Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf ·...

Epilepsy supervision based on brain and heart activity

analysis

Anton Popov, PhD

Associate Professor, Physical and Biomedical Electronics Dept., National Technical University of Ukraine

“Igor Sikorsky Kyiv Polytechnic Institute”,

Kusatsu, Japan, 2017

Contents

– What is EEG

– EEG methodology

– EEG spectral analysis

– EEG in epilepsy

– Epileptiform patterns localization

– Epileptic seizures prediction

– Heart activity in epilepsy

– Brain-Computer Interface

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EEG history-1

In August 28, 1875 "Electrical currents of the brain". British Medical Journal, 2 (765): 278.

Richard Caton records brain

potentials from cortex of dogs

and apes

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EEG history-2

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In 1924, Hans Berger

• Recorded brain activity from the closed skull

• Reported brain activity changes according to thefunctional state of the brain (1929)

– Alpha and Beta rythms

– Sleep, Hypnosis, mental tasks, telepathic transmission

– Pathological states (epilepsy)

– Cerebral injury

– Drugs: phenobarbital,

morphine, cocaine

6Archive für Psychiatre und Nervenkrankheiten

Electroencephalography

• Is the measurement of the difference in voltage between two different cerebral locations, which then is studied over time

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EEG definition

An electroencephalorgam (EEG) is a record of multichannel signal of potential differences, that can be registered on the intact head surface.

• EEG is the only measurement modality describing the functioning of the brain, since all processes in the brain related to the information processing are of electrical nature

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EEG of healthy subject

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Applications of EEG• Monitoring alertness, coma, and brain death;

• locating areas of damage following head injury, stroke, and tumour;

• testing afferent pathways (by evoked potentials);

• monitoring cognitive engagement (alpha rhythm);

• producing biofeedback situations;

• controlling anaesthesia depth (servo anaesthesia);

• investigating epilepsy and locating seizure origin;

• testing epilepsy drug effects;

• assisting in experimental cortical excision of epileptic focus;

• monitoring the brain development;

• testing drugs for convulsive effects;

• investigating sleep disorders and physiology;

• investigating mental disorders;

• providing a hybrid data recording system together with other imaging modalities;

• deciphering thoughts and intentions in BCI.10

Origin of EEG signal - 1

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Origin of EEG signal - 1

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Electrodes location –10/20 system

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Nasion: point between the forehead and the skull

Inion: bump at the back of the skull

Location: Frontal, Temporal, Parietal, Occipital, Centralz for the central line

Numbers: Even numbers (2,4,6) - right hemisphere, odd (1,3,5) – left hemispere

EEG electrode placement

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EEG recording

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Channel: Recording from a pair of electrodes (here with a common reference: A1 –left ear)

Multichannel EEG recording: up to 256 channels recorded in parallelReference electrode: Mastoid, nose, ear lobe…

Unipolar (monopolar) measurement

Bipolar measurement

EEG reference systems

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EEG recording

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EEG recorders – 2

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OpenEEG project

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http://openeeg.sourceforge.net/doc/modeeg/modeeg.html

EEG of healthy subject

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EEG rhythms – 1

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Rhythm Frequency

(Hz)

Amplitude

(uV)

Location State of mind,

Source of activity

Alpha (α) 8 – 13 50 – 100 Occipital, Parietalregions

Adults, rest, eyes closed. Alpha blockadeoccurs when new stimulus is processed.Oscillating thalamic pacemaker neurons.

Beta (β) 14 - 40 2 – 20 Frontal region

Adult, mental activity.

Reflects specific information processing betweencortex and thalamus

Gamma (γ) 40-70 (100) 5 – 7 Variable location

Is believed to be reflecting mental activity, consciousness, meditation states etc. Associated with sensory processing.

Delta (δ) 0.5 – 4 Above 50 Variable location

Deep sleep, children in sleep.Oscillations in Thalamus and deep corticallayers. Usually inibited by ARAS (AscendingReticular Activation System)

Theta (θ) 5 – 7 5 – 100 Occipital Children, drowsy adult, sleepiness, emotional distress.Nucleus reticularis slows oscillating thalamicneurons , therefore sensory throughput to cortexdiminished.

