(19)
(11)EP 2 544 588 B1

(12)EUROPEAN PATENT SPECIFICATION

(45)Mention of the grant of the patent:
29.07.2020 Bulletin 2020/31

(21)Application number: 11710597.3

(22)Date of filing:  09.03.2011
(51)International Patent Classification (IPC): 
A61B 5/0476(2006.01)
(86)International application number:
PCT/US2011/027651
(87)International publication number:
WO 2011/112652 (15.09.2011 Gazette  2011/37)

(54)

METHOD AND DEVICE FOR REMOVING EEG ARTIFACTS

VERFAHREN UND VORRICHTUNG ZUR ENTFERNUNG VON EEG-ARTEFAKTEN

PROCÉDÉ ET DISPOSITIF DESTINÉS À ÉLIMINER LES ARTEFACTS SUR LES EEG


(84)Designated Contracting States:
AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

(30)Priority: 10.03.2010 US 720861

(43)Date of publication of application:
16.01.2013 Bulletin 2013/03

(73)Proprietors:
  • Brainscope Company, Inc.
    Bethesda, MD 20814 (US)
  • New York University
    New York City, NY 10012 (US)

(72)Inventors:
  • ISENHART, Robert
    Brooklyn NY 11225 (US)
  • JACQUIN, Arnaud
    New York NY 10011 (US)
  • PRICHEP, Leslie
    Mamaroneck NY 10543 (US)

(74)Representative: Finnegan Europe LLP 
1 London Bridge
London SE1 9BG
London SE1 9BG (GB)


(56)References cited: : 
US-A- 4 279 258
US-A- 6 067 467
US-A1- 2010 041 962
US-A- 5 467 777
US-A1- 2007 255 164
  
      
    Note: Within nine months from the publication of the mention of the grant of the European patent, any person may give notice to the European Patent Office of opposition to the European patent granted. Notice of opposition shall be filed in a written reasoned statement. It shall not be deemed to have been filed until the opposition fee has been paid. (Art. 99(1) European Patent Convention).


    Description


    [0001] The present disclosure pertains to devices and methods for collecting brain electrical activity data, and specifically to devices and methods for automatically editing brain electrical activity signals.

    [0002] Automatic analysis of EEG data or other types of brain electrical activity date using, for example, Quantitative Assessment of EEG, requires signals that are free of noise due to physiologic and non-physiologic factors. Attempts at obtaining artifact-free data have included methods for eliminating artifacts from EEG signals, thereby leaving only the underlying brain electrical activity signal, or by identifying EEG segments that contain artifacts and manually editing EEGs to remove segments affected by artifact.

    [0003] Current systems and methods for automatically filtering EEG signals have limited accuracy and may not reliably identify and/or remove all artifacts. In addition, manual editing of EEGs is time consuming and subject to user bias. Accordingly, there is a need for improved methods for automatically identifying EEG artifacts and editing EEGs to remove segments affected by artifacts.
    US 2010/0041962 relates to a headset for detecting brain electrical activity. The headset comprises sensors to sense brain electrical activity. Brain electrical activity may be processed using hardware and software of a base unit. Artifacts may be removed from the data during the processing. The artifacts may be a result of factors such as a disconnected electrode, electromyogram artifacts resulting from muscular movement, and eye movement.
    US 6,067,467, US 5,467,777, US 4,279,258 and US 2007/255164 describe techniques for identifying artifacts in EEG data.

    SUMMARY



    [0004] In accordance with the invention, there is provided: a method for automatically editing brain electrical activity data as recited by claim 1; and a device for automatically editing brain electrical activity data signals as recited by claim 9.

    [0005] A device for automatically editing brain electrical activity data is provided. The device comprises at least two EEG electrodes; a circuit for measuring electrical potential signals from the electrodes; a memory unit configured to store data related to the electrical potential; an analysis unit configured to analyze the signal to determine if temporal segments of the signal include artifacts due to of any of eye movements, cable or electrode movements, impulse artifacts, and muscle activity, and if any segment does include artifacts, identifying the segment as including artifacts; and editing the data in the analysis unit to remove segments that include artifacts.

    [0006] A method for automatically editing brain electrical activity data is provided. The method comprises positioning at least two frontal EEG electrodes on a patient; obtaining a signal representing brain electrical activity in each of the electrodes; analyzing the signal to determine if temporal segments of the signal include artifacts due to of any of eye movements, cable or electrode movements, impulse artifacts, and muscle activity, and if any segment does include artifacts identifying the segment as including artifacts; and editing the signal to remove segments that include artifacts.

    DESCRIPTION OF THE DRAWINGS



    [0007] 

    Fig. 1A illustrates a brain electrical activity monitoring system according to one embodiment of the present disclosure.

    Fig. 1B illustrates a schematic diagram of the monitoring system of Fig. 1A, illustrating additional components.

    Figs. 2A illustrates an electrode set for use with the brain electrical activity monitoring system of the present disclosure.

    Figs. 2B illustrates the electrode set of Fig. 1B, as applied to a patient.

    Fig. 3 shows approximately eight seconds of EEG that includes artifacts produced by vertical eye movements (VEM).

