DE102013015427A1 - Method and device for measuring cerebral perfusion - Google Patents

Abstract

A method is provided for measuring the cerebral perfusion of a patient (8) comprising the following steps: Continuous long-term derivation and storage of an EEG signal (E) of the patient (8); Clean up the EEG signal (E) of artifacts; Power spectral analysis of the artifact-adjusted EEG signal in certain frequency bands; Clean up the tracks obtained by the power spectral analysis by a common trend of all tracks; Detecting deviations (D) of the trended power values of at least one track from a baseline; and displaying significant deviations (D) and / or triggering an alarm (A) when a significant deviation (D) is detected. An associated device (1) comprises an EEG device (2) as well as a control unit (3) connected therewith in terms of data communication technology, which is set up for automatically carrying out the method.

Description

The invention relates to a method for measuring cerebral perfusion. The method is used in particular for the detection of vasospasm-related imminent cortical ischemia in patients with Subarachnoidalblutungen. The invention further relates to an apparatus for carrying out the method.

Subarachnoid haemorrhages are among the most common acute neurological conditions. In addition to rebleeding and cerebrospinal fluid disturbances, vasospasm and resulting cortical ischaemia represent the greatest risk to the affected patients.

Gold standard for the detection of vasospasm is currently the transcranial Doppler and duplex sonography. However, this only allows a punctual examination of the patient, for example once a day. Events between the examinations are not detected late or can not be treated effectively.

The invention has for its object to provide a related improved method for measuring cerebral perfusion. In particular, the method is intended to enable the early detection of an imminent cortical ischemia as a result of vasospasm. A further object of the invention is to provide a device which is particularly suitable for carrying out the method.

According to the invention this object is achieved by the features of claim 1. Advantageous embodiments and further developments of the invention are set forth in the subclaims and the description below.

To effectively prevent vasospasm-related infarcts and thus to improve the functional outcome of patients, continuous monitoring of cortical perfusion seems desirable. This can be recognized by a continuous EEG recording guaranteed. Compromising brain function due to disturbances of cortical perfusion is measured directly here. It is recognized that reversible neuronal changes that occur in an abnormal decrease in cerebral blood flow lead to measurable EEG changes. An imminent ischemia (Delayed Cerebral Ischaemia, in short: DCI) can thus be detected before irreversible cell death and at best by appropriate countermeasures -. B. by the administration of nimodipine, by intra-arterial spasmolysis or by triple-H therapy - be corrected.

Cortical ischemia is recognized in the EEG as a focal reduction of power in the frequency spectrum of the EEG signal. This effect is automatically detected by the inventive method, this for an automated artifact elimination and power spectral analysis, the trend correction of the raw data and the automated detection of significant deviations from includes a baseline.

• 1) Kontinuierliche Langzeitableitung des EEG-Signals des Patienten und Abspeicherung dieses EEG-Signals, insbesondere auf einer Festplatte. Für die EEG-Aufzeichnung wird in zweckmäßiger Ausführung eine Elektrodenanordnung nach dem internationalen 10-20-System verwendet. Zur Vereinfachung der Verfahrensdurchführung oder sofern nicht anders möglich (wenn z. B. aufgrund vorhandener Bandagen nicht alle Elektroden des 10-20-Systems anbringbar sind), wird in alternativer Ausführung der Erfindung vorteilhaft nur eine Teilmenge der Elektroden (z. B. F7/F8, T3/T4, C3/C4, P3/P4, O2/O1, Fz, Cz und Pz) für die EEG-Aufzeichnung herangezogen. Die Abtastrate der Multi-Kanal-EEG-Ableitung beträgt vorzugsweise mindestens 200 Hz. Das Roh-EEG-Signal wird vorzugsweise in dem Frequenzbereich von mindestens 0,1 Hz–70 Hz (besonders bevorzugt etwa zwischen 0,05 Hz und 100 Hz) aufgezeichnet. Die Aufzeichnung des EEG-Signals erfolgt zweckmäßigerweise über mehrere Tage, insbesondere über mindestens 4 Tage. Bevorzugt wird das EEG-Signal über eine Dauer zwischen 5 und 12 Tagen aufgezeichnet. Für die Erkennung vasospasmenbedingter kortikaler Ischämien nach einer Subarachnoidalblutung (SAB) wird mit der Aufzeichnung vorteilhafterweise so bald wie möglich, insbesondere innerhalb der ersten drei Tage nach Feststellung der SAB begonnen.
• 2) Automatisierte Artefaktbereinigung (d. h. Artefakterkennung und -eliminierung), durch welche Artefakte, die das EEG-Signal in der Regel überlagern, und die daher das Ergebnis einer quantitativen Analyse signifikant verfälschen würden, ausgefiltert werden. Als Artefakte werden allgemein alle Signalanteile des gemessenen EEG-Signals bezeichnet, die nicht auf die zu messende elektrische Hirnaktivität, sondern auf andere auslösende Ereignisse, z. B. EMG, Körper- und Augenbewegungen, etc. zurückzuführen sind. In einer vorteilhaften Ausführung dieses Artefaktbereinigungsprozesses wird zunächst im Rahmen einer sogenannten Fensterung jeder Kanal des EEG-Signals in (Zeit-)Intervalle (von z. B. jeweils 2 Sekunden) unterteilt. In diesen Intervallen wird dann jeweils ein Mittelwert (arithmetischer Mittelwert, Median, etc.) der Amplitude pro Kanal berechnet, wobei dieser Mittelwert von jedem Abtast-Signalwert subtrahiert wird. Aus diesem neuen Signal werden in einem nächsten Schritt der Maximalwert und der Minimalwert bestimmt und die Differenz aus diesen beiden Extremwerten ermittelt. Überschreitet der Betrag dieser Differenz einen bestimmten festgelegten Schwellwert (z. B. 200 μV), so wird dieses Intervall für diesen Kanal für die weitere Analyse nicht berücksichtigt.
• 3) Spektralanalyse (Power) des artefaktbereinigten EEG-Signals in bestimmten Frequenzbändern. Die Power-Spektralanalyse wird dabei insbesondere in den Frequenzbändern Delta (0,5–3,5 Hz), Theta (4–7,5 Hz), Alpha (8–12 Hz) und Beta (12,5–22 Hz) vorgenommen. Im Rahmen der Erfindung können ferner auch höhere Frequenzbänder bei der Power-Spektralanalyse berücksichtigt werden. Vorzugsweise werden für die Spektralanalyse mindestens zwei Kanäle des aretfaktbereinigten EEG-Signals, die unterschiedlichen Hirnregionen oder Gefäßstromgebieten zugeordnet sind, getrennt betrachtet.
• 4) Trendbereinigung der aus der Power-Spektralanlyse resultierenden Spuren. Um Power-Veränderungen in den betrachteten Frequenzbändern zu identifizieren, die nicht global, sondern fokal (nur in einer bestimmten Hirnregion bzw. einem bestimmten Gefäßstromgebiet) auftreten und somit mit hoher Wahrscheinlichkeit Korrelat einer DCI sind, wird der gemeinsame Trend aller betrachteten Spuren berechnet und jede Spur um diesen gemeinsamen Trend bereinigt. Als Spur werden die aus der Power-Spektralanalyse resultierenden Power-Werte bezeichnet, die einem Kanal oder einer Kanalgruppe des EEG-Signals und einem betrachteten Frequenzband zuzuordnen sind. Um eine entsprechende Korrektur zu erreichen, werden vorzugsweise die gemeinsamen Schwankungen aller Ableitungen berechnet und die einzelnen Ableitungen danach mit der gemeinsamen Schwankung verrechnet. Dies wird insbesondere erreicht, indem zunächst der Median des Power-Verlaufs in jeder Spur berechnet wird; jede Spur wird dann mit ihrem Median normiert. Im Anschluss wird die Medianspur aus den gemittelten Spuren gebildet. Durch die so errechnete Trendspur wird in der Folge jede der Originalspuren geteilt. Infolge der Trendbereinigung bilden sich die einzelnen Ableitungen mit stark verschmälerten Verteilungsbreiten ab, so dass regionale Ereignissen, wie etwa eine fokalen Power-Abnahme in einem bestimmten Frequenzbereich fehlersicher erkannt werden können.
• 5) Erfassung von Abweichungen der trendbereinigten Power-Werte mindestens einer Spur von einer Baseline. Vorzugsweise werden dabei alle erfassten Spuren auf Abweichungen von der jeweiligen Baseline untersucht. Die Baseline wird in zweckmäßiger Ausgestaltung des Verfahrens für jede Spur aus einem zu Beginn der EEG-Aufzeichnung, insbesondere dem während der ersten 6 bis 24 Stunden nach dem Beginn der EEG-Aufzeichnung erfassten Teil des EEG-Signals berechnet. Für die Erkennung vasospasmenbedingter kortikaler Ischämien nach einer Subarachnoidalblutung (SAB) wird die Basline zweckmäßigerweise aus einem Teil des EEG-Signals berechnet, der innerhalb der – regelmäßig vasospasmenfreien – ersten 24 Stunden nach einer festgestellten Subarachnoidalblutung aufgenommen wurde.
• 6) Anzeige der erfassten Abweichungen in Echtzeit und/oder Auslösung eines Alarms, wenn eine signifikante fokale Abweichung der Power von der Baseline in mindestens einem ausgewählten Frequenzband ermittelt wird. Als signifikant wird eine Abweichung dabei insbesondere dann erkannt, wenn sie eine vorgegebene Toleranz (insbesondere einen Schwellwert oder Schwellwertbereich) überschreitet. In ersterem Fall werden jedenfalls diejenigen Spuren angezeigt, in denen eine signifikante Abweichung von der Baseline festgestellt wird. Eine signifikante, insbesondere fokale Abweichung wird verfahrensgemäß als Hinweis auf eine DCI gewertet. Die Anzeige bzw. Auslösung eines Alarms ermöglicht hierbei dem behandelnden medizinischen Team eine fehlersichere Früherkennung der DCI, ohne dass hierfür besondere spezielle medizinische Kenntnisse seitens des Behandlers vonnöten wären.

