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BIOMEDICAL
DATA PROCESSINGresearch overview
Head:
Sabine Van Huffel
Alexander Bertrand
Compressed Sensing
for Biomedical Signals
Yipeng Liu
Compressed Sensing Theory
Multi-sparse signal recovery• Multi-sparsity• Sparsity + Low Rank
Robust sparse signal recovery• Measurement matrix uncertainty• Representation matrix uncertainty• Quantification error
Compressed Sensing for Medical Monitoring
wired wireless Wireless Body Area Network
Best dictionary for sparse EEG, EMG, ECG representation
Multi-correlated-channel sparse signal recovery
Missing component analysis for EMG
Compressed Sensing for Magnetic
Resonance Imaging
Magnetic Resonance Imaging samples the frequency space
of the human body
Data set consists of Fourier Coefficients
Different sampling patterns
Best dictionary for different kinds of MRIsConvex Optimization Model using a priori structural information:
SparsityPiecewise smoothLow RankSlow time variation
Efficient solver: Alternating Direction Method of Multiplier
NeoGuard:
Neonatal Brain Monitoring
Vladimir Matic,
Ninah Koolen, Amir H. Ansari
UZ Leuven partners: G. Naulaers, J. Vervisch,
K. Jansen, A. Dereymaeker
• Newborn baby is admitted at the
Neonatal Intensive Care Unit• Prematurity
• EEG monitoring Starts promptly !
• What are the brain functions?• No neurological experts present 24/7
• No scans for small babies
• No MRIs
• Limited time window for
interventions • therapeutic hypothermia has to start within
the 6 hours after birth
NeoGuard
Neonatal Brain Monitoring
NeoGuard: Decision Support
• Brain injury estimate• Detection of neonatal epileptic
seizures
• Seizures localization
• Inter-burst intervals
• Incorporated expertise• Knowledge of neurophysiologists are
incorporated into algorithms
• Monitoring• evolution rate of the background EEG
• Maturity in premature
• Outcome prediction• Good
• Poor
NeoGuard: User Interface
NeoGuard: Clinical Research Neonatal epileptic seizures Inter-burst intervals
Cerebral
Hemodynamics
Monitoring in
Neonates
Alexander Caicedo Dorado
UZ Leuven partners: Gunnar Naulaers
Why to monitor Cerebral Hemodynamics?
MABP
Temperature
CO2
Metabolism
Neurogenic
Control
Glucose
Concentration
CBF
TOI
Regulation
Mechanisms
Impaired regulation
Hemorrhage Ischemia
Brain damage
Proper
BRAIN Hemodynamics
Monitor cerebral autoregulation in a clinical
environment.
GBdCI
IA ...log 0
C,
0I I
d
Beert-Lambert law
BdCI
IA ...log 0
BdCHbOBdCHHbA
BdCHbOBdCHHbA
......
......
2
2
222
111
In Clinical Practice
where α, β represent the extinction coefficient for the HbO2 and HHb respectively.
Near-Infrared Spectroscopy
[%] 100 x HHbHbO
HbOTOI
2
2
kk
k
NIRS is used as a surrogate measurement for Cerebral Blood Flow
Regulation Mechanisms
Cerebral Autoregulation CO2 Vaso-reactivity
MABP ↔ CBF CO2 ↔ CBF
Cerebral Autoregulation
Taken from
http://www.sofiascdhstory.com/2008_01_01_archive.html
Cerebral
CO2 vaso-reactivity
?
Systemic Variables
MABPCO2
Temperature
HR
SaO2
Hemodynamic
Variables
TOI
CBF
rScO2
HbD CBFv
Mathematical Tools
CORR
COHTF
CCA
Subspace Projections
WBTFLinear
Non-Linear KPCR
Clinical Outcome
Cerebral Haemodynamic
Status
Problem Layout
Analyzed recording
Clinical Case Study: Lamb
Methods:
• Animal model → Induced variations in MABP
• Nonlinear regression → KPCR.
• Clinical interpretability → Subspace projections
Nonlinear
Regression
Clinical
Interpretation?
Computation
Time?
Model?
Training
Model?