Mu (μ) 8 – 13 up to 50 Motor cortex

Associated with sensorimotor transformation. Suppressed when person performs real of imaginary motor action.

Changes in EEG patterns

• Ageing: reduction of the alpha waves, bilateral slowing in the theta and delta waves, increase in beta wave activity(intellectual loss)

• Dementia: alpha rhythm slows, delta and theta wave activities increase, beta wave activity may decrease, epileptiform discharges and triphasic waves can appear

• Epileptic Seizure and Nonepileptic Attacks: sudden change of frequency, sudden desynchronization of activity, gradual change from chaotic to ordered waveforms

• Psychiatric Disorders: associated EEG features have been rarely analytically investigated

• External Effects: pharmacological and drug, music, cognitive

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EEG spectral analysis

• In classical Fourier analysis the signal is represented as a sum of sinusoidal oscillations of harmonically increasing frequencies

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EEG spectrum, ageing

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EEG spectrum, sleep

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EEG spectrum, drugs

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EEG spectrum, dementia

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Brain functional regions

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Quantitative EEG (qEEG)

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Topographical maps plot EEG data on a map of thebrain surface.

Usual data plotted:

• ERP maps– potential changes

• Spectral maps– frequency changes

• Statistical maps– comparison of

measurements

Time-frequency analysis

• TFA allows to track time changes in spectral contents of EEG signals

• Short-Time Fourier transform is used to analyze EEG

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Real-time Brain activity mapping

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Cross-spectra and Coherence

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Cross-correlation function of two signals

Cross- spectra of two signals

Spectral coherence of two signals

Coherence during relaxation

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Coherence during meditation

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Epilepsy

• Epilepsy is a group of diseases characterised in most cases by repeated sudden unpredictable seizures caused by excessive abnormal synchronous activity of neuronal groups in the brain.

• Clinical manifestations of epilepsy are unforeseen and abrupt motor phenomena like tremor and convulsions, lost of consciousness, breathing pauses and respiratory changes, hallucinations, screaming and other psychic and sensory symptoms.

– Causes: insults, trauma, vascular diseases, infections, neurodegeneration, inflammations, cysts, tumors, genetic inclinations, developmental defects.

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Epileptic seizures

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• Partial (focal)• Simple partial

• Motor

• Somatosensory

• Autonomic

• Psychological

• Complex partial

• Simple partial with impaired consciousness

• Partial seizures with secondary generalization

• Generalized• Absence

• Tonic

• Clonic

• Tonic-clonic

• Atonic

• Myoclonic

EEG during epileptic seizure

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EEG activity in Epilepsy

Epilepsy

Normal activity

Abnormal activity

Pre-ictal

Inter-ictal Ictal

Focal activity

Generalized activity

Multifocal

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Targets of EEG analysis in epilepsy

Automated analysis of

EEG

Epileptic activity

detection

Epileptic spike

detection

Epileptic seizure analysis

Seizure detection

Seizure prediction

Seizure classification

Seizure focus localization

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Epileptic seizure analysisFeatures of

seizure activity

Spectral and wavelet

characteristics

Amplitude distribution

Spatial distribution

Chaotic characteristics

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Classificationof seizure

activity

Nearest neighbor

Artificial Neural Nets

Support Vector

Machines

Decision Trees

Fuzzy Classifiers

Epileptic Pattern Localization• Epileptic oscillation complexes in human electroencephalogram -

complexes of a sharp wave and a slow wave, indicating the presence of neuronal epileptic discharges in the brain.

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Epileptiform Complexes – 1• At the early stages of disease, epileptiform complexes

are rare, distorted, non-prominent and low-amplitude.

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Objective – to detect and properly localize the epileptiform complexes in EEG.