    Fig. 4 shows approximately eight seconds of EEG that includes artifacts produced by horizontal eye movements (HEM).

    Fig. 5 shows approximately eight seconds of EEG that includes artifacts produced by cable/electrode movement (PCM).

    Fig. 6 shows approximately eight seconds of EEG that includes impulse artifacts (IMP).

    Fig. 7 shows approximately eight seconds of EEG that includes artifacts produced by muscle activity (EMG).

    Fig. 8 shows approximately eight seconds of EEG that includes artifacts produced by significantly low-amplitude signal due to Burst Suppression (SLAS).

    Fig. 9 shows approximately eight seconds of EEG that includes Atypical Electrical Activity Pattern (AEAP).


    DESCRIPTION OF EXEMPLARY EMBODIMENTS



    [0008] The present disclosure provides devices and methods for analyzing brain electrical activity, including editing brain electrical activity data to identify and remove brain electrical activity signals that contain certain types of artifacts.

    [0009] As used herein, "EEG signal" or "signal" refers to recordings of cerebral electrical activity, or other types of brain electrically activity, recorded from any location on the cranium. EEG or other brain electrical activity data can be stored as a digital signal in a memory unit. As used herein, "artifacts" or "noise" refers to any electrical potential recorded while obtaining an EEG or other brain electrical activity signal that is not of cerebral origin or is the result of abnormal brain activity. As used herein, "EEG electrode" refers to any electrode placed on a person's head and capable of detecting brain electrical activity. EEG electrodes can be placed according to known positioning systems, such as, the expanded international 10/20 placement system. In addition, as used throughout, EEG can include cerebral electrical activity or other types of brain electrical activity, and it will be understood that the methods of the present disclosure can be used to identify and remove artifacts from any type of brain electrical activity signal.

    [0010] Most systems that rely on quantitative analysis of EEG typically assume that a trained technologist has manually edited the raw data to remove artifacts. However, the editing process can be time-consuming and is inherently subjective. In addition, technologist editing prevents automated monitoring, and therefore, is not suitable for continuous and rapid monitoring (e.g., in an ICU, in a field hospital, at a sporting event, or in typical primary care settings). The following processing techniques can be used to automatically identify and/or remove (e.g., edit out) EEG or other brain electrical activity data segments that include artifacts. This may be accomplished using standard signal processing components, which include digital filtering (low-pass filtering, bandpas filtering, etc.), thresholding, peak detection, and frequency-based processing.

    [0011] There are seven typical types of noise that can contribute to poor signal quality. EEG segments including each of these types of artifacts, as recorded with a limited electrode montage (i.e., 5 electrodes) are shown in Figs. 3-9, with the segment containing artifact data identified by a dark dashed-line box . These EEGs were recorded with electrode impedances under 5 kΩ. The data was sampled at 8 kHz, low-pass filtered to remove signal frequencies above 45 Hz, and downsampled to 100 Hz for purposes of display and editing. These artifacts include (1) horizontal/lateral eye movements (HEM) (see Fig. 3, 300), (2) vertical eye movements (e.g. blinks) (VEM) (see Fig. 4, 400), (3) cable or electrode movement causing over-range artifacts (PCM) (see Fig. 5, 500), (4) impulse artifacts (for example due to electrode "pops") (IMP) (see Fig. 6, 600), (5) electromyographic activity (also referred to as "muscle activity") (EMG) (see Fig. 7, 700), (6) significantly low amplitude signal (for example as a result of the suppression component of "burst suppression") (SLAS) (see Fig. 8, 800), and (7) atypical electrical activity pattern (for example due to paroxysmal brain activity) (AEAP) (see Fig. 9, 900). Out of these seven artifact types, two are non-physiological (type 3, type 4), three are physiological, but are not brain-generated (type 1, 2, type 5) and two are brain-generated (type 6, type 7). All of these artifacts reflect either non-brain electrical activity or abnormal brain-electrical activity.

    [0012] The present disclosure provides a comprehensive, fully-automated, artifact detection system, mimicking the ability of trained EEG technologists to edit EEG records. The edited records may be used for subsequent processing and analysis, using, for example, quantitative analyses of brain electrical activity. In certain embodiments, the method and device of the present disclosure can include a limited frontal electrode montage, as described further below.

    [0013] In certain embodiments, the present disclosure provides a device and method for automatically editing EEG signals. In certain embodiments, the method comprises positioning at least two frontal EEG electrodes on a patient, and obtaining a signal representing brain electrical activity in each of the electrodes. The signal can be analyzed to determine if temporal segments of the signal include artifacts due to of any of eye movements, cable or electrode movements, impulse artifacts, and muscle activity, and if any segment does include artifacts identifying the segment as including artifacts. In some embodiments, the signal is edited to remove segments that include artifacts. In some embodiments, the method further includes analyzing the signal to determine if temporal segments of the signal include artifacts due to of any significantly low amplitude signal and atypical electrical activity.

