WO2008058343A1 - Procédé de détection de crises épileptiques par eeg chez un nouveau-né ou un jeune enfant - Google Patents

Procédé de détection de crises épileptiques par eeg chez un nouveau-né ou un jeune enfant Download PDF

Info

Publication number
WO2008058343A1
WO2008058343A1 PCT/AU2007/001765 AU2007001765W WO2008058343A1 WO 2008058343 A1 WO2008058343 A1 WO 2008058343A1 AU 2007001765 W AU2007001765 W AU 2007001765W WO 2008058343 A1 WO2008058343 A1 WO 2008058343A1
Authority
WO
WIPO (PCT)
Prior art keywords
seizure
eeg
time
frequency
features
Prior art date
Application number
PCT/AU2007/001765
Other languages
English (en)
Inventor
Mostefa Mesbah
Paul B. Colditz
Luke Rankine
Boualem Boashash
Original Assignee
The University Of Queensland
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from AU2006906381A external-priority patent/AU2006906381A0/en
Application filed by The University Of Queensland filed Critical The University Of Queensland
Publication of WO2008058343A1 publication Critical patent/WO2008058343A1/fr

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A61B5/372—Analysis of electroencephalograms
    • A61B5/374—Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
    • A61B5/4094—Diagnosing or monitoring seizure diseases, e.g. epilepsy
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/02—Preprocessing
    • G06F2218/04—Denoising