According to the method, quantitative EEG (qEEG) directly measures the compromise of brain function, and thus indirectly cerebral perfusion. The method comprises the following steps:

• 1) Continuous long-term derivation of the patient's EEG signal and storage of this EEG signal, especially on a hard disk. For the EEG recording, an electrode assembly according to the international 10-20 system is suitably used. In order to simplify the process or, if not otherwise possible (if, for example, not all electrodes of the 10-20 system can be attached due to existing bandages), in an alternative embodiment of the invention, advantageously only a subset of the electrodes (eg F7 / F8, T3 / T4, C3 / C4, P3 / P4, O2 / O1, Fz, Cz and Pz) for EEG recording. The sampling rate of the multi-channel EEG derivative is preferably at least 200 Hz. The raw EEG signal is preferably recorded in the frequency range of at least 0.1 Hz-70 Hz (more preferably, for example, between 0.05 Hz and 100 Hz) , The recording of the EEG signal is expediently carried out over several days, in particular for at least 4 days. Preferably, the EEG signal is recorded for a period of between 5 and 12 days. For the detection of vasospasm-related cortical ischemia following subarachnoid hemorrhage (SAB), recording is advantageously started as soon as possible, in particular within the first three days after detection of SAB.
• 2) Automated artifact correction (ie, artifact detection and elimination) that filters out artifacts that typically overlay the EEG signal and that would therefore significantly distort the result of a quantitative analysis. As artifacts are generally all signal components of the measured EEG signal, not on the electrical brain activity to be measured, but on other triggering events, eg. As EMG, body and eye movements, etc. are due. In an advantageous embodiment of this artifact elimination process, each channel of the EEG signal is first subdivided into (time) intervals (of, for example, 2 seconds in each case) within the framework of a so-called windowing. Each of these intervals then calculates an average (arithmetic mean, median, etc.) of the amplitude per channel, this average value being subtracted from each sample signal value. From this new signal, the maximum value and the minimum value are determined in a next step and the difference between these two extreme values is determined. If the amount of this difference exceeds a certain defined threshold value (eg 200 μV), this interval is not taken into account for this channel for further analysis.
• 3) Spectral analysis (power) of the artifact-corrected EEG signal in certain frequency bands. The power spectral analysis is carried out in particular in the frequency bands delta (0.5-3.5 Hz), theta (4-7.5 Hz), alpha (8-12 Hz) and beta (12.5-22 Hz) , In the context of the invention, higher frequency bands can also be taken into account in the power spectral analysis. Preferably, at least two channels of the arctf-corrected EEG signal, which are assigned to different brain regions or vessel flow regions, are considered separately for the spectral analysis.
• 4) Trend correction of the traces resulting from the power spectral analysis. In order to identify power changes in the considered frequency bands, which occur not globally, but focal (only in a specific brain region or a specific vascular flow area) and thus are likely correlate of a DCI, the common trend of all considered tracks is calculated and each Track adjusted for this common trend. The track refers to the power values resulting from the power spectral analysis, which are assigned to a channel or channel group of the EEG signal and a frequency band under consideration. In order to achieve a corresponding correction, the common fluctuations of all derivatives are preferably calculated and the individual derivatives are then calculated with the common variation. This is achieved in particular by first calculating the median of the power history in each track; each track is then normalized with its median. Subsequently, the median track is formed from the averaged tracks. As a result, the trend track calculated in this way divides each of the original tracks. As a result of the trend adjustment, the individual derivatives are formed with greatly narrowed distribution widths, so that regional events such as a focal power decrease in a certain frequency range can be detected with fail-safety.
• 5) Detecting deviations of the trended power values of at least one track from a baseline. Preferably, all recorded tracks are examined for deviations from the respective baseline. The baseline is calculated in an expedient embodiment of the method for each track from a part of the EEG signal acquired at the beginning of the EEG recording, in particular during the first 6 to 24 hours after the start of the EEG recording. For the detection of vasospasm-related cortical ischemia after a subarachnoid hemorrhage (SAB), the baseline is expediently calculated from a part of the EEG signal which was recorded within the - 24 hours after a detected subarachnoid haemorrhage - which was usually vasospasm-free.
• 6) Display of the detected deviations in real time and / or triggering of an alarm, if a significant focal deviation of the power from the baseline in at least one selected frequency band is determined. In this case, a deviation is recognized to be significant in particular if it exceeds a predetermined tolerance (in particular a threshold value or threshold value range). In the former case, in any case, those tracks are displayed in which a significant deviation from the baseline is found. A significant, in particular focal deviation is evaluated according to the method as an indication of a DCI. The display or triggering of an alarm in this case allows the treating medical team a fail-safe early detection of DCI, without the need for special special medical knowledge on the part of the practitioner.