    [0014] A number of different EEG systems can be used to collect data using the methods of the present disclosure. In certain embodiments, the system can be a compact, self-contained device. For example, Fig. 1A illustrates an EEG system 10, according to certain embodiments of the present disclosure. As shown, the system 10 can include an enclosure 20 containing electrical circuitry configured to perform data processing, stimulus generation, and analysis for diagnosis and patient monitoring. In addition, the enclosure 20 may further include a display system 30, such as an LCD or other visual display to provide real-time, easy-to-interpret information related to a patient's clinical status.

    [0015] In some embodiments, the system 10 will include circuitry configured to provide real-time monitoring of brain electrical activity. The system 10 will provide rapid data acquisition, processing, and analysis to allow point-of-care diagnosis and assessment. For example, as shown, the display system 30 can include one or more indicators 35, or visual displays, that are configured to display an easy-to-interpret indication of a patient's status. In one embodiment, the indicators 35 will include an indication of where a patient's status lies relative to a normal data set, a patient's status relative to a base line, and/or one or more indicators of the origin of any abnormalities. In some embodiments, the indicators provide a scale (from normal to severely abnormal). In other embodiments, typical EEGs, as shown in the attached figures may be displayed on the system 30, as recorded and/or after editing.

    [0016] Fig. 1B illustrates a schematic diagram of the monitoring system of Fig. 1A, illustrating additional components. As shown, the enclosure 20, can include a number of component parts. For example, the enclosure 20 may include a memory unit or storage system 22 configured to store data related to patient brain electrical activity data measurements, or a database of normal and/or pathological readings. Further, the enclosure will include circuitry configured to process and evaluate electrical signals and data 24, and a transmitter unit 26.

    [0017] The circuitry 24 can include a number of circuitry types. For example the circuitry 24 can include processing circuitry configured to receive electrical signals from electrodes and to process such signal using filters (e.g., band pass, low pass, and/or high pass filters), as shown in Figs. 2A-2B, and to convert such signals into data that can be further evaluated. In some embodiments, the circuitry can be configured to enable nonlinear processing, including nonlinear amplifiers. Further, the circuitry 24 can also include components configured to allow analysis of processed data and comparison of brain electrical activity data to normal data, or to previous or future measurements, as described in more detail below. Further, it will be understood that, although shown as a single component, multiple components can be included, either on a single chip or multiple chips.

    [0018] The transmitter unit 26 can include a number of transmitter types. For example, the transmitter 26 may include a hardware connection for a cable or a telemetry system configured to transmit data to a more distant receiver 28, or a more powerful transmission system to redirect data to a database 32 that may be stored nearby or at a remote or distant location. In certain embodiments, the data can be transmitted and stored and/or evaluated at a location other than where it is collected.

    [0019] The brain electrical monitoring system 10 may be configured to attach to various patient interfaces. For example, Figs. 2A-2B illustrate an electrode set 50 for use with the system 10 of the present disclosure. As shown, the electrode set 50 includes one or more electrodes 60 for placement along the patient's forehead and mastoid region. As shown, the electrode set 50 includes a limited number of electrodes 60 to facilitate rapid and easily repeated placement of the electrodes 60 for efficient, but accurate, patient monitoring. Further, in one embodiment, the electrodes 60 may be positioned on a head band 70 that is configured for easy and/or rapid placement on a patient, as shown in Fig. 2B. Further, it will be understood that other electrode configurations may be selected, which may include fewer or more electrodes.

    [0020] In certain embodiments, a limited frontal electrode montage can be used to implement the methods of the present disclosure, including at least two electrodes. In some embodiments, the at least two frontal EEG electrodes are positioned at FP1 and FP2 positions based on the expanded international 10/20 placement system. In some embodiments, the at least two frontal EEG electrodes are positioned at F7 and F8 positions based on the expanded international 10/20 placement system. In some embodiments, the electrodes include at least five electrodes positioned at FP1, FP2, F7, F8, and AFz positions based on the expanded international 10/20 placement system.

    [0021] As noted, the electrode set 50 will be operably connected to the monitoring system 10. Generally, the electrodes 60 will be electrically coupled with the monitoring system 10 to allow signals received from the electrodes to be transmitted to the monitoring system 10. Such an electrical coupling will generally be through one or more electrical wires, but nonphysical connections may also be used.

    [0022] To identify artifacts, EEG signals may be analyzed in certain temporal segments or epochs. Generally, the segment duration should be long enough to allow identification of artifacts in question, but as short as possible to minimize editing out segments or data that do not contain artifact. In some embodiments, segments having lengths between 10 to 500 ms are analyzed and/or edited out if they contain EEG artifacts. In one embodiment, the signals are analyzed in approximately 320ms length segments or sub-epochs, although other signal lengths may be used depending, for example, on the type of brain electrical activity being analyzed. In certain embodiments, when editing out segments containing artifacts, data recorded just before and/or after the artifacts may also be edited out. For example, in some embodiments, segments of duration of 320 ms occurring immediately before and immediately after a segment containing artifact are automatically edited out.