Definitions

  • the present invention relates to a method for detecting electroencephalogram (EEG) seizures in a newborn or a young child.
  • EEG electroencephalogram
  • EEG experts e.g. highly qualified pediatric neurologists manually review newborn EEGs for characteristics which are evidence of seizure activity. This review process can be quite arduous and is time intensive. The process is also subjective and some seizures may go undetected when reviewing EEGs.
  • the EEG recordings are typically of a relatively short duration (i.e. between 20-40 minutes) and are not suited for detecting the ongoing presence, frequency and duration of seizures.
  • a cerebral function monitor (CFM) system is one time-based technique which can be used for displaying the integrated amplitude of an EEG channel continuously over a very long time basis.
  • CFM systems have been proposed for the long term monitoring of newborn EEGs and allow for the determination of global neurological status. However, CFM systems must be manually monitored for seizures and do not allow for automated seizure detection.
  • EEG seizure detection techniques which involves estimating the frequency content of an EEG signal and providing a frequency domain representation, can be used to detect EEG seizures.
  • One such newborn EEG seizure detection technique developed by Gotman et al. [6] incorporates features from the frequency domain representation of 10 second EEG epochs.
  • the newborn EEG signal has been shown to be highly nonstationary, particularly during EEG seizure periods [7]. Therefore, the features obtainable using stationary signal processing techniques are likely to be suboptimal and lead to suboptimal automatic newborn EEG seizure detection methods.
  • a seizure detection monitor developed by "Brainz” involves long term EEG recording by virtue of a two channel recording configuration.
  • the Brainz monitor displays basic signal features such as CFM and spectral edge measurement. These features, while useful in identifying seizure manually, are not suitable for automatic detection.
  • a seizure detection system developed by "Stellate Harmonie E” can record in a full channel configuration. This system uses the frequency-based Gotman algorithm (discussed above) to detect newborn EEG seizures.
  • a method for detecting a seizure in a newborn or a young child including the steps of: transforming electroencephalogram (EEG) data of the newborn or young child to form a time-frequency representation; extracting features from the time-frequency representation using a component linking technique; and detecting the seizure using the extracted features.
  • EEG electroencephalogram
  • time-frequency signal analysis/processing techniques are highly suitable for the analysis and processing of non- stationary signal data, such as newborn EEG data.
  • Time-frequency signal processing techniques can effectively account for the non-stationary nature of the EEG signals by representing them in a joint time-frequency domain representation.
  • the step of transforming involves forming the time- frequency representation of the EEG data using either a linear or a quadratic transformation.
  • the step of transforming may involve the step of selecting the transformation.
  • the step of transforming may involve the further step of selecting suitable parameters of the transformation.
  • the step of extracting features may involve the steps of: detecting significant local peaks of the time-frequency representation; and linking the detected significant local peaks together to form linked time- frequency components.
  • the linked time-frequency components may represent an estimate of the instantaneous frequencies (IFs) of the multicomponent EEG signal
  • the step of. extracting features may further involve extracting a set of time- frequency features based on the estimated IFs.
  • the step of extracting features may further involve extracting a set of time- domain features and frequency-domain features to enhance a true positive rate (to account for different types of EEG seizures) and to reduce a false positive rate.
  • the step of detecting the seizure may involve the step of comparing the extracted features to a number of thresholds previously derived from the analysis of a plurality of EEG seizures.
  • the step of detecting the seizure may further involve combining the results of the threshold comparisons using logical operators to determine whether or not a seizure is present.
  • the duration of the detected seizure may be determined by performing a gap closing procedure.
  • the step of detecting may further include the step of setting a flag responsive to the compared extracted features satisfying predetermined seizure conditions.
  • a seizure may be detected upon the setting of a plurality of consecutive flags.
  • the method may further include the step of generating an alarm to signify the detection of the seizure.
  • the method further includes the step of removing artefacts from the EEG data.
  • the artefacts may be removed using a matching pursuit (MP) decomposition method.
  • the method further involves the step of segmenting the EEG data into a plurality of epochs.
  • the step of segmenting may involve using a sliding rectangular window of length 12.8 seconds with an overlap of 6.8 seconds between epochs.
  • the method may further include the step of re-sampling the EEG data to reduce the amount of data to be subsequently processed.
  • the method may further include the step of filtering the EEG data.