• – das EEG-Gerät zur kontinuierlichen Langzeitableitung eines EEG-Signals des Patienten anzusteuern und dieses EEG-Signal abzuspeichern,
• – das EEG-Signal um Artefakte zu bereinigen,
• – das artefaktbereinigte EEG-Signal einer Power-Spektralanalyse in speziellen Frequenzbändern zu unterziehen und
• – Abweichungen aller oder einzelner Spuren von einer Baseline in Echtzeit anzuzeigen zu erfassen und
• – erfasste Abweichung (zumindest signifikante Abweichungen) anzuzeigen und/oder einen Alarm auszulösen, wenn eine signifikante fokale Abweichung der Power von der Baseline in mindestens einem ausgewählten Frequenzband ermittelt wird.

The device according to the invention comprises an EEG device for detecting an EEG signal of a patient as well as a control unit connected therewith in terms of data communication technology. The control unit is - switching and / or program technology - set up for the automatic implementation of the method according to the invention described above. The control unit is thus in particular designed to

• To control the EEG device for continuous long-term derivation of an EEG signal of the patient and to store this EEG signal,
• - to clean up the EEG signal for artifacts,
• - To subject the artifact-corrected EEG signal of a power spectral analysis in special frequency bands and
• - To detect deviations of all or individual tracks from a baseline in real time and to capture
• - detect detected deviation (at least significant deviations) and / or trigger an alarm if a significant focal deviation of the power from the baseline is determined in at least one selected frequency band.

In particular, the control unit is constituted by a control computer (for example, a personal computer) in which a control program (that is, control software) is installed, so that the above-described method is automatically performed when the control program is executed.

With regard to the design details and variants of the functional design of the device, reference is made mutatis mutandis to the above statements relating to the method according to the invention.

An embodiment of the invention will be described below with reference to a drawing. Show:

1 in a schematic block diagram, a device for measuring cerebral perfusion (cerebral blood flow), with an EEG device and thus a data communication technically interconnected control unit,

2 in a flow chart, a method performed by the device, and

3 - 5 Results of a pilot study using the method.

1 shows a gross schematic simplification of a device 1 to measure the compromise of brain function, and thus indirectly to measure cerebral perfusion. The method is used in particular for the detection of a vasospasm-related imminent cortical ischemia in patients with Subarachnoidalblutungen means of quantitative EEG.

The device 1 includes an EEG device 2 as well as a control computer 3 (eg a personal computer). The control computer 3 is via a data transmission line 4 (eg in the form of an Ethernet connection of a conventional LAN) for data transmission with the EEG device 2 connected. In the control computer 3 is a control program 5 installed on the one hand, as described in more detail below, on the one hand functions for controlling the EEG device 2 via control commands C and on the other hand functions for the evaluation of the EEG device 2 detected EEG signal E implemented.

The EEG device 2 includes a number of electrodes 6 to measure the electrical activity of the brain during operation of the device 1 on the scalp of a - in 1 for example on a patient bed 7 lying down - patients 8th be attached. The number and arrangement of the electrodes 6 at the head 9 of the patient 8th is hereby preferably selected according to the 10-20 system. However, only a subset of the 10-20 system electrodes may be used in the operation of the device 1 in particular, according to the nomenclature of the 10-20 system, the electrodes C3 / C4, P3 / P4, O2 / O1, T3 / T4, F3 / F4 (with the electrodes F3 / F4 alternatively being replaced by the electrodes F5 / F6 be replaced).

To the control computer 3 are connected in a conventional manner means for data input and data output, in particular a screen 10 ,

That at the expiration of the control program 5 by means of the device 1 automatically performed procedure is in 2 shown schematically.

After that, in a first step 20 - by appropriate control of the EEG device 2 - automatically the EEG signal E of the patient 8th over a period of at least 4 days (preferably between 5 and 12 days) using a 256 Hz multi-channel EEG derivative sampling rate in a frequency range of 0.08 Hz to 86 Hz.

The recorded EEG signal E is in one step 21 through the control program 5 on a hard disk of the control computer 3 stored.

For automated artifact cleaning will be done in one step 22 each channel of the EEG signal E is divided into (time) intervals of 2 seconds each. In these intervals, one (in particular arithmetic) average of the amplitude per channel, this average being subtracted from each sample signal value. From this new signal, the maximum value and the minimum value are determined and the difference between these two extreme values is determined. If the amount of this difference exceeds a certain threshold value of 200 μV, this interval will not be considered for this channel for further analysis.

The raw artifact-corrected EEG signals are processed in one step 23 performed a power spectral analysis. For this purpose, the artifact-adjusted raw EEG signals are subjected to a short-time Fourier transform (STFT) in order to determine the corresponding spectral frequency components in a temporal window (preferably with a temporal extension of 2 seconds). For the windowing, a Welch window (essentially parabolic characteristic) is used, taking into account a 50% overlap of adjacent intervals. From the frequency distribution (in particular with a spectral resolution of 0.5 Hz) of the signal, the corresponding power values in the various frequency bands (delta, theta, alpha and beta bands, as well as possibly higher frequencies, such as eg Gamma).

