epilepsy supervision based on brain and heart activity analysispopov/lab_rab/lecture_10_2017.pdf ·...
TRANSCRIPT
![Page 1: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/1.jpg)
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
![Page 2: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/2.jpg)
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
2
![Page 3: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/3.jpg)
3
![Page 4: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/4.jpg)
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
4
![Page 5: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/5.jpg)
EEG history-2
5
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
![Page 6: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/6.jpg)
6Archive für Psychiatre und Nervenkrankheiten
![Page 7: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/7.jpg)
Electroencephalography
• Is the measurement of the difference in voltage between two different cerebral locations, which then is studied over time
7
![Page 8: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/8.jpg)
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
8
![Page 9: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/9.jpg)
EEG of healthy subject
9
![Page 10: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/10.jpg)
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
![Page 11: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/11.jpg)
Origin of EEG signal - 1
11
![Page 12: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/12.jpg)
Origin of EEG signal - 1
12
![Page 13: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/13.jpg)
Electrodes location –10/20 system
13
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
![Page 14: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/14.jpg)
EEG electrode placement
14
![Page 15: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/15.jpg)
EEG recording
15
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
![Page 16: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/16.jpg)
EEG reference systems
16
![Page 17: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/17.jpg)
EEG recording
17
![Page 18: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/18.jpg)
EEG recorders – 2
18
![Page 19: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/19.jpg)
OpenEEG project
19
http://openeeg.sourceforge.net/doc/modeeg/modeeg.html
![Page 20: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/20.jpg)
EEG of healthy subject
20
![Page 21: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/21.jpg)
EEG rhythms – 1
21
![Page 22: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/22.jpg)
22
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.
![Page 23: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/23.jpg)
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
23
![Page 24: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/24.jpg)
EEG spectral analysis
• In classical Fourier analysis the signal is represented as a sum of sinusoidal oscillations of harmonically increasing frequencies
24
![Page 25: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/25.jpg)
EEG spectrum, ageing
25
![Page 26: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/26.jpg)
EEG spectrum, sleep
26
![Page 27: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/27.jpg)
EEG spectrum, drugs
27
![Page 28: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/28.jpg)
EEG spectrum, dementia
28
![Page 29: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/29.jpg)
Brain functional regions
29
![Page 30: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/30.jpg)
Quantitative EEG (qEEG)
30
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
![Page 31: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/31.jpg)
Time-frequency analysis
• TFA allows to track time changes in spectral contents of EEG signals
• Short-Time Fourier transform is used to analyze EEG
31
![Page 32: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/32.jpg)
32
![Page 33: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/33.jpg)
Real-time Brain activity mapping
33
![Page 34: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/34.jpg)
Cross-spectra and Coherence
34
Cross-correlation function of two signals
Cross- spectra of two signals
Spectral coherence of two signals
![Page 35: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/35.jpg)
Coherence during relaxation
35
![Page 36: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/36.jpg)
Coherence during meditation
36
![Page 37: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/37.jpg)
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.
37
![Page 38: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/38.jpg)
Epileptic seizures
38
• 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
![Page 39: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/39.jpg)
EEG during epileptic seizure
39
![Page 40: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/40.jpg)
EEG activity in Epilepsy
Epilepsy
Normal activity
Abnormal activity
Pre-ictal
Inter-ictal Ictal
Focal activity
Generalized activity
Multifocal
40
![Page 41: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/41.jpg)
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
41
![Page 42: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/42.jpg)
Epileptic seizure analysisFeatures of
seizure activity
Spectral and wavelet
characteristics
Amplitude distribution
Spatial distribution
Chaotic characteristics
42
Classificationof seizure
activity
Nearest neighbor
Artificial Neural Nets
Support Vector
Machines
Decision Trees
Fuzzy Classifiers
![Page 43: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/43.jpg)
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.