  • the method may further include the step of sampling EEG signals from the newborn or young child using a plurality of channels to form the EEG data. In one embodiment, two channels are used.
  • a medium such as a magnetic or optical disk or solid state memory, containing machine readable instructions for execution by one or more processors to thereby perform the method.
  • a computational device which is loaded with computer readable instructions to perform the method.
  • a seizure detection apparatus including: one or more connectors for connecting to electroencephalogram (EEG) measurement electrodes; and one or more processors in communication with said connectors, and for receiving EEG signals from the connectors and storing related EEG data in a storage means, the processors being configured to: transform the stored EEG data of a newborn or young child to form a time-frequency representation; extract features from the time-frequency representation using a component linking technique; and detect a seizure using the extracted features.
  • EEG electroencephalogram
  • the processors may be in communication with an electronic memory, the electronic memory storing computer readable instructions to: transform the stored EEG data of a newborn or young child to form a time-frequency representation; extract features from the time-frequency representation using a component linking technique; and detect a seizure using the extracted features
  • the storage means may be an electronic memory or a disk.
  • Figure 1 is a schematic view of a hardware system for performing a method for detecting a seizure in a newborn or young child in accordance with an embodiment of the present invention.
  • FIG. 2 is a block diagram of a software product in accordance with the embodiment.
  • Figure 3 is a flowchart showing the steps performed by a preprocessing/signal conditioning submodule of the software product of Figure 2.
  • Figure 4 is a graph showing raw EEG data and processed EEG data with artefacts removed by the preprocessing/signal conditioning submodule of Figure 3.
  • Figure 5 is a data flow diagram relating to a time-frequency representation submodule of the software product of Figure 2.
  • Figure 6 is a flowchart showing the steps performed by a time-frequency feature extraction submodule of the software product of Figure 2.
  • FIG 7 is a flowchart showing the steps performed by an EEG state identification submodule of the software product of Figure 2.
  • Figure 8 is a control flow diagram of an output/user interface submodule of the software product of Figure 2.
  • a method for detecting an electroencephalogram (EEG) seizure in a newborn or young child using time-frequency signal processing techniques is performed using the hardware system 2 (i.e. seizure detection apparatus) shown in Figure 1.
  • EEG electroencephalogram
  • the system 2 includes two or more electrodes 4 for attachment to the scalp 3 of the newborn for measuring two or more channel EEG signals.
  • the electrodes 4 sense EEG signals 6 and are releasably attached to an amplifier 8 via electrical connectors 9.
  • An optical protection circuit may be provided between the electrodes 4 and amplifier 8 for electrically isolating the newborn from the amplifier 8.
  • the amplifier 8 serves to amplify the measured EEG signals 6 which are subsequently passed though an anti-aliasing (low pass) filter 10 to remove any high frequency noise.
  • the filtered analog EEG signals are coupled to an analog to digital converter (ADC) 11 and the resulting raw digital EEG data is subsequently transferred to and recorded on a storage medium 15 via disk drive 13.
  • the processor 12 e.g.
  • the personal computer is interfaced to a monitor display 14 for displaying information and user inputs 16 including a mouse and a keyboard for receiving user input.
  • the processor 12 loads software product 17 into memory 7, and executes software product 17 to perform the method for detecting an EEG seizure as described in detail below.
  • the software product 17 may be provided as a set of computer readable instructions on an optical disk 15 or other like storage media.
  • the software product 17 includes instructions for processor 12 to record measured raw digital EEG data.
  • the software product 17 further includes instructions for processor 12 to process the recorded EEG data to determine whether an EEG seizure condition exists.
  • the software product 17 includes an input data module 18, a seizure detection module 20 and an output/user interface module 22.
  • the input data module 18 retrieves and passes the recorded EEG data 64 from the storage medium 12 to the seizure detection module 20.
  • the retrieved data 64 is also provided to the output/user interface module 22.
  • the seizure detection module 20 includes four submodules: a preprocessing/signal conditioning submodule 24, a time-frequency representation submodule 25, a feature extraction submodule 26 and an EEG state identification submodule 28. These submodules 24, 25, 26, 28 are described in detail below.
  • Preprocessing/Signal conditioning submodule 24 is described in detail below.
  • the preprocessing/signal conditioning submodule 24 receives the recorded EEG data 64 and performs a variety of signal alterations (i.e. conditioning) to enhance the desired signal characteristics and remove any unwanted signal components.
  • the flowchart of Figure 3 shows the preprocessing method 30 performed by the preprocessing/signal conditioning submodule 24.
  • the digitally recorded raw EEG data 64 is received by the preprocessing/signal conditioning submodule 24.