    [0023] In certain embodiments, slow lateral eye movements (HEMs) are identified, and brain electrical activity data segments containing lateral eye movement artifacts are edited out of the signal. In certain embodiments, HEM artifacts are identified as waveforms of 1 Hz or less that have opposite polarity at F7 and F8. Each of the two EEG channels F7 and F8 may band-pass filtered using an FIR filter with passband 0.5-3 Hz, producing signals F7f and F8f, the high-pass cut-off frequency of 0.5 Hz being chosen to ignore the influence of low-frequency activity occurring at frequencies below the delta_1 band (0.5 - 1.5 Hz). EEG segments containing HEM artifacts are identified wherever the difference signal F7f - F8f exceeds a threshold. In various embodiments, the threshold can be between 10µV and 100µV, or between 10µV and 30µV, or in one embodiment, approximately 24µV.

    [0024] In certain embodiments, vertical eye movement(VEM)/eye opening/eye closing (EOEC) artifacts are identified, and brain electrical activity data segments containing those artifacts are edited out of the signal. Detection of the electrophysiological effect of a vertical eye movement (VEM) (of which eye opening/closing is a sub-type) can performed by locating large excursions ("peaks") on the Fp1 and Fp2 leads. Since both eyes move in unison, only such excursions that occur concurrently and in the same direction (same polarity of the peaks) on Fp1 and Fp2 are identified as vertical eye movements. In some embodiments, each of the two signals Fp1 and Fp2 is first low-pass filtered in the range 0.5-5 Hz. In each segment, runs of samples exceeding a given threshold. In various embodiments, the threshold can be between 10µV and 100µV, or between 10µV and 30µV, or in one embodiment, approximately 24µV. In each such run, the global extremum is located and its value is compared to average signal values on either side of it. If the absolute difference between the extremum and either average exceeds the threshold, the segment is identified as a candidate VEM artifact. After this processing has occurred on both leads, the results are combined to turn candidate VEMs to true VEMs wherever they occurred concurrently on Fp1 and Fp2 as described above. In certain embodiments, determining if temporal segments of the signal include artifacts due to eye movements includes filtering signals obtained from electrodes positioned at the Fp1 and Fp2 positions, comparing each signal to an average signal from the same electrode, determining if the signals exceed a threshold, and if the signal exceeds a threshold, determining if changes in the Fp1 and Fp2 signals occur concurrently, and if the changes do occur concurrently, identifying the segment as including artifacts.

    [0025] In some embodiments, cable or electrode movement (PCM) artifacts, are identified, and brain electrical activity data segments containing those artifacts are edited out of the signal. In some embodiments, determining if temporal segments of the signal include artifacts due to cable or electrode movement includes identifying a signal amplitude greater than a threshold, and if any segment includes an amplitude greater than the threshold, identifying that segement as including cable or electrode movement artifacts. In certain embodiments, the threshold can be between 50µV and 250µV, or between 50µV and 150µV, or, in one embodiment, approximately 120µV.

    [0026] In some embodiments, impulse artifacts are identified, and brain electrical activity data segments containing those artifacts are edited out of the signal. In some embodiments, a frontal EEG channel is first high-pass filtered with cutoff frequency at to remove the alpha-1 band from the signal in that channel. In some embodiments, the cut-off frequency is 15 Hz. Next, high-frequency variations of signal amplitude in successive segments of 100 ms width with 50% overlap are examined. Within each segment, the value (max - min) is computed and trigger an IMP artifact detection when it exceeds a given threshold. In certain embodiments, the threshold can be between 25µV and 250µV, or between 50µV and 125µV, or in one embodiment, approximately 75µV.

    [0027] In some embodiments, muscle activity (EMG) artifacts are identified, and brain electrical activity data segments containing those artifacts are edited out of the signal. This artifact is characterized by high-frequency signals (above 20Hz) occurring in bursts of variable duration. In certain embodiments, muscle movement artifacts are identified by band pass filtering a signal from at least one electrode in the range of the EEG β1 band to produce signal E1 and band pass filtering the same signal in the range of the β2 band to produce signal E2, and if relative energy of E2 relative to E1 exceeds a threshold, identifying the segment as containing muscle movement artifacts. In certain embodiments, the signal is band-pass filtered in the range of 25-35 Hz (β2 band) and 15-25 Hz (β1 band).

    [0028] In some embodiments, brain electrical activity data segments containing significantly low amplitude signal (SLAS) are identified, and brain electrical activity data segments containing those artifacts are edited out of the signal. This artifact is meant to capture extremely low-amplitude EEG signals (at all frequencies) which occur, for example, when the brain is in Burst Suppression mode; a condition which can occur (but should be avoided) during anesthesia. No additional filtering of the signal is used for detection of this activity. In some embodiments, SLAS can be detected by looking for signal epochs with mean-square energy below a threshold. In certain embodiments, the threshold can be between 1µV2 and 25 µV2, or between 10 µV2 and 15 µV2, or, in one embodiment, approximately 12 µV2.

    [0029] In some embodiments, brain electrical activity data segments containing atypical electrical activity pattern (AEAP) are identified, and brain electrical activity data segments containing those artifacts are edited out of the signal. This artifact includes unusual patterns of activity in the signal such as those that occur in the EEG of epileptic subjects during a convulsive or non-convulsive seizure. Such artifacts can be identified using a combination of wavelet analysis and fractal dimension computation, as described in A. Jacquin et al. "Automatic Identification of Spike-Wave Events and Non-Convulsive Seizures with a Reduced Set of Electrodes," Proceedings of the 29th IEEE EMBS International Conference, Lyon, France, Aug. 2007.

