implemented by a microprocessor in conjunction with program code executed by the microprocessor.
- the invention relates to an EEG narcosis monitor having a device according to claim 12.
- the device according to the invention is thus integrated into an EEG narcosis monitor, wherein it is provided and designed to analyze and display EEG data in real time.
- FIG. 1 shows exemplary EEG signals in the awake state and after anesthesia-induced unconsciousness, both as a time-dependent signal and in the power spectrum;
- Figure 2 shows an example of the time course of the spectral cutoff frequency in one
- FIG. 3 is a flowchart of the method according to the invention.
- FIG. 4 shows by way of example a device for carrying out the method of FIG. 3;
- FIG. 1 Positioning points for EEG electrodes according to the 10-20 system.
- FIG. 1 shows an EEG signal for explaining the background of the invention in the upper illustration ("output signal"), as occurs in a patient in the awake state.
- the signal is shown both as a time signal (left) and after a spectral analysis as a power spectrum (right).
- the power (the amplitude square) in db is plotted against the frequency in Hz.
- the power spectrum reflects the respective proportion of the individual frequency ranges in the total power component of the raw signal.
- SEF spectral corner frequency
- This is defined as the frequency below which a share of 95% of the energy of the total spectrum lies.
- the median frequency F50 is also shown, but in this case it does not matter.
- the lower representation (“anesthesia”) of Figure 1 shows an EEG signal under anesthesia. It can be seen that the spectral cut-off frequency SEF is shifted to the left compared with the value in the patient being awake.
- the spectral cut-off frequency thus provides information about how awake a patient is. Since higher frequencies are contained in the EEG signal during wakefulness, high values of the spectral neck frequency result. In sleep state or under anesthesia slow frequencies dominate in the EEG, so that lower values of the spectral cutoff frequency are present.
- the EEG electrodes were placed on the patient who was still awake before the anesthesiologist administered the first drugs. For this purpose, the forehead and temples were thoroughly disinfected and freed from skin fats. This measure improved the conductivity of the skin and therefore ensured a more trouble-free derivation of the EEG signals.
- the prefabricated EEG adhesive electrodes made by Masimo 4248RD SEDLine Sensor, Single Patient Use, Non-Sterile) were then placed on the prepared skin areas on the forehead, with the EEG electrodes in each case at the positions F7, F8, FP1 and FP2 corresponding to the 10 / 20 system systems, with Fpz as reference electrode. The corresponding positions are shown in FIG.
- the impedance of the individual electrodes was less than 5 kQ during the derivation, the sampling rate was 250 Hz.
- a bandpass filter is preset to 0.5 - 40 Hz.
- EEG-based brain function monitor the "SEDLine Monitor” from Masimo Corporation, Irvine, California
- the derivation and recording of a continuous 4-channel EEG was started.
- the Patients were still awake at this point, so the initial baseline values were in line with baseline activity.
- Event Markers were entered manually during the EEG recording in the EEG.
- the anesthetist In the course of the drug administration was initiated by the anesthetist. This time was noted as an event marker "Start anesthesia”. All patients received the drug propofol intravenously for anesthesia induction.
- SEDLine Monitor The following data were recorded by the SEDLine Monitor: the spectral cut-off frequency (SEF), the anesthesia index (PSI), the artifact level and the electromyographic activity.
- SEF spectral cut-off frequency
- PSI anesthesia index
- electromyographic activity The following data were recorded by the SEDLine Monitor: the spectral cut-off frequency (SEF), the anesthesia index (PSI), the artifact level and the electromyographic activity.
- the power spectrum for determining the spectral cutoff frequency was thus determined in time windows of the EEG signal of 20 seconds, with an update every 2 seconds.
- the calculation was carried out by means of digital, computer-assisted EEG signal processing.
- the basis for this is the spectral analysis of the RohEEG by means of Fast Fourier Transformation, by means of which power components can be calculated for the currently analyzed time window.
- FIG. 2 shows the mean values of the spectral cutoff frequency ("SEK") determined in the study as a function of time.
- SEK spectral cutoff frequency
- the mean of the spectral cut-off frequency in Hz is shown over the time of the loss of consciousness in the period of 200 seconds before the loss of consciousness until 280 seconds after the loss of consciousness.
- Loss of Consciousness (LOC) is characterized as "00" on the timeline.
- LOC Loss of Consciousness
- the correlations determined are evaluated electronically or computer-based and used to determine the course of the spectral cut-off frequency of the EEG signal and in this the absolute minimum of the spectral cut-off frequency as a parameter for the occurrence of anesthesia-induced unconsciousness.
- the associated program can be integrated as a software tool into an EEG-based brain function monitor or electroencephalograph.
- FIG. 3 shows the method for determining a parameter which indicates a loss of consciousness of a patient under anesthesia.
- step 301 at least one frontal EEG signal is detected on a patient.
- EEG signals are recorded via electrodes at positions F7, F8, FP1 and FP2 according to the 10/20 system, with Fpz as the reference electrode and averaged over these signals.
- the spectral cutoff frequency is continuously determined in a current time window of the EEG signal.
- the determination is carried out continuously, for example, in the sense that the spectral frequency is currently determined every 2 seconds or every 5 seconds.
- the current time window has, for example, a length of 20 seconds, the values mentioned being to be understood as examples only.