  • the EEG data is resampled to FS d to reduce the size of the data for further processing. In this manner, redundant data is removed and, accordingly, further processing times are reduced.
  • the software segments the resampled EEG data 41 into manageable EEG epochs.
  • EEG electrocardiogram
  • EOG electrooculogram
  • EMG electromyogram
  • Interference from these signals on the EEG data can have a masking effect, hiding seizure patterns that lead to missed EEG seizure detections [8].
  • ECG electrocardiogram
  • EOG electrooculogram
  • EMG electromyogram
  • the artefact removal process involves using a time-frequency signal processing technique to adaptively remove energy caused by short-time and high amplitude artefacts from the EEG data.
  • the artefact removal technique is based on a matching pursuit method [9]. This method can be effectively used to obtain a time-frequency representation/parameterization of non-stationary signals.
  • the matching pursuit method is an iterative process which decomposes the signal into a weighted sum of atoms selected from a predefined dictionary [9]. At each iteration, the matching pursuit method selects the atom that best represents the signal residue in an inner product sense.
  • a signal, x is represented as:
  • a signal, x can also be approximated, using the matching pursuit decomposition, with m atoms, as
  • the time-frequency dictionary used in the artefact removal procedure is a combination of linear frequency modulated (LFM) atoms and Gabor atoms, similar to that which was proposed in [10].
  • the Gabor atom is expressed as
  • ⁇ (t) is a Gaussian function
  • the indexing vector includes the initial frequency, ⁇ , , the rate of change in frequency, ⁇ r , and phase, ⁇ (i.e.
  • the fundamental idea behind the artefact removal technique is that short- time and high amplitude artefacts will be well represented by Gabor atoms with small scales in the first couple of iterations of the matching pursuit decomposition. Therefore, if these atoms are removed from the signal representation, an artefact reduced signal will be obtained.
  • the inclusion of the LFM atoms is to prevent atoms representing EEG seizure from being removed by the artefact removal method as detailed below.
  • This artefact removal method uses the matching pursuit decomposition to iteratively determine whether or not an artefact atom has been selected. If the atom selected is not an artefact atom, the procedure is exited. If, however, an artefact atom is selected, the atom is removed from the signal and the procedure is repeated on the residual signal.
  • Table I shows the atom scale (width) and associated percentage of signal energy which corresponds to atoms representing short-time and high amplitude artefacts. These values were selected based on the analysis of newborn EEG data corrupted by artefacts. The scale values and percentage of signal energy were based on a sampling frequency of
  • the preprocessed EEG data 41 is stored.
  • An example of the proposed artefact removal technique applied to an EEG epoch which is corrupted by a short-time and high amplitude artefact is shown in Figure 4.
  • the dashed line shows an original EEG data 64 and the continuous line shows the preprocessed EEG data 41.
  • the time-frequency representation submodule 25 receives the preprocessed EEG 41 which, in turn, is transformed to a time- frequency representation 51.
  • the time-frequency representation 51 can be obtained through using linear or quadratic time-frequency transformations in the time-frequency representation submodule 25.
  • the time-frequency representation 51 is preferably a reduced interference quadratic time- frequency distribution (QTFD).
  • the time-lag kernel G(t, ⁇ ) defines the particular characteristics of a QTFD. Therefore, decision on G(t, ⁇ ) , and hence on which QTFD to use, must be made by a user using the QTFD decision submodule 42, as indicated in the time-frequency representation submodule 25 in Figure 5.
  • a subclass of QTFDs which are suitable for real life applications such as newborn EEG seizure detection, are the reduced interference distributions
  • time-lag kernel of RIDs generally incorporates a smoothing parameter which can be adjusted to provide optimal time-frequency representation.
  • h ⁇ the length of the lag window
  • time-lag kernel G(t, ⁇ ) is chosen to be
  • a time-frequency representation 51 of the segmented newborn EEG data is then obtained at the output of this time-frequency representation submodule 25.
  • Newborn EEG seizure can be characterized by dominant and distinct frequency modulated components in the time-frequency domain [12, 13].
  • the newborn EEG background lacks any dominant frequency modulated components.
  • features which indicate the presence of clear, dominant and distinct frequency modulated components are extracted from the time-frequency representation 51 in the feature extraction submodule 26.
  • the feature extraction submodule 26 receives the time-frequency representation 51 which is subsequently processed using image processing techniques which allow for feature extraction according to the method 52 shown in Figure 6.
  • step 54 the significant local peaks of the time-frequency representation 51 are detected.
  • the significance of the peaks is determined by a predefined threshold parameter 53.
  • the detected significant local peaks are then linked together at step 56 using a component linking procedure.
  • This procedure links the detected significant peaks to form an estimate of the instantaneous frequency of a frequency modulated component.