Early detection –earlier start of treatment –

more favorable prognosis of recovery

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«generic» epileptiform complex

Parameters of possible acceptable distortions

Template for the Class of Epileptiform Complexes

1 1, 1,, min maxA K

i Q n zd f n f n

Pseudometric:

Template for the class: 1 1 1 1 11

,K K K K KKn C S A L V

Magnitude Alternation

t

U

Offset relative to isoelectric line

Magnitude Ratio AlternationDuration alternation

Presence of slow trend

Template matching – 1

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1

11 12 11

21 22 22

1 2

N

N

K

M M MNM

x x xX

x x xXX

x x xX

1

1 M

ij ki kjk

c x xM

1

det 0KC I

1

11 12 1

21 22 2

1 2

N

N

K

N N NN

c c c

c c cC

c c c

1K k k kC v v

1 1,

maxK kk N

1 1 1 1K K K KC

1 1 2[ , , , ]K N Main eigenvector keeps the most information about member’s prevailing and dominating properties.

Template matching – 2

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Duration:

Magnitude:

Offset:

Trend:

Magnitude Ratio:

Template matching – 3

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TP – 85%

FP (while all diagnostically meaningful complexes are detected) – 15%

Sensitivity – 82%

Selectivity – 72%

Percentage P of TP (○) and FP (□) from threshold dА

Template matching – 4

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Template matching – 5

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Template matching – 6

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Template matching – 7

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Epileptic seizures control

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Epileptic seizure prediction – 1

Epilepsy is a group of long-term neurological disorders characterized by epileptic seizures.

• About 1-2% of people worldwide (65 million)have epilepsy;

• 36% patients have seizures, that cannot becontrolled with medication

• Control of seizures:

– Detection

• Prediction

– Prevention

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Seizure prediction and prevention

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Neurostimualator

Simulator control

Classification and prediction

Multiparametric data analysis system

Feature extraction from EEG and/or ECG

Linear/nonlinear analysis and coupling estimation

Situation analysis and optimized feature set

Neurostimulator control strategy selection

Generation of signals for direct brain/nerve tissue stimulation

Neurostimulator with Feedback

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Seizure predictionIctal

Pre-ictalInter-ictal

Seizure prediction approaches

Patient-specific

• Easier to build

• A lot of data from each patient is needed

• Possibility to use feature selection for each subject

Non-patient-specific

• Harder to build

• Comprehensive dataset is needed

• Extensive and descriptive set of features are needed due to inter-subject diversity of EEG characteristics

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A4OP3

Слайд 60

A4 тут в тексте надо упомянуть и про пациент-специфический подход?Admin; 20.08.2016

OP3 да, спасибо. дописалOleg Panichev; 22.08.2016

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Correlation features extraction

1

2 2

1 1

,

Nm m n ni i

imn

N Nm m n ni i

i i

x x x x

r

x x x x

Channel

Channel

Patient-specific classification

Window length for largest AUC. Mean AUC is 0.97±0.028.

Patient

Win

dow

length

, s

Classification performance in prediction of epileptic seizures

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Seizure prediction performance (AUC values) for SVM classifier (left), and DA classifier (right) depending on epoch length and duration of pre-ictal segment for

one patient from group of patients with focal seizures

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Derivation scheme influence

Permutation Entropy

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PE is a descriptor of time series complexity, unpredictability, disorder, chaoticity, nonlinearity and stochasticity and can reflect complex nonlinear interconnections between anatomical and functional subsystems emerged in brain in healthy state and during various diseases.

1

j

q jp

N m l

!

1

log

,log !

m

j jj

norm

p p

PermEn m lm

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`

Lag=1

Lag=2

Lag=3

Lag=4

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0 1 2 3 4 5 6 7 8 9 10-50

0

50

x(t),

uV

0 1 2 3 4 5 6 7 8 9 10-50

0

50

x(t)

, uV

0 1 2 3 4 5 6 7 8 9 10-1000

0

1000

Time, sec

x(t)

, uV

0

50

100

23

45

67

80.4

0.6

0.8

1

1.2

1.4

LagOrder

PE

0.5

0.6

0.7

0.8

0.9

1

0

50

100

23

45

67

80.2

0.4

0.6

0.8

1

1.2

LagOrder

PE

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

100

101

102

103

0.4

0.5

0.6

0.7

0.8

0.9

1

Lag

PE

PE as a function of time lag and order

for tree types of EEG signals

PE saturates starting from some lag value, different for various signal types

Permutation Entropy averaged values for 5th order for different combination of lags and

downsampling factor

1 2 3 4 5 6 7 8 9 10

12

34

56

78

910

0

0.2

0.4

0.6

0.8

1

l

Order m=5

M

Me

an P

E

0.4

0.5

0.6

0.7

0.8

0.9

PE for seizure prediction

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0 2000 4000 6000 8000 10000 12000-400