    [0030] The methods for automatically identifying and editing out brain electrical activity data segments that contain artifacts has been tested and validated by comparison to manual editing techniques. The process has been found to be suitable for editing recordings from patients with a variety of different pathologies, including, for example, traumatic brain injury with positive imaging, head injuries/concussions with negative or no imaging, subjects who had no head injury or evidence of CNS abnormalities, subjects with strokes or tumors, subjects with alcohol or drug encephalopathies, and other patient populations. In addition, the methods have been used to edit EEG recordings from patients with cerebro-vascular accidents (CVA) who frequently had the characteristic of frontal slow waves in their EEGs, indicating that the methods of the present disclosure remove pathology from the EEG by mistaking it as artifact.

    [0031] Other embodiments will be apparent to those skilled in the art from consideration of the specification and practice of the devices and methods disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope being indicated by the following claims.


    Claims

    1. A method for automatically editing brain electrical activity data received from at least two frontal EEG electrodes (60) positioned on a patient, wherein the method is performed by a device for automatically editing brain electrical activity data signals, the method comprising:

    obtaining a signal representing brain electrical activity based on the two electrodes (60);

    analyzing the signal to determine if a plurality of temporal segments of the signal include an artifact due to lateral eye movements (300), wherein:

    determining if temporal segments of the signal include an artifact due to lateral eye movements (300) includes filtering signals obtained from the electrodes positioned at F7 and F8 positions, determining if the difference between the filtered signals obtained from the electrodes positioned at the F7 and F8 positions exceeds a threshold, and identifying the segment as including the artifact in dependence on the threshold being exceeded; and

    editing the signal to remove the segment that includes the artifact.


     
    2. The method of claim 1, wherein the at least two frontal EEG electrodes (60) are positioned at the F7 and F8 positions based on the expanded international 10/20 placement system.
     
    3. The method of claim 1, wherein the electrodes (60) include at least five electrodes positioned at FP1, FP2, F7, F8, and AFz positions based on the expanded international 10/20 placement system.
     
    4. The method of any one of claims 1-3, further including the step of determining if the artifact includes at least one of a significantly low amplitude signal (800) and an atypical electrical activity (900).
     
    5. The method of claim 4, further including determining if the artifact includes a cable movement or an electrode movement (500) based on identifying a signal amplitude greater than a predetermined threshold, and if any segment includes an amplitude greater than the threshold identifying that segment as including a cable movement or an electrode movement artifact.
     
    6. The method of claim 5, wherein determining if the artifact includes a cable movement or an electrode movement (500) based on identifying a signal amplitude greater than a predetermined threshold, and if any segment includes an amplitude greater than the threshold identifying that segment as including cable or electrode movement artifacts, wherein the threshold is between 50 and 250 µV.
     
    7. The method of any one of claims 1 - 6, further comprising determining if the temporal segment of the signal includes an artifact comprising:

    (i) a muscle movement artifact (700) by band pass filtering a signal from at least one electrode in the range of the EEG β1 band to produce signal E1 and band pass filtering the same signal in the range of the β2 band to produce signal E2, and if relative energy of E2 relative to E1 exceeds a threshold, identifying the segment as containing muscle movement artifacts; and/or

    (ii) a significantly low amplitude signal (800) by determining if the mean square energy of a segment is below a threshold, and if the mean square energy is below a threshold, identifying the segment as the segment including significantly low amplitude signal.


     
    8. The method of any of claims 1 - 7, wherein the predetermined threshold for determining if temporal segments of the signal include an impulse artifact is between 50 and 250 µV.
     
    9. A device for automatically editing brain electrical activity data signals, comprising:

    at least two electrodes (60);

    a circuit for measuring electrical potential signals from the electrodes;

    a memory unit (22) configured to store data related to the electrical potential signals;

    an analysis unit (24) configured to:

    analyze the signals to determine if a plurality of temporal segments of the signals include an artifact, wherein:
    the analysis unit is configured to determine if temporal segments of the signal include an artifact due to lateral eye movements (300) by filtering signals obtained from electrodes positioned at F7 and F8 positions, and determining if the difference between the filtered signals obtained from the electrodes positioned at the F7 and F8 positions exceeds a threshold; and
    identify the segment as including the artifact in dependence on the threshold being exceeded; and

    edit the data in the analysis unit to remove the segment.


     
    10. The device of claim 9, wherein the at least two electrodes (60) are configured to be positioned at the F7 and F8 positions based on the expanded international 10/20 placement system.
     
    11. The device of claim 9, wherein the electrodes include at least five electrodes (60) configured to be positioned at FP1, FP2, F7, F8, and AFz positions based on the expanded international 10/20 placement system.
     
    12. The device of any one of claims 9-11, configured to determine if the artifact includes a cable movement or an electrode movement (500) based on identifying a signal amplitude greater than a predetermined threshold, and if any segment includes an amplitude greater than the threshold, identify that segment as including a cable movement or an electrode movement artifact.
     