  • a frequency modulated component is identified if significant peaks in the time-frequency representation 51 are continuously linked for a predefined period of time in accordance with a predetermined time threshold 55.
  • a set of time-frequency features are extracted.
  • the following is a list of output extracted features 46 from the input time- frequency representation 51:
  • TF-Comp is a feature taking the value of 0 or 1, indicating whether a DOMINANT time-frequency component exists in the newborn EEG time- frequency representation 51. This feature is extracted using the local peak detection step 54 with a threshold 53 of.
  • the component linking step 56 with a time threshold 55 of 9.5 seconds. If a linked component is found, the feature is assigned the value 1. Otherwise the feature is assigned the value 0.
  • Min-IF-Freq is the minimum instantaneous frequency of the TF- Comp.
  • Mean-Freq-Fund is the mean frequency associated with TF-Comp.
  • Num-Close-Peaks is the number of significant local peaks which are within a predefined number of pixels (typically 10) of the TF-Comp.
  • a component must have a predefined minimum duration (e.g., 9.5 seconds) to be considered as a true component.
  • This feature is a three parameter feature consisting of 1) Intersection of component time supports, 2) mean ratio between component IFs and 3) standard deviation of ratio between component IFs.
  • the following is a list of features extracted from the time domain and frequency domain introduced to account for more types of EEG seizures and to help reduce the false positive rate.
  • Time domain features the following features are extracted directly from the EEG epoch.
  • Max-Amp is the maximum of the absolute value of the voltages in each EEG epoch.
  • Epoch-Energy is the total energy of the EEG epoch.
  • TP-Ratio is the ratio between the number of turning points in an EEG epoch and Feat 3. Mean-Freq-Fund.
  • Frequency domain features the following features are extracted from the power spectrum of the EEG epoch.
  • the power spectrum is typically estimated using Burg's method with autoregressive prediction model order of 10.
  • H-Spec is the spectral entropy of the EEG epoch. It is calculated as the Shannon entropy using the normalized spectrum as a probability density function [14].
  • the EEG state identification submodule 28 receives the EEG features 46 extracted from the newborn EEG epoch. The extracted features 46 are then evaluated in accordance with the method 90 of Figure 7. A detailed description of the EEG state identification method 90 is provided below.
  • the extracted features 46 are compared with predefined seizure threshold values 91 and conditions.
  • These seizure thresholds 91 for the extracted EEG state identifying features 46, that indicate whether or not a seizure is occurring, are determined based on an analysis of a large database of newborn EEGs containing seizures, with the seizure events being identified by a neurologist.
  • a seizure event is marked at step 94. Otherwise, a non-seizure event is marked at step 96.
  • the first step in determining whether there is a seizure is to determine whether the EEG epoch has sufficient energy or the maximum voltage is excessively large (i.e. typically, if threshold values 91 Epoch-Energy ⁇ 5 ⁇ lO ⁇ 8 or Max-Amp > 300). If either of these criteria is met, a nonseizure event is recorded as the data is not suitable to make the determination. Otherwise, the epoch has met the criteria to be assessed for seizure characteristics based on the five seizure subtypes defined below. The five seizure subtypes are labeled as
  • CMCS Continual Multiple Components Seizure
  • the following pseudocode provides typical criteria for the five seizure subtypes to be registered as either seizure at step 94 or nonseizure at step 96 for a given EEG epoch.
  • CMCS & CMCS(next epoch) & (CMCS(next-next epoch) 0
  • a seizure event is flagged in a vector of binary flags 60.
  • a seizure is indicated by assigning the flag vector 60 a value "1". Otherwise the flag vector is assigned a value "0" to indicate that no seizure event was detected for that epoch of time.
  • the flag vector 60 indicating the decision on the state of the EEG (e.g. seizure or nonseizure) is then sent to the Output/User interface module 22.
  • the output/user interface module 22 receives the flag vector 60 from the EEG state identification submodule 28 and the digitally recorded raw EEG data 64 from the input data module 18.
  • the raw EEG data 64 and associated flag data 66 are displayed on the monitor display 14 in real-time.
  • the raw EEG data 64 and flag data 66 are stored on a storage medium of personal computer 12 for future review at a later time if necessary.
  • the output/user interface module 22 determines in real-time whether a seizure state exists based upon the flag data 66. An audible alarm is triggered when the flag data 66 indicates a seizure positive state at box 74 for three or more successive epochs. Otherwise, the audible alarm is off (i.e. not triggered) as the flag data 66 indicates a seizure negative state.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Public Health (AREA)
  • Molecular Biology (AREA)
  • Neurology (AREA)
  • Veterinary Medicine (AREA)
  • Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • Pathology (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Medical Informatics (AREA)
  • Animal Behavior & Ethology (AREA)
  • Surgery (AREA)
  • Signal Processing (AREA)
  • Psychiatry (AREA)
  • Physiology (AREA)
  • Neurosurgery (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Artificial Intelligence (AREA)
  • Psychology (AREA)
  • Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)