From the thus obtained power values in the individual channels or a plurality of channel groups combined according to belonging to a common brain region, the respective median value of the power in the respective frequency band is automatically determined for every 60 second intervals, the individual brain activity of the patient in this time interval should represent.

The thus obtained absolute values of the power of the respective frequency bands in the individual channels or regionally grouped channel groups are averaged over a period of one minute.

To differentiate focal changes from global changes, which may have a variety of causes (increase in intracranial pressure, iatrogenic change in sedation depth, metabolic processes, etc.) in one step 24 Detrending: In 60s EEG intervals, the median of the power of each channel per frequency band is calculated. The absolute power value of each channel is then divided by this median. This removes generalized EEG changes over time from the individual EEG channels. The resulting from the spectral analysis traces of the EEG signal are thus adjusted trend.

To detect signs of vasoplasmic cortical ischemia and the resulting compromise of brain function will be in one step 25 First, a characteristic of the absence of vasoplasmic signal waveform ("baseline") of the trend-corrected EEG signal determined.

The baseline is determined from the EEG signal recorded during the first 6 to 24 hours after subarachnoid hemorrhage at the beginning of EEG monitoring. This is based on the finding that no cerebral vasospasms are regularly present during this period. Rather, the occurrence of vasospasm after clinical studies is expected from the third day after the subarachnoid hemorrhage.

Significant deviations D of individual traces (eg decrease in alpha power) from this baseline are displayed in real time numerically or graphically (trend curve, color bar, etc) visually on the screen 10 shown. The attending physician will be shown in this way threatening events clearly. The treating physician can thereby recognize signs of a DCI fail-safe without the need for expert knowledge regarding EEG diagnostics.

Optionally, in one step 26 Additionally, an alarm A is automatically triggered when one or more lanes deviate from the baseline by more than a predetermined tolerance (eg, 40% of baseline) for a certain amount of time (eg, more than 4 hours). The alarm A can according to 1 as - in particular conspicuous - visual indication of the screen 10 be issued. Additionally or alternatively, the alarm A can be generated in other ways, for. B. by turning on a warning light, generation of an acoustic signal, automatic sending of an e-mail, SMS, etc.

If the generation of the alarm A according to step 26 is provided, the constant display of the trend-adjusted EEG signal and its deviation A from the baseline can also be omitted. Thus, in a variant of the method, it is provided that the trend-corrected EEG signal and its deviation A from the baseline only on user interaction, in particular on request by the attending physician on the screen 10 is shown.

The steps 20 to 26 are performed parallel to each other. The recorded EEG signal E is thus already during the duration of the recording (steps 20 . 21 ) (steps 22 to 26 ).

By the method described above, spasms can be detected earlier by several hours or days than by previous methods (in particular transcranial Doppler sonography, CCT). Thus, it is possible to dramatically advance the therapeutic window of this serious complication of SAB.

Description of a pilot study carried out by the method:

Aneurysmal subarachnoid hemorrhage (SAB) is still associated with high morbidity and mortality. (1) Delayed cerebral ischaemia (DCI) has a significant impact on mortality and functional outcome. (3) Vasospasm, defined as an arterial narrowing measurable by angiography or Trascranial Doppler sonography (TCD), was strongly associated with DCI. (4-5) However, there is evidence to suggest that DCI may also be the result of other mechanisms, such as cortical spreading depression or microcirculatory dysfunction, which can not be detected by TCD. (5-6) Approximately 20% of DCAB after SAB are not clinically detected or identified too late, mostly due to limited clinical assessment in severely affected comatose SAB patients. (7) The early detection of impending ischemia, which is the prerequisite for the prevention of infarcts, therefore remains a challenge.

The EEG sensitively reflects metabolic deterioration resulting from decreased cerebral blood flow. (8-9) It has been shown that EEG changes such as flattening and loss of higher frequencies occur rapidly when cerebral blood flow drops below 0.16-0.17 ml · g ¹ · min ¹ . (10-11) Continuous EEG monitoring has been shown to be a promising approach for the early detection of impending DCI in SAB patients. (12-19) Powerspectral analysis of crude EEG data provides a fast and robust screening method for long-term EEG recordings. (20) Earlier studies (15), (12) reported their information from artifact-free, visually pre-selected EEG clips (13), our goal was to develop a semiautomatic method to show and analyze trends in the unselected multichannel EEG over several days. Hereby, we wanted to provide a real-time analysis method that also enables non-EEG experts in everyday life to reliably detect and locate DCI-related threats at their earliest possible date.

Participation in the study was offered to all patients enrolled in the Neurological Intensive Case Unit (NICO) at the Erlangen University Hospital between November 2011 and February 2013 and who suffered from any non-traumatic SAB of any clinical severity. The diagnosis had to be confirmed by a CT scan or, if negative, by xanthochromia of the CSF (SF). The following inclusion criteria had to be met: age> 18 years, symptom onset 24 hours before admission. Patients were excluded from the study for angiographically or sonographically proven vasospasm prior to EEG monitoring. The study was endorsed by the local ethics committee. All patients or their relatives signed a written statement before enrollment.

All patients received diagnostic angiography on the day of admission. In all patients, SAB was caused by aneurysmal bleeding. All aneurysms were treated by endovascular coiling. An extraventricular drainage was introduced in case of an imminent or existing CSF disturbance. All patients were treated with nimodipine. (22) Our current SOP included monitoring of intracranial pressure, systolic, diastolic and mean arterial blood pressure, heart rate, temperature and oxygen saturation. Neurological status was assessed at least three times daily by ICU staff. All patients received transcranial Doppler (TCD) or duplex (TCCS) scans at least every other day. CT examinations were performed with clinical deterioration or increase in Doppler frequencies.

Relevant vasospasm was diagnosed by TCD (defined as systolic peak frequencies above 4 kHz) or TCCS (defined as flow rates> 200 cm / s or an increase of> 50 cm / s in 24 hours in accordance with the guidelines of the German Neurological Society) or by angiography. The clinical criteria of DCI were a new focal neurological deficit that was not caused by other conditions such as an increase in intracranial pressure or rebleeding.