    13. The device of any one of claims 9-12, configured to determine if temporal segments of the signal include an artifact comprising:

    (i) a muscle movement artifact (700) by band pass filtering a signal from at least one electrode in the range of the EEG β1 band to produce signal E1 and band pass filtering the same signal in the range of the β2 band to produce signal E2, and if relative energy of E2 relative to E1 exceeds a threshold, identify the segment as containing the muscle movement artifact; and/or

    (ii) a significantly low amplitude signal (800) by determining if the mean square energy of a segment is below a threshold, and if the mean square energy is below a threshold, identify the segment including the significantly low amplitude signal.


     


    Ansprüche

    1. Verfahren zum automatischen Bearbeiten von Daten zur elektrischen Aktivität des Gehirns, die von mindestens zwei an einem Patienten angebrachten frontalen EEG-Elektroden (60) empfangen wurden, wobei das Verfahren von einer Vorrichtung zum automatischen Bearbeiten von Datensignalen zur elektrischen Aktivität des Gehirns durchgeführt wird, das Verfahren Folgendes umfassend:

    Erhalten eines die elektrische Aktivität des Gehirns darstellenden Signals basierend auf den zwei Elektroden (60);

    Analysieren des Signals, um zu bestimmen, ob mehrere zeitliche Segmente des Signals ein von seitlichen Augenbewegungen (300) hervorgerufenes Artefakt aufweisen, wobei:

    das Bestimmen, ob zeitliche Segmente des Signals ein von seitlichen Augenbewegungen (300) hervorgerufenes Artefakt aufweisen, das Filtern von Signalen, die von den an den Positionen F7 und F8 angebrachten Elektroden erhalten wurden, das Bestimmen, ob die Differenz der gefilterten Signale, die von den an den Positionen F7 und F8 angebrachten Elektroden erhalten wurden, einen Schwellenwert überschreitet, und abhängig davon, ob der Schwellenwert überschritten wird, das Identifizieren des Segments als das Artefakt aufweisend beinhaltet; und

    Bearbeiten des Signals, um das Segment zu entfernen, das das Artefakt aufweist.


     
    2. Verfahren nach Anspruch 1, wobei die mindestens zwei frontalen EEG-Elektroden (60) basierend auf dem erweiterten internationalen 10-20-System an den Positionen F7 und F8 angebracht werden.
     
    3. Verfahren nach Anspruch 1, wobei die Elektroden (60) mindestens fünf Elektroden umfassen, die basierend auf dem erweiterten internationalen 10-20-System an den Positionen FP1, FP2, F7, F8 und AFz angebracht werden.
     
    4. Verfahren nach einem der Ansprüche 1 bis 3, ferner umfassend den Schritt des Bestimmens, ob das Artefakt mindestens eines aus einem Signal mit signifikant niedriger Amplitude (800) und einer atypischen elektrischen Aktivität (900) aufweist.
     
    5. Verfahren nach Anspruch 4, ferner umfassend das Bestimmen, ob das Artefakt eine Kabelbewegung oder eine Elektrodenbewegung (500) aufweist, basierend auf dem Identifizieren einer Signalamplitude, die größer als ein vorbestimmter Schwellenwert ist, und falls Segmente eine Amplitude, die größer als der Schwellenwert ist, aufweisen, das Identifizieren dieses Segments als ein Artefakt einer Kabelbewegung oder einer Elektrodenbewegung aufweisend.
     
    6. Verfahren nach Anspruch 5, wobei das Bestimmen, ob das Artefakt eine Kabelbewegung oder eine Elektrodenbewegung (500) aufweist, basierend auf dem Identifizieren einer Signalamplitude, die größer als ein vorbestimmter Schwellenwert ist, und falls ein Segment eine Amplitude, die größer als der Schwellenwert ist, aufweist, das Identifizieren dieses Segments als Artefakte einer Kabelbewegung oder einer Elektrodenbewegung aufweisend, wobei der Schwellenwert zwischen 50 und 250 µV liegt.
     
    7. Verfahren nach einem der Ansprüche 1 bis 6, ferner umfassend das Bestimmen, ob das zeitliche Segment des Signals ein Artefakt aufweist, das Folgendes umfasst:

    (i) ein Muskelbewegungsartefakt (700), durch Bandpassfiltern eines Signals von mindestens einer Elektrode im Bereich des Beta-1-Bandes des EEGs zur Erzeugung des Signals E1 und Bandpassfiltern des gleichen Signals im Bereich des Beta-2-Bandes zur Erzeugung des Signals E2 und, falls die relative Energie von E2 im Verhältnis zu E1 einen Schwellenwert überschreitet, Identifizieren des Segments als Muskelbewegungsartefakte aufweisend; und/oder

    (ii) ein Signal mit signifikant niedriger Amplitude (800), durch Bestimmen, ob die mittlere quadratische Energie eines Segments unter einem Schwellenwert liegt, und, falls die mittlere quadratische Energie unter einem Schwellenwert liegt, Identifizieren des Segments als das Segment, das ein Signal mit signifikant niedriger Amplitude aufweist.


     
    8. Verfahren nach einem der Ansprüche 1 bis 7, wobei der vorbestimmte Schwellenwert zum Bestimmen, ob zeitliche Segmente des Signals ein Impulsartefakt enthalten, zwischen 50 und 250 µV liegt.
     