Abstract

La présente invention concerne un procédé de détection d'une crise épileptique chez un nouveau-né ou un jeune enfant, comprenant l'étape consistant à transformer des données d'électroencéphalogramme (EEG) du nouveau-né ou du jeune enfant pour former une représentation temps fréquence. Des caractéristiques sont extraites de ladite représentation à l'aide d'une technique de liaison de composantes. L'extraction de caractéristiques peut impliquer la détection de pics locaux significatifs de ladite représentation, et la liaison desdits pics détectés ensemble pour former des composantes temps fréquence liées. Le procédé comprend en outre l'étape consistant à détecter la crise épileptique à l'aide des caractéristiques extraites.
PCT/AU2007/001765 2006-11-15 2007-11-15 Procédé de détection de crises épileptiques par eeg chez un nouveau-né ou un jeune enfant WO2008058343A1 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
AU2006906381 2006-11-15
AU2006906381A AU2006906381A0 (en) 2006-11-15 A method for detecting EEG seizures in a newborn or young child
AU2007903032 2007-06-05
AU2007903032A AU2007903032A0 (en) 2007-06-05 A method for detecting EEG seizures in a newborn or young child

Publications (1)

Publication Number Publication Date
WO2008058343A1 true WO2008058343A1 (fr) 2008-05-22

Family

ID=39401257

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/AU2007/001765 WO2008058343A1 (fr) 2006-11-15 2007-11-15 Procédé de détection de crises épileptiques par eeg chez un nouveau-né ou un jeune enfant

Country Status (1)

Country Link
WO (1) WO2008058343A1 (fr)

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2010115939A2 (fr) 2009-04-07 2010-10-14 National University Of Ireland, Cork Méthode d'identification d'attaques en temps réel dans un signal d'électroencéphalogramme (eeg)
CN102824172A (zh) * 2012-08-28 2012-12-19 浙江大学 一种脑电特征提取方法
EP2571289A3 (fr) * 2008-12-22 2013-05-22 Oticon A/s Système de prothèse auditive comprenant les électrodes EEG
EP2782498A4 (fr) * 2011-11-25 2015-07-29 Persyst Dev Corp Méthode et système d'affichage de données eeg et interface utilisateur
WO2020049430A1 (fr) 2018-09-04 2020-03-12 Johnson & Johnson Consumer Inc. Appareil et procédé pour évaluer l'émotion des nourrissons et des jeunes enfants
CN111938631A (zh) * 2020-08-07 2020-11-17 南京伟思医疗科技股份有限公司 一种基于新生儿脑电信号的惊厥检测方法
CN112075932A (zh) * 2020-10-15 2020-12-15 中国医学科学院生物医学工程研究所 一种诱发电位信号的高分辨率时频分析方法
EP3675724A4 (fr) * 2017-08-29 2021-04-14 Kaoskey Pty Limited Système, procédé, programme informatique et interface informatique pour l'analyse de l'information électroencéphalographique
WO2023084509A1 (fr) * 2021-11-14 2023-05-19 Rambam Med-Tech Ltd. Système et procédé de détermination d'un type de dysfonctionnement cérébral pendant des procédures médicales

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5815413A (en) * 1997-05-08 1998-09-29 Lockheed Martin Energy Research Corporation Integrated method for chaotic time series analysis
WO2001067288A2 (fr) * 2000-03-07 2001-09-13 Ut-Battelle, Llc Evaluation d'etat de processus non lineaires
US20020095099A1 (en) * 2000-04-27 2002-07-18 Centre National De La Recherche Scientificque Method and device for the medical monitoring in real time of a patient from the analysis of electro-encephalograms, application of this method to characterize and to differentiate between physiological or pathologial conditions, and a method for anticipating epileptic seizures in real time
US20030195429A1 (en) * 2002-04-15 2003-10-16 Wilson Scott B. Method and system for detecting seizures using electroencephalograms
US20030199781A1 (en) * 2002-04-22 2003-10-23 Fuji Xerox Co., Ltd. Automatic electroencephalogram analysis apparatus and method
WO2004100765A2 (fr) * 2003-05-06 2004-11-25 Aspect Medical Systems, Inc. Systeme et procede d'evaluation de l'efficacite du traitement de troubles neurologiques faisant appel a l'electroencephalogramme
WO2005018448A1 (fr) * 2003-08-22 2005-03-03 Brainz Instruments Limited Analyse de capture d'eeg