In the case of relevant vasospasms or DCI, a triple H therapy (hypervolemia, hypertension, hemodilution) was started. (23) In persistence of neurological deficits, patients received interventional angiography and were treated by chemical vasospasmolysis or balloon angioplasty. (24-25)

EEG monitoring was started within the first 48 hours of admission and maintained for a maximum of 12 days, but interrupted earlier if the patient was discharged from the intensive care unit or at the request of the patient or relative. We used surface cup electrodes, fixed with collodion. We restricted the EEG to a limited number of 10 electrodes, attached to the international 10-20 system: C3 / C4, P3 / P4, O2 / O1, T3 / T4, F3 / F4 (alternatively, if unavoidable because of head dressings F5 / 6 instead of F3 / F4). Simplified EEG montages are widely used and successfully used for EEG monitoring under intensive care conditions, although general recommendations regarding optimal assembly and number of electrodes are still lacking. (26), (13), (12) Our goal was a compromise between stably maintaining record and coverage of all vascular territories. The EEG data was digitized with a sampling rate of 256 Hz and a 0.08 Hz high pass filter and a low pass filter of 86 Hz. We used a bipolar mount (F4-C4, T4-P4, P4-O2, F3-C3, T3-P3, P3-O1), hypothesized that F4-C4 (F3-C3) and T4-P4 (T4-P3) represent the anterior and medial canal area during P4-O2 (P3-O1) changes in the posterior Capture circulation.

To perform a reliable semi-automatic qEEG analysis, automated artifact cleansing is essential. To achieve this, we used a standardized and efficient technique that has been described and evaluated in detail in previous work. (20, 27)

The remaining raw EEG signal went through a two second short-term Fourier transform with a 50% overlap of adjacent epochs. The frequency distribution in each epoch was identified, which allowed the power values in the following frequency bands to be determined: delta 0.5-3.5 Hz, theta 4-7.5 Hz, alpha 8-12 Hz, beta 12.5-22 Hz. The median of each minute of power values in these frequency bands and the alpha-delta ratio were calculated separately to produce a linear time series for further analysis.

All EEG channels are subject to common variations due to global effects such as changes in intracranial pressure, depth of sedation and analgesia or circadian rhythm. In order to unmask focal changes that we posit to be correlates of impending DCI, several procedures were needed.

Because all channels in their power are highly correlated with each other over time (R> 0.92, p <0.001 for each combination of channels) - even though they have different power levels - we established a trend cleanup procedure that eliminated common variations and uncovered regional power drops.

First, each median one-minute power value was divided by the median of all 1-minute power values of the same channel over 24-hour EEG recording for normalization. Second, the trend adjustment followed. For this purpose, the median of all normalized power values of the 6 channels was calculated at one point in time in order to generate the normalized common trend of all channels. Each channel was then shared by the common trend to eliminate common variations and emphasize focal changes.

In order to detect focal EEG changes over time from day two of the monitoring, the median was 60 normalized and trimmed one minute powers per hour and in each channel compared to the median of the normalized and trend adjusted power values of the same channel of the baseline period. The baseline was defined as a minimum of 6 and a maximum of 12 hours of uninterrupted EEG recording within the first 24 hours of EEG monitoring, assuming that there are no vasospasms or DCI present at this time.

We correlated each patient's qEEG changes individually with vasospasm or DCI detected by TDC / TCCS or CT / MRI. To calculate the strength of the association between qEEG changes and DCI, we used receiver operator characteristics (ROC), looking at each hemisphere separately. In order to explore the validity of the level and duration of EEG power depletion, we assessed the sensitivity and specificity for various power-draw scenarios, from 10 to 100%, and consistently a minimum of 1 to a maximum of 8 hours. To the optimum threshold on the ROC curve We calculated the Youden Index (J), a function of sensitivity and specificity (28). The Youden index provides the optimal cut-off for the differentiation strength of a marker, if sensitivity and specificity are weighted equally. It moves between 0 and 1, with values near 1 illustrating the effectiveness of the marker.

Of 22 consecutive SAB patients enrolled during the study phase, 12 met the inclusion criteria and were included. 5 patients were sedated and mechanically ventilated during the entire EEG monitoring, 7 patients were awake and spontaneously breathing.

Fifty percent (N = 6) of the patients developed vasospasms (Table 2). The media (MCA) and anterior stromal areas (ACA) were most commonly affected. Isolated vasospasm of the posterior cerebral artery (PCA) and basilar artery (BA) did not occur. In two patients, vasospasm led to infarcts, visible by CT / MRI. One of these patients died as a result of a complete media and anterior infarction.

Decreases in total alpha and theta capacity showed the strongest association with vasospasms / DCI (Youden Index J = 0.79 point, Table 3). In both frequency bands a power decrease of 40% marked the optimal threshold. The duration of the power decrease, which had the highest significance, differed slightly; it was five hours for alpha and six hours for theta.

The optimal combined cut-off for power loss and duration proved to be highly sensitive (100%) with a specificity of 78% for both frequency bands (Table 3). Changes in the beta band showed a lower correlation with vasospasms (DCI, presumably due to contamination with muscle artifacts.) The weakest correlation existed between DCI and delta power variations.

Consequently, the alpha-delta ratio showed only a moderate correlation with vasospasms / DCI (sensitivity 62.5%, specificity 85.7%). In 5 out of 6 cases, EEG changes preceded vasospamen / DCI detection by TCD or CCT / MRI. In Patient 2, TCD vasospasm detection was 4 days ahead of EEG power loss. On average, EEG power decreases occurred 2-3 days earlier (SD 3.3, area 2.5) than detection of vasospasm / DCI in TCD / TCCS or CCT / MRI.

In patient 10, the Alpha-Power dropped significantly in the left PCA region for 31 hours without evidence of vasospasm / DCI in TCD / TCCS or CCT / MRI. Visual inspection of the original EEG at that time revealed focal slowing and amplitude flattening without artifacts obscuring the EEG. Patient 12 exhibited ubiquitously accelerated flow rates in TCD / TCCS on day 4 (Table 2), which did not reflect the EEG. The patient, however, did not develop DCI and recovered without neurological deficits. In addition, the patient had hemolytic anemia with hemoglobin levels that dropped below 100 g / l from day 4 after symptom onset and did not recover until day 13, in parallel with TCCS changes. We therefore considered anemia to be the cause of TCCS flux acceleration, while we found the assumption of ubiquitous critical vasospasms, which did not lead to infarcts or neurological deficits, to be less plausible.

Patient 9 had a pronounced alpha decrease bilaterally, starting on day 3. TCD detected vasospasm in the right MCA on day 7. During the entire monitoring, no vasospasm was seen on the left side. While the Alphapower of the right hemisphere recovered in the following days, it continued to fall in all channels on the left side. CCT and MRI on day 10 showed complete infarction in the left anterior and medial canal area, resulting in death of the patient.

The aim of the study was to develop an automated, observer-independent, EEG-based method to identify impending infarcts in SAB patients. To ensure clinical applicability, we applied a new EEG algorithm based on continuous EEG monitoring without visual preselection of specific EEG sections. In this setting, a 40% decrease in alpha and theta power lasting longer than 5 and 6 hours, respectively, showed a strong association with vasospasms / DCI and was able to complete this complication 2, 3 days before detection by other methods such as TCD in the Predict most cases.