    9. Gerät zum automatischen Bearbeiten von Datensignalen zur elektrischen Aktivität des Gehirns, umfassend:

    mindestens zwei Elektroden (60);

    eine Schaltung zum Messen der Signale des elektrischen Potentials der Elektroden;

    eine Speichereinheit (22), die dazu konfiguriert ist, Signale des elektrischen Potentials betreffende Daten zu speichern;

    eine Analyseeinheit (24), die dazu konfiguriert ist:
    die Signale zu analysieren, um zu bestimmen, ob mehrere zeitliche Segmente der Signale ein Artefakt aufweisen, wobei:

    die Analyseeinheit, dazu konfiguriert ist, zu bestimmen, ob zeitliche Segmente des Signals ein von seitlichen Augenbewegungen (300) hervorgerufenes Artefakt aufweisen, durch Filtern von Signalen, die von an den Positionen F7 und F8 angebrachten Elektroden erhalten wurden, und Bestimmen, ob die Differenz der gefilterten Signale, die von den an den Positionen F7 und F8 angebrachten Elektroden erhalten wurden, einen Schwellenwert überschreitet; und

    abhängig davon, ob der Schwellenwert überschritten wird, das Segment als das Artefakt aufweisend zu identifizieren; und

    die Daten in der Analyseeinheit zu bearbeiten, um das Segment zu entfernen.


     
    10. Gerät nach Anspruch 9, wobei die mindestens zwei Elektroden (60) dazu konfiguriert sind, basierend auf dem erweiterten internationalen 10-20-System an den Positionen F7 und F8 angebracht zu werden.
     
    11. Gerät nach Anspruch 9, wobei die Elektroden mindestens fünf Elektroden (60) umfassen, die dazu konfiguriert sind, basierend auf dem erweiterten internationalen 10-20-System an den Positionen FP1, FP2, F7, F8 und AFz angebracht zu werden.
     
    12. Gerät nach einem der Ansprüche 9 bis 11, das dazu konfiguriert ist, zu bestimmen, ob das Artefakt eine Kabelbewegung oder eine Elektrodenbewegung (500) aufweist, basierend auf dem Identifizieren einer Signalamplitude, die größer als ein vorbestimmter Schwellenwert ist, und falls ein Segmente eine Amplitude, die größer als der Schwellenwert ist, aufweist, dieses Segment als ein Artefakt einer Kabelbewegung oder einer Elektrodenbewegung aufweisend zu identifizieren.
     
    13. Gerät nach einem der Ansprüche 9 bis 12, das dazu konfiguriert ist, zu bestimmen, ob zeitliche Segmente des Signals ein Artefakt aufweisen, das Folgendes umfasst:

    (i) ein Muskelbewegungsartefakt (700), durch Bandpassfiltern eines Signals von mindestens einer Elektrode im Bereich des Beta-1-Bandes des EEGs zur Erzeugung des Signals E1 und Bandpassfiltern des gleichen Signals im Bereich des Beta-2-Bandes zur Erzeugung des Signals E2 und, falls die relative Energie von E2 im Verhältnis zu E1 einen Schwellenwert überschreitet, das Segment als das Muskelbewegungsartefakt aufweisend zu identifizieren; und/oder

    (ii) ein Signal mit signifikant niedriger Amplitude (800), durch Bestimmen, ob die mittlere quadratische Energie eines Segments unter einem Schwellenwert liegt, und, falls die mittlere quadratische Energie unter einem Schwellenwert liegt, das Segment, das ein Signal mit signifikant niedriger Amplitude aufweist, zu identifizieren.


     


    Revendications

    1. Procédé destiné à éditer automatiquement les données liées à l'activité électrique cérébrale reçues d'au moins deux électrodes frontales d'EEG (60) positionnées sur un patient, le procédé étant réalisé par un dispositif destiné à éditer automatiquement les signaux de données liés à l'activité électrique cérébrale, le procédé comprenant :

    l'obtention d'un signal représentant l'activité électrique cérébrale sur la base des deux électrodes (60) ;

    l'analyse du signal afin de déterminer si une pluralité de segments temporels du signal comportent un artefact dû à des mouvements latéraux des yeux (300), dans lequel :

    la détermination de savoir si des segments temporels du signal comportant un artefact dû à des mouvements latéraux des yeux (300) comporte le filtrage des signaux obtenus à partir des électrodes positionnées aux positions F7 et F8, la détermination de savoir si la différence entre les signaux filtrés obtenus à partir des électrodes positionnées aux positions F7 et F8 dépasse un seuil, et l'identification du segment comme comportant l'artefact en fonction du seuil dépassé ; et à

    l'édition du signal afin d'éliminer le segment comprenant l'artefact.


     
    2. Procédé selon la revendication 1, dans lequel les au moins deux électrodes frontales d'EEG (60) sont positionnées aux positions F7 et F8 sur la base du système de placement international 10/20 élargi.
     
    3. Procédé selon la revendication 1, dans lequel les électrodes (60) comportent au moins cinq électrodes positionnées aux positions FP1, FP2, F7, F8 et AFz sur la base du système de placement international 10/20 élargi.
     