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5815413A (en) * 1997-05-08 1998-09-29 Lockheed Martin Energy Research Corporation Integrated method for chaotic time series analysis
WO2001067288A2 (fr) * 2000-03-07 2001-09-13 Ut-Battelle, Llc Evaluation d'etat de processus non lineaires
US20020095099A1 (en) * 2000-04-27 2002-07-18 Centre National De La Recherche Scientificque Method and device for the medical monitoring in real time of a patient from the analysis of electro-encephalograms, application of this method to characterize and to differentiate between physiological or pathologial conditions, and a method for anticipating epileptic seizures in real time
US20030195429A1 (en) * 2002-04-15 2003-10-16 Wilson Scott B. Method and system for detecting seizures using electroencephalograms
US20030199781A1 (en) * 2002-04-22 2003-10-23 Fuji Xerox Co., Ltd. Automatic electroencephalogram analysis apparatus and method
WO2004100765A2 (fr) * 2003-05-06 2004-11-25 Aspect Medical Systems, Inc. Systeme et procede d'evaluation de l'efficacite du traitement de troubles neurologiques faisant appel a l'electroencephalogramme
WO2005018448A1 (fr) * 2003-08-22 2005-03-03 Brainz Instruments Limited Analyse de capture d'eeg

Non-Patent Citations (7)

* Cited by examiner, † Cited by third party
Title
BOASHASH ET AL.: "Detection of seizures in newborns using time-frequency analyis of EEG signals", IEEE STATISTICAL SIGNAL AND ARRAY PROCESSING, 2000. 10TH IEEE WORKSHOP. IEEE, 2000, pages 564 - 568, XP010512228 *
BOASHASH ET AL.: "Newborn EEG seizure pattern characterisation using Time-Frequency analysis", IEEE ACOUSTICS, SPEECH AND SIGNAL PROCESSING, 2001. IEEE INTERNATIONAL CONFERENCE, vol. 2, 2001, pages 1041 - 1044, XP010803686, DOI: doi:10.1109/ICASSP.2001.941097 *
FLANAGAN ET AL.: "Automatic seizure detection in the newborn: methods and initial evaluation", ELSEVIER ELECTROENCEPHALOGRAPHY AND CLINICAL NEUROPHYSIOLOGY, 1997, vol. 103, no. 3, September 1997 (1997-09-01), pages 356 - 362, XP002502776, DOI: doi:10.1016/S0013-4694(97)00003-9 *
FRANASZCZUK ET AL.: "Time-Frequency analysis using the matching pursuit algorithm applied to seizures orginating from the mesial temporal lobe", ELSEVIER ELECTROENCEPHALOGRAPHY AND CLINICAL NEUROPHYSIOLOGY, vol. 106, no. 6, June 1998 (1998-06-01), pages 513 - 521, XP027283774 *
GOTMAN ET AL.: "Evaluation of an automatic seizure detection method for the newborn EEG", ELSEVIER ELECTROENCEPHALOGRAPHY AND CLINICAL NEUROPHYSIOLOGY, 1997, vol. 103, no. 3, September 1997 (1997-09-01), pages 363 - 369 *
HUSSAIN ET AL.: "Multi-component IF estimation: a statistical comparison in the quadratic class of time-frequency distributions", IEEE CIRCUITS AND SYSTEMS, 2001. IEEE INTERNATIONAL SYMPOSIUM. IEEE, vol. 2, 2001, pages 109 - 112 *
MESBAH ET AL.: "A Time-Frequency approach for newborn seizure detection", IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE. IEEE, vol. 20, no. 5, September 2001 (2001-09-01) - October 2001 (2001-10-01), pages 54 - 64, XP002567213, DOI: doi:10.1109/51.956820 *