Y
Decompose Y as a linear combination of
nonlinear contributions of the systemic
variables.
Feature space
x xxxxx
x
xxx
xxxx
x
xxxx
xxx
xxxxx
xxxxxxxxx
xxxxx
xx
xxxxx
xx
xx ix
Subspace
Projections
Input space
xx
xx
xxx
xxxxxxx
xxx
x
xxxxxxxxxx
xxxx
x
xxxx
xx
x
xxx
xxx
x
xx
xxx
TNxxx 21
Nonlinear regression problem
1sCol
Y
dsCol
TNull
YQ
YP
YP
11 /
YP
dd /
Clinical Case Study: Lamb
Clinical Case Study: Lamb
Results I:
• MABP-TOI → autoregulation curve?
• MABP-EtCO2-TOI → highly nonlinear relation.
Signal Decomposition
Estimated nonlinear relationship between
MABP-EtCO2 and TOI.
Autoregulation is not
so simple
Signal processing for
home monitoring of
epileptic children
Thomas De Cooman, Carolina Varon
Kris Cuppens and Milica Milosevic
UZ Leuven partners: Lieven Lagae,
Katrien Jansen
Pulderbos partners: Berten Ceulemans
Anouk Van de Vel
Epileptic seizure detectors based on ECG
Seizures
Heart Rate
Respiration
ANS
Seizures and the autonomic nervous system (ANS)
SeizureSeizures Pre-ictal changes Autonomic symptoms Motor activity Stress response Apnea episodes Reduced HRV Tachycardia or bradycardia
Goal: Detect cardiac and respiratory changes caused by seizures
Epilepsy monitoring at homewireless accelerometers
Electrocardiogram
Electromyogram
Combine
modalities in
optimal way
Single decision output
-Learn online which sensors are useful for the patient
-Other sensors can be removed after a while
-Minimal number of necessary sensors used for patient convenience
-Done by using online l0-norm optimization in SVM classifier
Adaptive learning for improved usability
Problem: Seizure data very patient-specific
Collecting patient-specific data however too time consuming for short-term monitoring
Benefits compared to patient-specific algorithm:-Directly usable-No patient-specific seizure data required-Quick adaption to patient-specific requirements
Initially patient-independent system
Epilepsy monitoring at home: applications
Early seizure detection
Data Time Description
1 22/01/2012 22:37:16 CLONIC-TONIC seizure
2 22/01/2012 23:13:36 TONIC seizure
3 …
Logging of detected seizures Inform neurologist
Alter treatment/ medication
Comfort patient
Signal processing and
machine learning
supporting the
diagnosis of epilepsy
Borbála Hunyadi
UZ Leuven partners: Wim Van Paesschen
Patrick Dupont
Video-EEG monitoring in the clinical
environment
Early seizure detection Ictal SPECT scan
Seizure detection incorporating structural
information from the multichannel EEGRepresentation of EEG data in higher order arrays:
Goal: Exploit the inherent structure of the EEG signals using novel matrix and tensor-based machine learning solutions:
o Nuclear norm regularization
o Tensorial kernels
features
channels
channels
channels
featuresfrequency
time
channels
Classifier matrix
fMRI-based localization of the
epileptogenic zone
Goal: determine and resect the epileptogenic zone
Simultaneous EEG-fMRI is traditionally analyzed within the GLM framework, relying on IED timing
Spike timing
Disadvantages: EEG analysis is time consuming Not reliable due to artifacts IEDs from deep structures are
not recorded No IEDs occurs during recordings
Objective:
Localize epileptic activity based on fMRI time series, without using EEG
Data-driven fMRI analysis for localizing
the epileptogenic zone
fMRI time series
Spatial ICA
X = A S
Extract discriminative features
Supervised classification to
select epileptic ICsConcordant with the EZ?
