0_a_introduction into eeg processing

8/16/2019 0_a_Introduction Into EEG Processing

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BIOLOGICALBIOLOGICAL SIGNALSSIGNALS

PROCESSINGPROCESSINGIntroductionIntroduction

Assoc. Prof. Assoc. Prof. Vladim Vladim í írr KrajKrajččaa, MSc., PhD., MSc., PhD.

Department of neurology Department of neurology Faculty Hospital Na Bulovce,Faculty Hospital Na Bulovce,

Prague, Czech RepublicPrague, Czech Republic

phonephone +420+420--22--66086608 23072307

ee--mailmail krajcakrajca v v @@fnbfnb.cz.cz

mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


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Source materials on webSource materials on web www.skolicka.fbmi.cvut.cz www.skolicka.fbmi.cvut.cz

password: signaly password: signalyContents of web pages:

e-learning courses (2009, 10 lectures, in Czech)

selected publications - international journals and conferences

video - EEG, EMG, EP signals recording in EEG lab FN Bulovka andFN Ostrava

requirements and terms of exams (enrolment in KOS)

e-learning course (in Czech - pictures, formulas)

Mohylová J., Krajča V. Zpracování signál ů v lékař ství , Ostrava 2007 animated examples and simulations (some in DOS),

programs for simulation of filtering, brain mapping, cluster analysis…

homeworks

http://www.skolicka.fbmi.cvut.cz/http://www.skolicka.fbmi.cvut.cz/http://www.skolicka.fbmi.cvut.cz/


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MetMethods of computerhods of computer--assistedassisted

electroencephalogramelectroencephalogram analysis realizedanalysis realizedinin EEGEEG lablab FN Na BulovceFN Na Bulovce

Overview Overview

Examples of real world EEG biosignal processing.Case studies from clinical practice and research.


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Outline of lectureOutline of lecture

1. Introduction – what it is electroencephalogram, properties ofthe signal

2. Problems of visual evaluation

3. Selection of proper methods of analysis

4. Practical assets of methods5. Publications overview, courses , source materials


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1.1. What is electroencephalogramWhat is electroencephalogram (EEG) ?(EEG) ?

• complex elektrical biosignal, measurement on the scull by

electrodes (µV)• reflects the electrical activity of brain functions

• apart from structural methods (CT - computer tomography), itshows dynamic functional manifestation of living brain

• basic diagnostic tool for the treatment and diagnosis of epilepsy

and sleep analysis• manifests behavioural states (psychology), consciousnes,

disturbances of brain functions, sleep stages, coma, lesions


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1.a.1.a. Basic spectral activityBasic spectral activity -- waves, waves, graphoelementsgraphoelements

• Principal frequency content for diagnosis (backgroundactivity)

• Delta 0.5 - 4 Hz - children, deep sleep, tumours• Theta 4 - 8 Hz - normal• Alpha 8 - 13 Hz - awake, eyes closed, blocked by eyes opened• Beta 13 - 22 Hz - normal, attention, pain, unrest

• Graphoelements• epileptic abnormalities - transients - spikes, spike and wave complexes(SWC)

• spike - transient clearly distinctive from the background activity,

pointed peak, 70 msec wide (sharp wave 200msec)

• Artefacts• Defects in recordings technical (50/60 Hz noise, drop off of electrode)

and physiologic (sweating, movement) caused by external influence.Must be displaceed from recording (detection, ICA, PCA)


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1.b.1.b. ExamplesExamples

Amplitude artefactEpileptic spikes


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2.2. Problems of Problems of visual evaluation visual evaluation -- why why useusecomputercomputerss

1. Visual evaluation / interpretation

• subjective

• „more art than science“ - evaluation depends on the teacher and hisexperience

• education of top specialists is difficult ( 5 years of maturation)

• tedious, requires permanent attention

2. Length of recording

• ambulatory recording - 20 min -> epileptic activity doesn't have to manifestitself

• long term monitoring is necessary (24 hours and more)

• sleep analysis - 8 hours = 864 m of paper recording3. Archivation - (paper machines obsolete); DVD is compact, databases provide

fast information retrieval

4. Data processing (montage changing, filtering, not possible on papertraces )

5. Numeric EEG quantification and analysis (qEEG, digital signal processing)


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2.a.2.a. Aims of computer Aims of computer--assisted analysisassisted analysis

Support of physician's decision Extension of his capabilities by objective data Graphic data presentation Normal/abnormal activity distinction Signal classification Trends evaluation Data reduction and archiving Quantification - assesment of exact value of

dominant frequency Hidden information extraction (not visible by a

naked eye) Concentration on interesting parts of long-term

recordings an skipping the uninterested parts (byartificial intelligence)