However, the approach has been challenging in several ways. First, maintaining high quality EEG monitoring over several days and intensive care conditions is difficult and laborious. (29) Secondly, it is inevitable that EEG leads in the ICU are contaminated by various artifacts, and third, the EEG sensitively but nonspecifically responds to various systemic changes that make the EEG difficult to DCI other factors, such as metabolic changes, changes in intracranial pressure, or sedation effects (30-31). We tried to overcome these difficulties by using a reduced yet multichannel assembly that could be sustained over several days at a reasonable cost and yet able to reliably detect focal changes. In addition, we implemented an efficient automatic artifact elimination that has been extensively evaluated in previous studies (20, 27).

This allowed the undistorted analysis of changes in the alpha, theta, and delta bands. However, it was impossible to automatically differentiate between beta activity and muscle artifacts, resulting in low usability of beta fluctuations. The key to distinguishing DCI and other induced EEG changes has been the observation of vasospasms as primarily focal events, (32-33) while most other events that are reflected in the intensive care patient EEG cause global changes. Due to the trend adjustment, generalized fluctuations are excluded from the further analysis, whereby focal changes in the EEG are made visible. A theoretical disadvantage of this approach could be the lack of evidence of global changes that can not be predicted with certainty that they are not also caused by the SAB. In any case, in 5 of 6 patients in our cohort, DCI was clearly confined to one or two adjacent arterial flow areas, and could be so well captured by the algorithm. One patient had ubiquitously increased flow rates in TCD / TCCS, which were more likely to be due to anemia (34-36) than to generalized vasospasm. In addition, the patient developed no infarction or neurological deficits. The EEG was not susceptible to this particular source of error and was therefore superior to the TCCS here.

To be a reliable marker for DCI, alpha and theta power had to be held for a certain amount of time. The optimal cut-off duration was slightly shorter for alpha than for theta. Accordingly, alpha appears to be the preferred parameter in both conscious and comatose patients. In our cohort, physiological variations such as sleep-wake patterns (including alpha rhythm) were symmetrical in their appearance, at least partially below trend trimming, below the power threshold associated with vasospasms / DCI. The method therefore seems to be as applicable for less severely affected awake patients as severely affected SAB patients.

One patient in our cohort without vasospasm / DCI detection in TCD and imaging had a significant left occipital alpha decrease, which according to the study protocol had to be interpreted as false positive. Since thetapower also decreased in this patient, an alternative explanation would be that the sensitive EEG correctly identified diminished cortical perfusion as a sign of impending DCI in this area, but therapeutic measures or the natural course of the disease prevented the perfusion deficit from increasing developed a permanent and more severe DCI, which would have been reflected in other diagnostic procedures.

Unexpectedly, delta power changes and the alpha-delta-power ratio showed only a weak to moderate correlation with vasospasms in our cohort. At first glance, this seems contradictory to other studies. Claassen and his colleagues saw a high probability of developing patients with an alpha-delta-power ratio that declined by more than 10% in 6 consecutive measurements or by more than 50% in a single shot to develop DCI. (15) Stuart et al. also described the alpha-delta-power ratio as the most predictive marker of DCI in the intracortical EEG. (14) One possible explanation for the divergent findings could be a hypothesis raised by Machado et al. (37), who used qEEG in patients with median infarctions and found that delta power increases occurred in the ischemic center, while endangered tissue, penumbra, and environmental edema were characterized by alpha and theta changes. Similarly, alpha in our study marks an early point in time when perfusion drops below a critical level and produces endangered tissue but no permanent ischemia. This thesis is supported by patient 3, who developed an infarct during the course of the disease.

The delta power increased two days after the alpha decrease, probably as an expression of now irreversible ischemia ( 4 ). In this context, the alpha-delta-power ratio could be a valuable parameter for predicting and predicting the patient's functional outcome, while alpha power could be the superior parameter to prevent infarction by countermeasures.

Claassen and his co-workers described a strong correlation between a drop in the alpha-delta-power ratio and DCI (100% sensitivity, 76% specificity), with EEG experts evaluating visually pre-selected, artifact-free clips (15).

Our approach introduces an automated technique that does not require the presence of an EEG expert to select and analyze the EEG and is therefore suitable for daily use by ICU staff. Vespa et al. chose a similar approach and identified relative alpha variability (RA) as the most sensitive indicator of impending ischemia. (12) Although highly sensitive, RA did not respond exclusively to vasospasms / DCI but fell significantly as a result of various other events, e.g. Increase in intracranial pressure or hemiation, resulting in low specificity (50%). Labar et al. used a 2-channel EEG and compared total power changes by applying stable EEG trends from day to day and left versus right against each other. (13) To be considered stable, a trend in this study had to last for 24 hours. The algorithm of our study has already shown a focal alpha decrease over five hours as highly sensitive and specific for vasospasm / DCI.

Our study has several limitations. First, the small number of patients limits the statistical power of the results. The results must therefore be confirmed in larger cohorts. Another restriction of the algorithm is the need for a vasospasm-free baseline EEG. The method was therefore not used in patients who were admitted to our clinic later in the course of their illness, with existing vasospasm.

In summary, our study introduces an automated, easy-to-use, investigator-independent, reliable, and robust ischemia-detecting algorithm for SAB patients, which can be done early enough to take countermeasures that may still prevent infarction. In contrast to established methods such as TCD or imaging, the real-time nature of the method provides the basis for an EEG-based alarm system with a high potential for improving the functional outcome of SAB patients.

Brief Description:

3 - Correlation of Alpha Power, TCD and Imaging: Patient 9, SAH III °, aneurysm of the anterior communicating artery; A. TCD plot of critical vasospasm of right MCA on day 7-10; no frequency increase in the left MCA; B. EEG Alpha Power: significant decrease in aplha power in all channels on days 3 and 4 (black arrow); Recovery of alpha power in channels T4P4 and F8C4 on day 6/7 (gray bearing); no recovery in T3P3 / F7C3; C. CCT and D. Perfusion-weighted MRI on day 10 shows complete infarction of the left ACA and MCA area (CCT and MRI courtesy of A. Doerfler, MD, Department of Neuroradiology, University Hospital Erlangen)

4 Alpha and delta power during various ischemic conditions: patient 3, SAH III, aneurysm of the anterior communicating artery. A. Critical vasospasm in the left and right ACA on day 9, detected by TCD, B. Decrease in alpha power on days 4 and 5 in F8C4 and F7C3 (black arrow), no relevant EEG changes in T4P4 and T3P3; C. Increase in delta power in F8C4 and F7C3 on day 7/8 (gray arrow), D. moderate decrease in alpha / delta ratio from day 6 (black arrow).