-200

0

200

400EEG Signal # 2

0 50 100 150 200 250 300 350 400 4500.7

0.8

0.9

1PermEn of order m=3

Interhemispheric Interactions- Left: mostly analytics (speech, input

signals), telic actions,

- Left hemisphere is mainly responsible for speech

- Right: interaction with environment, automatic actions, shape, distance and space perception. Signing!

- People with right hemisphere injuries have difficulties with perception, orientation, memory, face recognition and attention.

- Natural opposition, like sense-intuition, science-art, logic-mystics.

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Synchrony and Symmetry of brain functioning

It is known that the electrical brain activity is more or less synchronous in two hemispheres.

Symmetry is not exact but is highly essential for healthy brain.

Spatial characteristics of brain activity:-Rhythm distribution between zones – areas of dominant activity;-Symmetry of bioelectric activity – similarity of oscillation parameters (e.g.

magnitude and frequency content) in different hemispheres.

• Applications of interhemispheric symmetry estimation:

- Response to functional tests, rhythm assimilation (hyperventilation, photostimulation etc.);

- Right- left-handed persons research;

- Dynamics of activity propagation, dispersion, penetration and entrapping;

- Pathological changes in symmetry (amplification or attenuation of differences);

- Epilepsy research (seizures etc.)70

Symmetry experiment setup

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asymmetricleads

symmetric leads

Phase-Locking Value

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Healthy EEG Epileptic EEG

Symmetric 0.22 ± 0.04 0.12 ± 0.02

Asymmetric 0.10 ± 0.01 0.03 ± 0.02

• In application to evaluation of degree of symmetry between two hemispheres in EEG of healthy people and in the presence of epileptiform activity, significant differencewas shown, and thus it could potentially be an important clinical diagnostic tool.

• EEG phase synchronization for main clinically meaningful bands was analyzed for healthy subjects and the difference between synchronization in symmetric and asymmetric channels was shown with high validity according to the Kruskal-Wallis criteria (p≤0.001) in all frequency bands.

Averaged synchronization

Synchronization for healthy brain

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• EEG phase synchronization for main clinically meaningful bands was analyzed for healthy subjects and the difference between synchronization in symmetric and asymmetric channels was shown with enough validity according to the Kruskal-Wallis criteria.

Delta Theta Beta

Gamma 1, 2 and 3:symmetric, symmetric channels and surrogate signals1. Kolmogorov-Smirnov test (non-normal,

p=0.05)2. Kruskal-Wallis (distributions with equal means), existance of synchronization was validated at p≤0.001 (for gamma (p≤0.05 )

Alpha

Heart activity in epilepsy

• heart rate variability (HRV) is promicing for epileptic seizures’ onsets detection

• interactions between cortical and cardiac parameters might also help to contribute to a more accurate prediction of epileptic seizures.

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HRV changes in epilepsy

• Recurrence plots – characteristics of periodicity and cycling behavior of time series.

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Classification results

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Nonlinear Methods- Phase locking- Transfer entropy- Mutual information- Synchronization likelihood- Granger causality- Cross-spectral analysis- Coherence- Multivariate autoregression- Scatter plots, recurrent plots

Quantitative analysis of interconnections

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Synchronization between brain regions during movements

Brain-Computer Interface• Brain–computer interface (BCI) is a direct communication pathway

between the human brain and an external device.

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BCI system

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Device Allows Brain To Bypass Spinal Cord, Move Paralyzed Limbs – 2014

82http://wexnermedical.osu.edu/mediaroom/pressreleaselisting/new-device-allows-brain-to-bypass-spinal-cord-move-paralyzed-limbs

2 years later

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OpenBCI Project

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A customizable and fully open brain-computer interface platform that gives you access to high-quality brain wave data.

BCI for WoW

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