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3.3. LongLong--termterm xx detaildetailed analysised analysis

• Long-term EEG• significant events

detection• significant

graphoelements (spikes)

searching• trends evaluation

• sleep stages classifiation

• epileptic seizuresdetection

• Detailed analysis• topographic mapping of

frequenct bands power• local and interhemispheric

coherence mapping

• frequency shifts in spectrum• source derivation

• dipole analysis

• phase specrum- epilepticfocus localization


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4.4. Methods overview Methods overview

PRE -PROCESSING

DIGITAL FILTERING

TRENDS REMOVING

LONG- TERM

PROCESSINGDETAILEDANALYSIS

SPECTRUM

ANALYSIS

LOCALCOHERENCE

WAVEFINDER

SPIKE

DETECTIONCSA

BRAIN

MAPPING

CORDANCE

PRINCIPALCOMPONENT

ANALYSIS

INDEPENDENTCOMPONENT

ANALYSIS

NEURALNETWORKS

PHASEMAPPING

PCASEGMENT

ATION

ARTEFACTELIMINATION

POWER

SPECTRUMPhoticdriving

3D PROJECTION3D PROJECTION

LORETALORETA

NEONATALSLEEP

ANALYSIS

NEONATALSLEEP

ANALYSIS

EEG SIGNAL


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CooperationCooperation

withwith SZSZÚÚ

4.1.4.1. CooperationCooperationCooperationCooperation withwith PsychiatricPsychiatric

CenterCenter

Cooperation withCooperation with

AcademyAcademy of of ScienceScience

Cooperation withCooperation withTechnicalTechnical

University OstravaUniversity Ostrava

PRE -PROCESSING

DIGITAL FILTERING

TRENDS REMOVING

LONG- TERM

PROCESSINGDETAILEDANALYSIS

SPECTRUM

ANALYSIS

LOCALCOHERENCE

WAVEFINDER

SPIKE

DETECTIONCSA

BRAIN

MAPPING

CORDANCE

PRINCIPALCOMPONENT

ANALYSIS

INDEPENDENTCOMPONENT

ANALYSIS

NEURALNETWORKS

PHASEMAPPING

PCASEGMENT

ATION

ARTEFACTELIMINATION

POWER

SPECTRUMPhoticdriving

CooperationCooperation

withwith ČČ

VUTVUTaandnd ÚÚPMDPMD

3D PROJECTION3D PROJECTION

LORETALORETA

NEONATALSLEEP

ANALYSIS

NEONATALSLEEP

ANALYSIS

EEG SIGNAL


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Signal ProcessingSignal Processing -- visualisation visualisation

A li d hi iA lit d t hi i


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13.5

-98.3

5.6

85.0

-100

+100

13.5 -98.3

85.0

5.6

1. iteration 2. iteration Topographic map

Conversion of numbers

into color scale

Average from four

neigbours

The new points are

included into computing… . . . till the whole area is

covered

V

V

a b

c

Amplitude topographic mapping Amplitude topographic mapping


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13.5 -98.3

85.0

5.6

13.5

-

98.3

5.6

85.0

mV

a)

2. iteration

… new computed valuesenter next iteration

Numbers are replaced by colors-100

100

µ Vb)

Topographic map

. . . final map

1. iteration

average of 4 neighbours

c)

Mapping of amplitudeMapping of amplitude1. In multichannel signal choose a time

instant – Fig. a)

2. Numerical values are replaced bycolors with from a color scale – Fig. b)

3. In several iterations do interpolationfrom neigbouring points – Fig. c)

4. Repeat (refine) interpolation byincluding the new values to cover all

plane5. The values of EEG amplitude are

color coded into topographic map.

Example in DOS


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amplitude brain mapping - between cursors


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4.3.1.4.3.1. Signal processingSignal processing -- unprocessed signalunprocessed signal


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4.3.1.4.3.1. FIR filteringFIR filtering


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4.3.3.4.3.3. Spectral componentsSpectral components

Signal + noise 50 Hz

Filtering 0.5-25 Hz


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5.5. Spectrum anlysisSpectrum anlysis

Assesment of frequency components in a single / all Assesment of frequency components in a single / all

channelschannels

Four main EEG frequency bands / single spectralFour main EEG frequency bands / single spectralline (power spectrum density)line (power spectrum density)

S l i EEG 1 h l


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Spectrum analysisSpectrum analysis – – EEG 1EEG 1 channelchannel


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S l iS l i ff


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Spectrum analysisSpectrum analysis -- frequency curvesfrequency curves