5 - Statistical analysis (supplementary material): Youden index (J) for different frequency bands. Percentage of power decrease (y-axis) plotted against the Dazer's power increase in hours (x-axis). Youden index rendered by gray scale, according to the bar shown on the right. High J values, represented by bright gray values, imply a strong correlation. Best results were in the alpha band (J = 0.79, optimal threshold 40% drop, 5 hours duration) and in the theta band (J = 0.79, optimal threshold 40% drop, 6 hours duration). Changes in beta, delta, and alpha-delta ratios were less sensitive and specific for DCI (beta: J = 0.59, delta: J = 0.34, alpha-delta ratio: J = 0.48). ROC curve for varying power acceptance values and fixed duration (5 hours); for AUC values see Table 3.

references

• 1. Johnston SC, Selvin S, Gress DR. The burden, trends, and demographics of mortality from subarachnoid hemorrhage. Neurology 1998; 50: 1413-1418.
• Second Rabinstein AA, Friedman JA, Weigand SD, et al. Predictors of cerebral infarction in aneurysmal subarachnoid hemorrhage. Stroke 2004; 35: 1862-1866.
• Third Vergouwen MD, Ilodigwe D, Macdonald RL. Cerebral infarction after subarachnoid hemorrhage to vasospasm-dependent and -independent effects. Stroke 2011; 42: 924-929.
• 4th Crowley RW, Medel R, Dumont AS, et al. Angiographic vasospasm is strongly correlated with cerebral infarction after subarachnoid hemorrhage. Stroke 2011; 42: 919-923.
• 5th Brown RJ, Kumar A, Dhar R, Sampson TR, Diringer MN. The relationship between delayed infarcts and angiographic vasospasm after aneurysmal subarachnoid hemorrhage. Neurosurgery 2013; 72: 702-708.
• 6th Threesome JP. The role of spreading depression, spreading depolarization and spreading ischemia in neurological disease. Nat Med 2011; 17: 439-447.
• 7th Schmidt JM, Wartenberg KE, Fernandez A, et al. Frequency and clinical impact of asymptomatic cerebral infarction due to vasospasm after subarachnoid hemorrhage. J Neurosurg 2008; 10911052-1059.
• 8th. Astrup J, Siesjo BK, Symon L. Thresholds in cerebral ischemia - the ischemic penumbra. Stroke 1981; 12: 723-725.
• 9th O'Gorman RL, Poil SS, Brandeis D, et al. Coupling Between Resting Cerebral Perfusion and EEG. Brain Topogr 2012.
• 10th Sundt TM, Jr., Sharbrough FW, Anderson RE, Michenfelder JD. Cerebral blood flow measurements and electroencephalograms during carotid endarterectomy. J Neurosurg 1974; 41: 310-320.
• 11th Diedler J, Sykora M, Bast T, et al. Quantitative EEG correlates of low cerebral perfusion in severe stroke. Neurocrit Care 2009; 11: 210-216.
• 12th Vespa PM, Nuwer MR, Juhasz C, et al. Ear1y detection of vasospasm after acute subarachnoid hemorrhage using continuous EEG ICU monitoring. Electroencephalogr Clin Neurophysiol 1997; 103: 607-615.
• 13th Labar DR, Fish BJ, Pedley TA, Finch ME, Solomon RA. Quantitative EEG monitoring for patients with subarachnoid hemorrhage. Electroencephalogram Clin Neurophysiol 1991; 78: 325-332.
• 14th Stuart RM, Waziri A, Weintraub D, et al. Intracortical EEG for the detection of vasospasm in patients with poor-grade subarachnoid hemorrhage. Neurocrit Care 2010; 13: 355-358.
• 15th Claassen J, Hirsch LJ, Kreiter KT, et al. Quantitative continuous EEG for detecting delayed cerebral ischemia in patients with poor-grade subarachnoid hemorrhage. Clin Neurophysiol 2004; 1 1512699-2710.
• 16th Claassen J, Taccone FS, HornP, Holtkamp M, Stocchetti N, Oddo M. Recommendations on the use of EEG monitoring in critically ill patients: consensus statement from the neurointensive care section of the ESICM. Intensive Care Med 2013.
• 17th Rathakrishnan R, Gotman J, Dubeau F, Angle M. Using continuous electroencephalography in the management of delayed cerebral ischemia following subarachnoid hemorrhage. Neurocrit Care 2011; 14: 152-161.
• 18th Rivierez M, Landau-Ferey J, Grob R, Grosskopf D, Philippon J. Value of electroencephalogram in prediction and diagnosis of vasospasm after intracranial aneurysm rupture. Acta Neurochir (Vienna) 1991; 110: 17-23.
• 19th Claassen J, Taccone FS, Horn P, Holtkamp M, Stocchetti N, Oddo M. Recommendations on the use of EEG monitoring in critically ill patients: consensus statement from the neurointensive care section of the ESICM. Intensive Care Med 2013; 39: 1337-1351.
• 20th Hopfengartner R, Kerling F, Bauer V, Stefan H. An efficient, robust and fast method for the offline detection of epileptic seizures in long-term scalp EEG recordings. Clin Neurophysiol 2007; 118: 2332-2343.
• 21st Fisher CM, Kistler JP, Davis JM. Relation of cerebral vasospasm to subarachnoid hemorrhage visualized by computerized tomographic scanning. Neurosurgery 1980; 6: 1-9.
• 22nd Kronvall E, Undren P, Romner B, Saveland H, Cronqvist M, Nilsson OG. Nimodipine in aneurysmal subarachnoid hemorrhage: a randomized study of intravenous or peroral administration. J Neurosurg 2009; 110: 58-63.
• 23rd Treggiari MM. Hemodynamic management of subarachnoid hemorrhage. Neurocrit Care 2011; 15: 329-335.
• 24th Aburto-Murrieta Y, Marquez-Romero JM, Bonifacio-Delgadillo D, Lopez I, Hernandez-Curiel B. Endovascular treatment: balloon angioplasty versus nimodipine intra-arterial for medically refractory cerebral vasospasm following aneurysmal subarachnoid hemorrhage. Vasc Endovascular Surg 2012; 46: 460-465.
• 25th Sayama CM, Liu JK, Couldwell WT. Update on endovascular therapies for cerebral vasospasm induced by aneurysmal subarachnoid hemorrhage. Neurosurgical Focus 2006; 21: E12.
• 26th Kull LL, Emerson RG. Continuous EEG monitoring in the intensive care unit: technical and staffing considerations. J Clin Neurophysiol 2005; 22: 107-118.
• 27th Hopfengartner R. Automatic Seizure Detection in Long-term Scalp EEG Monitoring Using an Adaptive Thresholding Technique: A Validation Study for Clinical Routine. (submitted) 2013.
• 28th Youden WJ. Index for rating diagnostic tests. Cancer 1950; 3: 32-35.
• 29th Young GB, Campbell VC. EEG monitoring in the intensive care unit: pitfalls and caveats. J Clin Neurophysiol 1999; 16: 40-45.
• 30th Huang Z, Dong W, Yan Y, Xiao Q, Man Y. Effects of intravenous mannitol on EEG recordings in stroke patients. Clin Neurophysiol 2002; 113: 446-453.
• 31st Huang Z, Dong W, Yan Y, Xiao Q, Man Y. Effects of intravenous human albumin and furosemide on EEG recordings in patients with intracerebral hemorrhage. Clin Neurophysiol 2002; 113: 454-458.
• 32nd Miller CM, Palestrant D. Distribution of delayed ischemic neurological deficits after aneurysmal subarachnoid hemorrhage and implications for regional neuromonitoring. Clin Neurol Neurosurg 2012; 114: 545-549.
• 33rd Thut G, Nietzel A, Brandt SA, Pascual-Leone A. Alpha-band electroencephalographic activity over occipital cortex indexes visuospatial attention bias and predicts visual target detection. J Neurosci 2006; 26: 9494-9502.
• 34th Brass LM, Pavlakis SG, DeVivo D, Piomelli S, Mohr JP. Transcranial Doppler measurements of the middle cerebral artery. Effect of hematocrit. Stroke 1988; 19: 1466-1469.
• 35th Ameriso SF, Paganini-Hill A, Meiselman HJ, Fisher M. Correlates of middle cerebral artery blood velocity in the elderly. Stroke 1990; 21: 1579-1583.
• 36th Isikay CT, Uzuner N, Gucuyener D, Ozdemir G. The effects of hematocrit and age on transcranial Doppler measurements in patients with recent ischemic stroke. Neurol India 2005; 53: 51-54; discussion 54.
• 37th Machado C, Cuspineda E, Valdes P, et al. Assessing acute middle cerebral artery ischemic stroke by quantitative electric tomography. Clin EEG Neurosci 2004; 35: 116-124.
• 38th Castle House L, Hilker R, Dohmen C, et al. Early electroencephalography in acute ischemic stroke: prediction of a malignant course? Clin Neurol Neurosurg 2007; 109: 45-49.
• 39th Sheorajpanday RV, Nagels G, Weeren AJ, van Putten MJ, De Deyn PP. Quantitative EEG in ischemic stroke: correlation with functional status after 6 months. Clin Neurophysiol 2011; 122: 874-883.
• Sheorajpanday RV, Nagels G, Weeren AJ, De Deyn PP. Quantitative EEG in ischemic stroke: correlation with infarct volume and functional status in posterior circulation and lacunar syndromes. Clin Neurophysiol 2011; 122: 884-890.