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2571289A3 (fr) * 2008-12-22 2013-05-22 Oticon A/s Système de prothèse auditive comprenant les électrodes EEG
WO2010115939A2 (fr) 2009-04-07 2010-10-14 National University Of Ireland, Cork Méthode d'identification d'attaques en temps réel dans un signal d'électroencéphalogramme (eeg)
EP2782498A4 (fr) * 2011-11-25 2015-07-29 Persyst Dev Corp Méthode et système d'affichage de données eeg et interface utilisateur
CN102824172A (zh) * 2012-08-28 2012-12-19 浙江大学 一种脑电特征提取方法
EP3675724A4 (fr) * 2017-08-29 2021-04-14 Kaoskey Pty Limited Système, procédé, programme informatique et interface informatique pour l'analyse de l'information électroencéphalographique
WO2020049430A1 (fr) 2018-09-04 2020-03-12 Johnson & Johnson Consumer Inc. Appareil et procédé pour évaluer l'émotion des nourrissons et des jeunes enfants
CN111938631A (zh) * 2020-08-07 2020-11-17 南京伟思医疗科技股份有限公司 一种基于新生儿脑电信号的惊厥检测方法
CN111938631B (zh) * 2020-08-07 2023-01-31 南京伟思医疗科技股份有限公司 一种基于新生儿脑电信号的惊厥检测方法
CN112075932A (zh) * 2020-10-15 2020-12-15 中国医学科学院生物医学工程研究所 一种诱发电位信号的高分辨率时频分析方法
CN112075932B (zh) * 2020-10-15 2023-12-05 中国医学科学院生物医学工程研究所 一种诱发电位信号的高分辨率时频分析方法
WO2023084509A1 (fr) * 2021-11-14 2023-05-19 Rambam Med-Tech Ltd. Système et procédé de détermination d'un type de dysfonctionnement cérébral pendant des procédures médicales

Similar Documents

Publication Publication Date Title
Gupta et al. A novel signal modeling approach for classification of seizure and seizure-free EEG signals
Fan et al. Detecting abnormal pattern of epileptic seizures via temporal synchronization of EEG signals
WO2008058343A1 (fr) Procédé de détection de crises épileptiques par eeg chez un nouveau-né ou un jeune enfant
Riaz et al. EMD-based temporal and spectral features for the classification of EEG signals using supervised learning
Abásolo et al. Analysis of regularity in the EEG background activity of Alzheimer's disease patients with Approximate Entropy
EP1820444B1 (fr) Détection d'activité épileptiforme
Boubchir et al. A review of feature extraction for EEG epileptic seizure detection and classification
Yadav et al. Model-based seizure detection for intracranial EEG recordings
US20160106331A1 (en) Fractal index analysis of human electroencephalogram signals
Celka et al. Nonlinear nonstationary Wiener model of infant EEG seizures
Jakaite et al. Feature extraction from electroencephalograms for Bayesian assessment of newborn brain maturity
JP2022536552A (ja) 脳波（ｅｅｇ）の非線形性の変化に基づく発作検出システム及び方法
JP2023018131A (ja) 電気生理学的誘発電位における変化を検出する医用システム及び方法
Subasi et al. Automatic recognition of vigilance state by using a wavelet-based artificial neural network
Wijayanto et al. Comparison of empirical mode decomposition and coarse-grained procedure for detecting pre-ictal and ictal condition in electroencephalography signal
JP2023099043A (ja) 脳波（ｅｅｇ）の非線形性の変化に基づく発作検出システム及び方法
Maddirala et al. ICA with CWT and k-means for eye-blink artifact removal from fewer channel EEG
Lee et al. Automated epileptic seizure waveform detection method based on the feature of the mean slope of wavelet coefficient counts using a hidden Markov model and EEG signals
Eltrass et al. A new automated multi-stage system of non-local means and multi-kernel adaptive filtering techniques for EEG noise and artifacts suppression
Thomas et al. Heart sound segmentation using fractal decomposition
Orhan et al. Epilepsy diagnosis using probability density functions of EEG signals
Hadiyoso et al. Noise removal in mild cognitive impairment eeg recording using empirical mode decomposition
Azami et al. MEMD-enhanced multivariate fuzzy entropy for the evaluation of complexity in biomedical signals
Wijayanto et al. A study on signal complexity measurement for epileptic seizure detection
Subasi Application of classical and model-based spectral methods to describe the state of alertness in EEG

Legal Events

Date Code Title Description
DPE2 Request for preliminary examination filed before expiration of 19th month from priority date (pct application filed from 20040101)
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 07815568

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 07815568

Country of ref document: EP

Kind code of ref document: A1