Spectrum analysisSpectrum analysis -- detailed maps fordetailed maps for


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Spectrum analysisSpectrum analysis -- detailed maps fordetailed maps forevery frequency lineevery frequency line

S l iS t l i f b df b d


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Spectrum analysisSpectrum analysis -- frequency bandsfrequency bands

S t l iS t l i 3D3D d ld l h i l lih i l li


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Spectrum analysis,Spectrum analysis, 3D3D modelmodel -- spherical splinesspherical splines


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6.6. CaseCase reportreport :: Local and interhemispheric coherenceLocal and interhemispheric coherence

Local coherence – indicator of cross correlation (cooperation) ofneighbouring areas of the brain cortex (prof. Rappelsberger)

Interhemispheric coherence – analysis of interhemispheric synchrony

Quantitative evaluation of focal lesion, not visible in native EEG


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Used also for topographic spectrum andUsed also for topographic spectrum and

coherence mappingcoherence mapping


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Local coherenceLocal coherence -- normalized cross spectrumnormalized cross spectrum

COHG

G Gxyxy

x y

=

2

magnitude squared coherence

[ ]G f E X f X f x ( ) ( ) ( )= ⋅

[ ]G f E X f Y f x y ( ) ( ) ( )= ⋅

auto-spectrum

Cross-spectrum

COHG

G Gxy

xy

x y

=

⋅

amplitude coherence

Examples (expert evaluation - without conclusive focal changes):


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Ischémie l.dx – vpravo (EEG závěr: …bez průkazných ložiskových změn)

Local coherence mapping - focus

temporally right in agreement with

CT

Classic spectrum

mapping - symmetrical


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Ictus (CMP)Ictus (CMP) -- right side hemiparesisright side hemiparesis


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7.7. LongLong--term analysisterm analysis CSACSA -- compressedcompressed

spectral arraysspectral arrays CSACSA – – compressedcompressed spectralspectral arraysarrays

Spectral curves fromSpectral curves from 2 sec2 sec segments in pseudosegments in pseudo

3D projection, like in theater3D projection, like in theater

Intensive Care UnitsIntensive Care Units -- spectral shifting, brainspectral shifting, brain

nmonitoring nmonitoring

77 CSACSA-- epileptic seizureepileptic seizure


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7.7. CSA CSA - epileptic seizureepileptic seizure

CSACSA h f ich n of i


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CSACSA -- change of view change of view

CaseCase study:study: PhoticPhotic drivingdriving in workers exposed to mercuryin workers exposed to mercuryVV i i h fi i h f U b SZU b SZÚÚ


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Vapers Vapers -- cooperation with prof.cooperation with prof. Urbanem, SZUrbanem, SZÚÚ

Photic driving – reaction of EEG to photostimulation fordifference freq of stroboscope (respons of harmonics in EEGcorresponding to stimulation).

Aim – asses CSA for indication of changes in PD for early

neurotoxical contamination of workers exposed to Mercuryvapors.

Significant changes in PD in comparison with the control groupearly before clinical manifestations

CSAsCSAs in ain a personperson withwith wellwell expressedexpressed photicphotic dridrivingving


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CSAsCSAs in ain a person person with with well well expressedexpressed photic photic dridri ving ving

Backgroundα activity

Driving on thefundamental

frequency

Driving on the

1st harmonic

frequency

Driving on the

2nd harmonic

frequency

CSAsCSAs in ain a personperson withwith absentabsent photicphotic drivingdriving


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CSAsCSAs in ain a person person with with absentabsent photic photic drivingdriving

Background

α activity

Automatically generated protocolAutomatically generated protocol


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Automatically generated protocol Automatically generated protocol

Quantitativeparams

8.8. LongLong--term signal analysisterm signal analysis -- significantsignificant


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graphoelements identification by WFgraphoelements identification by WF

WaveFinder: Hierarchic system of automatedEEG processing1. adaptive segmentation2. features extraction3. automatic classification4. visualization and quantification

System propertiesSystem properties


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y p py p p

LongLong--termterm (24(24 hours) signal processinghours) signal processing

Warning of the physician about some eventWarning of the physician about some event -- physician canphysician canmake his own conclusion bazsed on the original EEGmake his own conclusion bazsed on the original EEG

Mimetic techniqueMimetic technique -- physicianphysician´́s work immitations work immitation duringduring EEGEEGevaluationevaluation

Precision is not a goalPrecision is not a goal -- suspected eventssuspected events 1010 %% ,, 55%%artefacts/false warningsartefacts/false warnings -- time saving for 24 hours of EEGtime saving for 24 hours of EEG

User (physician) friendly, no parameter setting necessaryUser (physician) friendly, no parameter setting necessary

Graphic presentation of resultsGraphic presentation of results

TransparentTransparent -- switching to and from real original EEG signalswitching to and from real original EEG signal..