Tables 1 to 3: Table 1: Patient characteristics

Table 2: Identification and localization of vasospasm / DCI by different modalities (qEEG, TCD, CCT / MRI)

Table 3: Relationship between the decrease in qEEG power and vasospasms / DCI

LIST OF REFERENCE NUMBERS

1

contraption

2

EEG machine

3

tax calculator

4

Data transmission line

5

control program

6

electrodes

7

patient support

8th

patient

9

head

10

screen

20

step

21

step

22

step

23

step

24

step

25

step

26

step

C

command

e

EEG signal

D

deviation

A

alarm

QUOTES INCLUDE IN THE DESCRIPTION

This list of the documents listed by the applicant has been generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Cited non-patent literature

• Stuart et al. [0061]
• Machado et al. [0061]
• Vespa et al. [0064]
• Labar et al. [0064]

Claims (10)

Method for measuring the cerebral perfusion of a patient ( 8th ) comprising the following steps: - Continuous long-term derivation and storage of an EEG signal (E) of the patient ( 8th ); - clean up the EEG signal (E) of artifacts; Power spectral analysis of the artifact-adjusted EEG signal in certain frequency bands; - cleaning the tracks obtained by the power spectral analysis by a common trend of all tracks; - detecting deviations (D) of the trend-adjusted power values of at least one track from a baseline; and - indicating significant deviations (D) and / or triggering an alarm (A) when a significant deviation (D) is detected. The method of claim 1, wherein the EEG signal (E) is recorded for at least 4 days. Method according to claim 1 or 2, wherein an electrode arrangement according to the 10-20 system or a subgroup of the electrodes provided according to this system is used for the detection of the EEG signal (E). Method according to one of claims 1 to 3, wherein for the correction of the EEG signal (E) of artifacts, each channel of the EEG signal (E) is subdivided into temporal intervals, wherein in each interval an average value of the amplitude per channel of the EEG signal Signal (E) is calculated, this average is subtracted from each sample signal value of the EEG signal (E), from this new signal in each interval, the maximum value and the minimum value and the difference of these two extreme values are determined, and wherein any interval of a channel in which the amount of this difference exceeds a certain specified threshold is disregarded for further analysis. Method according to one of claims 1 to 4, wherein the artifact-adjusted EEG signal of the power spectral analysis at least in the frequency bands delta (0.5-3.5 Hz), theta (4-7.5 Hz), alpha (8-12 Hz) and beta (12.5-22 Hz). Method according to one of claims 1 to 5, wherein at least two channels of the artifact-adjusted EEG signal, which are assigned to different brain regions or vessel flow areas, are considered separately for the power spectral analysis. Method according to one of claims 1 to 5, wherein calculated for the trend adjustment, the common variations of all derivatives and the individual derivatives are then calculated with the common fluctuation. The method of claim 7, wherein for trending, the median of the power history in each track is first calculated, each track being normalized with its median, the median track being formed from the averaged tracks, and each of the original tracks being represented by the thus calculated trend track is shared. Method according to one of Claims 1 to 9, wherein the baseline is calculated from a part of the EEG signal (E) recorded at the beginning of the EEG recording, in particular during the first 6 to 24 hours after the start of the EEG recording. Contraption ( 1 ) for measuring the cerebral perfusion of a patient ( 8th ), with an EEG device ( 2 ) for detecting an EEG signal (E) of the patient ( 8th ) and one with the EEG device ( 2 ) Data communication technically interconnected control unit ( 3 ), the control unit ( 3 ) is arranged for automatically carrying out the method according to one of claims 1 to 9.

Images