- step 303 the course of the spectral cut-off frequency of the EEG signal is evaluated. This occurs in a period of time that begins before the administration of an anesthetic drug (eg, propofol) and ends after the onset of anesthesia-induced unconsciousness. The period may be fixed or not fixed with regard to its duration. In the second case, the period ends, for example, as soon as the minimum of the spectral cutoff frequency could be determined.
- step 304 the absolute minimum of the spectral cutoff frequency in the considered period is determined. It can be done, for example, by evaluating whether the spectral cutoff frequency has fallen below a value of 10 hertz, in particular below a value of 9 hertz, and is rising again.
- the measured value of the spectral cutoff frequency is higher than the preceding measured value.
- a negative peak of the spectral cutoff frequency is evaluated, the absolute minimum of the spectral cutoff frequency lying in the negative peak of the negative peak. Further methods of data analysis and curve discussion can be used to determine the absolute minimum of the spectral cutoff frequency with the greatest possible accuracy.
- this information is provided as a parameter for indicating indefinitely a patient's loss of consciousness, such as acoustically and / or on the display of an EEG monitor.
- a physician or anesthetist can use this information to adapt an intubation to be made precisely to the individual state of consciousness of the patient and thus to perform the intubation neither too early nor too late. This leads to a higher safety for patients during the induction of anesthesia.
- an EEG-based brain function monitor or, in general, a computer can be used.
- the method steps for determining and evaluating the spectral cutoff frequency and for determining the absolute minimum of the spectral cutoff frequency are carried out by a program code which is executed in a processor.
- the program code is stored in a memory of the processor or is loaded into it before execution.
- the processor executing the program code may be the main processor of the EEG monitor or a separate processor.
- FIG. 4 shows an example of a possible implementation of such an EEG-based brain function monitor 1.
- the EEG monitor 1 comprises a microprocessor 2, a memory 3, a control device 4, an output unit 5 and an interface 7 for connecting EEG cables. Via the interface 7, EEG cables with EEG electrodes 61, 62 can be connected to the EEG monitor 1.
- EEG cables are shown which receive an EEG signal, whereby further EEG cables may be provided for receiving a multi-channel EEG signal.
- the EEG signal is supplied to the microprocessor 2.
- the program code is stored in the memory 3 or a program code can be loaded into the memory 3 which, when executed in the microprocessor 2, carries out the method explained with reference to FIG.
- the sequence can be controlled via the control device 4 and this can be set up to receive corresponding input commands.
- the control device 4 may be a main processor of the EEG monitor 1 or contain such.
- the functionality of the microprocessor 2 can be taken over by the control unit 4. In this case, further functionalities of the EEG monitor 1 can be realized via the control device 4 and / or other modules, not shown.
- the microprocessor 2 determines when executing the loaded program code, the absolute minimum of the spectral cutoff frequency and the time at which this absolute minimum is present.
- the corresponding information is transmitted to the output unit 5 and outputted thereto. This can be done for example via a monitor 51 and / or an acoustic unit 52.
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Abstract
Description
Claims
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
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DE102018110275.5A DE102018110275A1 (de) | 2018-04-27 | 2018-04-27 | Verfahren und Vorrichtung zur Bereitstellung eines Parameters, der auf einen Bewusstseinsverlust eines Patienten unter Narkose hinweist |
PCT/EP2019/060788 WO2019207130A1 (de) | 2018-04-27 | 2019-04-26 | Bereitstellung eines parameters, der auf einen bewusstseinsverlust eines patienten unter narkose hinweist |
Publications (1)
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EP3773189A1 true EP3773189A1 (de) | 2021-02-17 |
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EP19721581.7A Withdrawn EP3773189A1 (de) | 2018-04-27 | 2019-04-26 | Bereitstellung eines parameters, der auf einen bewusstseinsverlust eines patienten unter narkose hinweist |
Country Status (5)
Country | Link |
---|---|
US (1) | US20210244353A1 (de) |
EP (1) | EP3773189A1 (de) |
CN (1) | CN112399826A (de) |
DE (1) | DE102018110275A1 (de) |
WO (1) | WO2019207130A1 (de) |
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CN116831598A (zh) * | 2023-06-14 | 2023-10-03 | 中国医学科学院生物医学工程研究所 | 一种脑肌信号评估方法和装置 |
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DE102011115116A1 (de) * | 2011-05-02 | 2012-11-08 | Denis Jordan | Verfahren zur Bewusstseins- sowie Schmerzüberwachung, Modul zur Analyse von EEG-Signalen und EEG-Narkosemonitor |
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US20170231556A1 (en) * | 2014-08-22 | 2017-08-17 | The General Hospital Corporation | Systems and methods for predicting arousal to consciousness during general anesthesia and sedation |
US10130813B2 (en) * | 2015-02-10 | 2018-11-20 | Neuropace, Inc. | Seizure onset classification and stimulation parameter selection |
CN104644166B (zh) * | 2015-02-16 | 2017-03-08 | 浙江大学 | 一种基于格子复杂性算法的无线动态麻醉深度检测方法 |
BR112018071795A8 (pt) * | 2016-05-02 | 2022-11-16 | Fresenius Vial Sas | Dispositivo de controle para controlar a administração de propofol a um paciente |
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2018
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2019
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- 2019-04-26 EP EP19721581.7A patent/EP3773189A1/de not_active Withdrawn
- 2019-04-26 US US17/049,479 patent/US20210244353A1/en active Pending
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WO2019207130A1 (de) | 2019-10-31 |
US20210244353A1 (en) | 2021-08-12 |
DE102018110275A1 (de) | 2019-10-31 |
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