Adaptive segmentation based on two connected windows Adaptive segmentation based on two connected windows


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EEG – nonstationary signal, but

quasistationary (piece-wisestationary). Fixed segmentsdoes not respect signalproperties. Better way - divide itinto variable length segmentsdepending on the occurence of

EEG graphoelementsAdaptive segmentation(Bodenstein and Praetorius,Michel and Houchin, Krajča,Varri) :

1. Two connected windows aresliding along the signal

2. The deviation of stationarityis estimated from thedifference of parameters of

the two indows3. The segment boundary is

located at the local maximaof the difference measure

1 dvě spojená okna hranice segmentu

2 míra rozdílu oken 3 lokální maximum

mez

Two connectedwindows

Localmaximum

Differencemeasure

Segmentboundary

Threshold for smallfluctuations

A i i f iAd i i f i l h l


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Adaptive segmentation of a single channelAdaptive segmentation of a single channel

EEG signal and

segments boundary

Amplitude differencemeasure

Frequency differencemeasure

Total differencemeasureand the threshold

Multichannel adaptive segmentationMultichannel adaptive segmentation


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Color identification of graphoelements by cluster analysisColor identification of graphoelements by cluster analysis


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Features extractionFeatures extraction


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Classification should be relevant to the visualevaluation of the physician

Set of 10 features describing the time and frequency propertiesof the signal graphoelements:

variance of amplitude of the segment difference of maximal positive and minimal negative amplitude

power spectrum in delta, theta, alpha, beta1 a beta2 frequency bands

maximal value of the first derivative of the signal (proportional to the slope)

maximal value of the second derivative of the signal (proportional to the sharpness)

average value of the frequency of the signal

now - research of the relevant features (selection, evaluation)

Automated classification: supervised and unsupervised, Automated classification: supervised and unsupervised,l i l nd f zzclassical and fuzzy


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classical and fuzzyclassical and fuzzy MotivationMotivation -- to detect the hidden data structure, EEG graphoelementsto detect the hidden data structure, EEG graphoelements

identificationidentification

Methods of classification:Methods of classification: CLUSTER ANALYSISCLUSTER ANALYSIS

searching the natural data structure (if exists)searching the natural data structure (if exists) learning without a teacher (unsupervised)learning without a teacher (unsupervised) -- NO apriori informationNO apriori information

about the dataabout the data datadata in a cluster is more similar (close) than data in different clasin a cluster is more similar (close) than data in different classesses feature oriented classification methodfeature oriented classification method (statistical pattern recognition)(statistical pattern recognition) onon--lineline classification not possible (all segments enter the computing)classification not possible (all segments enter the computing)

LEARNING CLASSIFIERLEARNING CLASSIFIER

the new incomer is compared to etalon, prototype (identified bythe new incomer is compared to etalon, prototype (identified by aateacher during the learning phaseteacher during the learning phase

problems with object unknown in the etalons selection during leaproblems with object unknown in the etalons selection during learningrningphasephase

cluster analysis can be used in learning phase for etalonscluster analysis can be used in learning phase for etalonsidentificationidentification

Classical methods andClassical methods and Neural Networks Classic andClassic and Fuzzy sets theorysets theory

k k --meansmeans algorithm (MacQueen)algorithm (MacQueen)


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1. Cluster centers, prototypes are selectedrandomly (can be the data)

2. The (Euclidean) distances forom thecenters to all the data are computed

3. The object is classified to the clusterwith minimal distance to the center

4. The new centers ( of gravity) are re-

computed

5. If the optimum is not reached (classmembership does not change), go to thestep 2

Learning kLearning k NN classifierNN classifier


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Learning k Learning k --NN classifierNN classifier

LEARNING PHASE

Etalons, prototypes are determined with the help of the teacher

- which etalons belongs to the specific class

CLASS1 CLASS 2 CLASS3

CLASSIFICATION PHASE

1. The newcomer is compared with all prototypes

2. It is put into the closest class (most similar)

3. Next objects are evaluated

Hard and fuzzy sets (Lofti Zadeh 1970)Hard and fuzzy sets (Lofti Zadeh 1970)


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Hard and fuzzy sets (Lofti Zadeh, 1970)Hard and fuzzy sets (Lofti Zadeh, 1970)

Classic sets no class sharing in different classes object belongs/does not belong to the set (class) black and white view

Fuzzy sets multiple class membership with a different grade

X={ x1, x2,....xN }, set of objects.