43
![Page 44: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/44.jpg)
44
![Page 45: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/45.jpg)
45
![Page 46: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/46.jpg)
Epileptiform Complexes – 1• At the early stages of disease, epileptiform complexes
are rare, distorted, non-prominent and low-amplitude.
46
Objective – to detect and properly localize the epileptiform complexes in EEG.
Early detection –earlier start of treatment –
more favorable prognosis of recovery
![Page 47: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/47.jpg)
47
«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
![Page 48: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/48.jpg)
Template matching – 1
48
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.
![Page 49: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/49.jpg)
Template matching – 2
49
Duration:
Magnitude:
Offset:
Trend:
Magnitude Ratio:
![Page 50: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/50.jpg)
Template matching – 3
50
TP – 85%
FP (while all diagnostically meaningful complexes are detected) – 15%
Sensitivity – 82%
Selectivity – 72%
Percentage P of TP (○) and FP (□) from threshold dА
![Page 51: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/51.jpg)
Template matching – 4
51
![Page 52: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/52.jpg)
Template matching – 5
52
![Page 53: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/53.jpg)
Template matching – 6
53
![Page 54: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/54.jpg)
Template matching – 7
54
![Page 55: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/55.jpg)
Epileptic seizures control
55
![Page 56: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/56.jpg)
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
56
![Page 57: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/57.jpg)
Seizure prediction and prevention
57
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
![Page 58: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/58.jpg)
Neurostimulator with Feedback
58
![Page 59: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/59.jpg)
Seizure predictionIctal
Pre-ictalInter-ictal
![Page 60: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/60.jpg)
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
60
A4OP3
![Page 61: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/61.jpg)
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
![Page 62: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/62.jpg)
61
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
![Page 63: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/63.jpg)
Patient-specific classification
Window length for largest AUC. Mean AUC is 0.97±0.028.
Patient
Win
dow
length
, s
![Page 64: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/64.jpg)
Classification performance in prediction of epileptic seizures
63
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
![Page 65: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/65.jpg)
64
Derivation scheme influence
![Page 66: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/66.jpg)
Permutation Entropy
65
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
![Page 67: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/67.jpg)
66
`
Lag=1
Lag=2
Lag=3
Lag=4
![Page 68: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/68.jpg)
67
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
![Page 69: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/69.jpg)
PE for seizure prediction
68
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
![Page 70: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/70.jpg)
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.
69
![Page 71: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/71.jpg)
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
![Page 72: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/72.jpg)
Symmetry experiment setup
71
asymmetricleads
symmetric leads
![Page 73: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/73.jpg)
Phase-Locking Value
72
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
![Page 74: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/74.jpg)
Synchronization for healthy brain
73
• 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
![Page 75: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/75.jpg)
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.
74
![Page 76: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/76.jpg)
HRV changes in epilepsy
• Recurrence plots – characteristics of periodicity and cycling behavior of time series.
75
![Page 77: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/77.jpg)
Classification results
76
![Page 78: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/78.jpg)
77
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
![Page 79: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/79.jpg)
78
Synchronization between brain regions during movements
![Page 80: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/80.jpg)
Brain-Computer Interface• Brain–computer interface (BCI) is a direct communication pathway
between the human brain and an external device.
79
![Page 81: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/81.jpg)
BCI system
80
![Page 82: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/82.jpg)
81
![Page 83: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/83.jpg)
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
![Page 84: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/84.jpg)
2 years later
83
![Page 85: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/85.jpg)
OpenBCI Project
84
A customizable and fully open brain-computer interface platform that gives you access to high-quality brain wave data.
![Page 86: Epilepsy supervision based on brain and heart activity analysispopov/Lab_Rab/lecture_10_2017.pdf · 2017. 4. 20. · Epilepsy supervision based on brain and heart activity analysis](https://reader035.vdocuments.site/reader035/viewer/2022071105/5fdfab94b72df25dd31921ee/html5/thumbnails/86.jpg)
BCI for WoW
85