    4. Procédé selon l'une quelconque des revendications 1 à 3, comportant en outre l'étape consistant à déterminer si l'artefact comprend un signal d'amplitude significativement faible (800) et/ou une activité électrique atypique (900).
     
    5. Procédé selon la revendication 4, consistant en outre à déterminer si l'artefact comporte un mouvement de câble ou un mouvement d'électrode (500) sur la base de l'identification d'une amplitude de signal supérieure à un seuil prédéfini, et si un segment comporte une amplitude supérieure au seuil, à identifier ce segment comme comprenant un mouvement de câble ou un artefact de mouvement d'électrode.
     
    6. Procédé selon la revendication 5, dans lequel le fait de déterminer si l'artefact comporte un mouvement de câble ou un mouvement d'électrode (500) sur la base de l'identification d'une amplitude de signal supérieure à un seuil prédéfini, et si un segment comporte une amplitude supérieure au seuil, identifier ce segment comme comprenant des artefacts de mouvement de câble ou d'électrode, dans lequel le seuil est compris entre 50 et 250 µV.
     
    7. Procédé selon l'une quelconque des revendications 1 à 6, comprenant en outre la détermination de savoir si le segment temporel du signal comporte un artefact comprenant :

    (i) un artefact de mouvement musculaire (700) par filtrage passe-bande d'un signal provenant d'au moins une électrode dans la plage de la bande EEG β1 pour produire le signal E1 et filtrage passe-bande du même signal dans la plage de la bande β2 pour produire le signal E2, et si l'énergie relative de E2 par rapport à E1 dépasse un seuil, identifier le segment comme contenant des artefacts de mouvement musculaire ; et/ou

    (ii) un signal d'amplitude significativement faible (800) par la détermination de savoir si l'énergie quadratique moyenne d'un segment est inférieure à un seuil, et si l'énergie quadratique moyenne est inférieure à un seuil, identifier le segment comme le segment comprenant un signal d'amplitude significativement faible.


     
    8. Procédé selon l'une quelconque des revendications 1 à 7, dans lequel le seuil prédéfini afin de déterminer si des segments temporels du signal comportant un artefact d'impulsion est compris entre 50 et 250 µV.
     
    9. Dispositif destiné à éditer automatiquement des signaux de données d'activité électrique cérébrale, comprenant :

    au moins deux électrodes (60) ;

    un circuit permettant de mesurer les signaux de potentiel électrique des électrodes ;

    une unité de mémoire (22) configurée pour stocker des données liées aux signaux de potentiel électrique ;

    une unité d'analyse (24) configurée pour :

    analyser les signaux afin de déterminer si une pluralité de segments temporels des signaux comportent un artefact, dans lequel :

    l'unité d'analyse est configurée pour déterminer si les segments temporels du signal comprennent un artefact dû aux mouvements latéraux des yeux (300) par le filtrage des signaux obtenus à partir d'électrodes positionnées aux positions F7 et F8, et pour déterminer si la différence entre les signaux filtrés obtenus à partir des électrodes positionnées aux positions F7 et F8 dépasse un seuil ; et pour

    identifier le segment comme comprenant l'artefact en fonction du seuil dépassé ; et pour

    éditer les données dans l'unité d'analyse afin d'éliminer le segment.


     
    10. Dispositif selon la revendication 9, dans lequel les au moins deux électrodes (60) sont configurées pour être positionnées aux positions F7 et F8 sur la base du système de placement international 10/20 élargi.
     
    11. Dispositif selon la revendication 9, dans lequel les électrodes comportent au moins cinq électrodes (60) conçues pour être positionnées aux positions FP1, FP2, F7, F8 et AFz sur la base du système de placement international 10/20 élargi.
     
    12. Dispositif selon l'une quelconque des revendications 9 à 11, configuré pour déterminer si l'artefact comporte un mouvement de câble ou un mouvement d'électrode (500) sur la base de l'identification d'une amplitude de signal supérieure à un seuil prédéfini, et si un segment comporte une amplitude supérieure au seuil, identifier ce segment comme comportant un mouvement de câble ou un artefact de mouvement d'électrode.
     
    13. Dispositif selon l'une quelconque des revendications 9 à 12, configuré pour déterminer si des segments temporels du signal comportent un artefact comprenant :

    (i) un artefact de mouvement musculaire (700) par filtrage passe-bande d'un signal provenant d'au moins une électrode dans la plage de la bande EEG β1 pour produire le signal E1 et filtrage passe-bande du même signal dans la plage de la bande β2 pour produire le signal E2, et si l'énergie relative de E2 par rapport à E1 dépasse un seuil, identifier le segment comme contenant l'artefact de mouvement musculaire ; et/ou

    (ii) un signal d'amplitude significativement faible (800) par la détermination de savoir si l'énergie quadratique moyenne d'un segment est inférieure à un seuil, et si l'énergie quadratique moyenne est inférieure à un seuil, identifier le segment comprenant le signal d'amplitude significativement faible.


     




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    Cited references

    REFERENCES CITED IN THE DESCRIPTION



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    Patent documents cited in the description




    Non-patent literature cited in the description