Classic set | Fuzzy set _____________________________________|____________________________

characteristic function u A: X - > {0,1} | generalization u A: X -> |

1 , xk ∈ Ai |

u Ai(xk) = { | u Ai(xk)=uik fuzzy membership0 , xk ∉ Ai |

FuzzyFuzzy sets and classificationsets and classification


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FuzzyFu y sets and classificationsets and c ass cat on

Tř ída 1 Tř ída 2 Tř ída 3

0.70 0.30 0

1.0 0 0

0.20 0.25 0.55

0 1.0 0

0 0.80 0.20 smíšený objekt může patř it do r ůzných tř íd s r ůzným stupněmčlenství

JABLKA VIŠNĚ ŠVESTKY

proměnný stupeňčlenství v intervalu 0 -

FUZZY TŘÍDĚNÍ

70 % JABLKA

30 % VIŠNĚ

70 % JABLKA

?30 % VIŠNĚ

Tř ída 1 Tř ída 2 Tř ída 3

0 1 0

0 0 1

1 0 0

1 0 0

1 0 0 smíšený objekt musí

být zař azen do jedné z tř íd

JABLKA VIŠNĚ ŠVESTKY

0 ... patř í do tř ídy

1 ... nepatř í

KLASICKÉ (HARD) TŘÍDĚNÍ

OVOCNÉMOŠTY

Automated classification of EEG graphoelements Automated classification of EEG graphoelements


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MotivationMotivation -- disclose the hidden structure of data, EEGdisclose the hidden structure of data, EEGgraphoelements identification.graphoelements identification.

MethodMethod – – Cluster analysisCluster analysis

Example of classic clusteringExample of classic clustering


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Class 6 with mixed objects of class 5Class 6 with mixed objects of class 5Classic class number 5.

Fuzzy elimination of hybrid segments (grey color)Fuzzy elimination of hybrid segments (grey color)


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Fuzzy elimination of hybrid segments (grey color)y y d g (g y )

alpha cut 0.5

Summary sheetsSummary sheetsTypical, representative segments


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yp , p g

(closest to the center of gravity),

percentual occurence - multichannel

summary information

Temporal profile shows dynamic

class membership in the course of

time

Classes are ranged according to the

decreasing amplitudeColor - indicates the segment class

membership in original EEG recording

Color graphic EEG segments identificationColor graphic EEG segments identification


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Significant graphoelements are identified by a color in the original EEG record

Temporal profilesTemporal profiles -- dynamic changes in EEG, significant events detectiondynamic changes in EEG, significant events detection


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Lékař má k dispozici efektivní nástroj,

umožňující pomocí kursoru vybrat př íslušnou

část originálního záznamu a pokračovat v

prohlížení buďto v časové oblasti EEG, nebo ve

schematickém diagramu. K tomu může vyvolat

sumární multikanálovou informaci o typu EEGaktivity

EEG with a seizureEEG with a seizure


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Časový profil dlouhodobých EEG. Př íklad zobrazení 20 minut. Možnost zobrazit až 3 hodiny

záznamu na edné stránce. Dva e ile tické záchvat o trvání 2 minut .

Z ačátek epi lept ického záchva tu v 11 m inutě ,vybraný k ursorem z

Sleep EEG analysisSleep EEG analysis (1.5(1.5 hour/page,hour/page, 11 channel)channel)


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Comparison with another methodsComparison with another methods -- CSACSA -- spectral informationspectral information only,only,shape of graphoelements is lostshape of graphoelements is lost


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CaseCase study:study: Detection and quantification of sleep stages inDetection and quantification of sleep stages innewbornsnewborns (Dr.(Dr. Paul,Paul, ÚÚPMD PodolPMD Podolíí))


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Temporal profiles processingTemporal profiles processing -- sleep stages detection in neonatal EEGsleep stages detection in neonatal EEG (120(120minmin of recordings)of recordings)


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VISUAL EVALUATION

AWAKE QUIET SLEEP ACTIVE SLEEP QUIET SLEEP ACTIVE SLEEP QSLEEP AWAKE

Selected publications with impact factor - www.skolicka.fbmi.cvut.cz


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Book chapterBook chapter


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pp

Book chapterBook chapter


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pp

CoursesCourses


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Cooperation with Psychiatric CenterCooperation with Psychiatric Center


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Prague (Dr. Brunovsky, PhD)Prague (Dr. Brunovsky, PhD)


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0_a_introduction into eeg processing

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