EEG-fMRI data fusion
for the study of brain
function
Borbála Hunyadi, Bogdan Mijović and
Maarten de Vos
UZ Leuven partners: Stefan Sunaert,
Wim Van Paesschen
Dept. of Psychology: Johan Wagemans
Simultaneous EEG-fMRI fusion
Simultaneous recording during task / rest
EEG measures electrical activity with
good temporal resolution, e.g. ERPs /
interictal activity in the EEG
fMRI measures active brain regions with
good spatial resolution
Goal:
spatiotemporal characterization of
neural processes via
EEG-fMRI fusion, achieved by
jointICA, coupled matrix - tensor
and tensor - tensor factorization
Application
Study cognitive functioning
Study the epileptic network
Visual Detection Study
Visual Path
Perceptual Grouping Study
Mobile EEG
Rob Zink, Borbala Hunyadi, Maarten
De Vos
Signal and cognitive analysis of
mobile EEG data.Hardware: • Mobile EEG with 24 channel (wet) electrode cap.
• Transmission via Bluetooth to Laptop or Smartphone
• High 500Hz sampling rate
• Long >4 hours battery life
Methods: • Decompose EEG into signal and noise using Tensor decompositions.
• Structured classification algorithms.
• Quantify motion artefacts during recordings.
• Cross-subject analysis
• Deal with non-stationarities in the EEG and unknown nuisance.
Data:• Transition from lab to real-life
• Novel data recording
• Auditory based
• Assistive applications e.g. Brain-Computer-Interfaces
Towards data-driven classifiers: Tensor Methods• Tensor decompositions for better modelling the high dimensional EEG data
• CPD, BTD, MLSVD…
• Use structured information in the EEG: Channels, time, frequency, repeated
measurements, stimulus types…
Application to mobile BCI:
Develop effective:
• Preprocessing.
• Tensorization.
• Choice of decomposition type.
• Choice of decomposition parameters.
• Definition of constraints.
81% Accuracy
Channels
Time
Data Driven ClassificationExample: Single subject unsupervised
decomposition of a BCI dataset with CPD.Auto-Distinction between target and non-target stimuli
Mobile EEG: Data Acquisition
Outdoor/Indoor – e.g. BCI, artefacts generation…
Spike classification
and analysis
Alexander Bertrand, Ivan Gligorijević
UZ Leuven partners: Bart Nuttin
IMEC : Fabian Kloosterman
• Associating each observed spike with a particular neuron (source)
• Makes the statistical analysis possible which further reveals interconnections and functional changes
Spike detection and sorting
Spike classification in deep brain recordings
Parameter SNR range
1.7-2 2-3 3-3.7
variability (2.812.48)% (3.252.25)% (2.823.22)%
skewness (7.757.52)% (12.9710.63)% (12.3412.04)%
kurtosis (16.5614.74)% (21.5016.12)% (20.9618.80)%
f>0.8*mean (11.438.60)% (11.5712.42)% (7.023.51)%
The goal:
picture source: IMEC Belgium
Statistical analysis on the classified spike trains
Picture source:
http://www.33rdsquare.com/2012/05/deep-brain-
stimulation-effective-in.html
Spike classification in surface EMG
The goal
Determine what motor units are active (and when) based on recording observations
Picture: website of A. Holobar
Tip
Each motor unit has unique “signature”
EMG – spike classification
0 5 10 15
-2
0
2
4
6
8
10
Spatio-temporal information from selected subset of electrodes assists the classification
Different classified “signatures”
Analyzing dynamics of
the cardiovascular
and respiratory
system
Devy Widjaja and Carolina Varon
Dept. of Psychology partners:
Ilse Van Diest, Omer Van Den
Bergh, Elke Vlemincx
ECG-derived respiration (EDR)Respiration causes changes in morphology of recorded ECG
Goal: reduce the number of sensors during monitoring by deriving the respiration signal from the ECG signal
use this interaction to estimate a respiratory signal from the ECG = ECG-derived respiration (EDR)
Time [s]
Reference
respiratory signal
EDR based on
kernel PCA
EDR based
on PCA
EDR algorithms based on R peak amplitude Area in QRS complex (Kernel) principal component analysis
to analyze respiratory beat-to-beat changes
…
Heart rate variability (HRV)
Variability of the heart rate is a non-invasive marker of autonomic activity
APPLICATIONS
analysis of HRV to study the effect of …
633 609 605 615 629 645 682
RR interval = time between 2 heart beats
… prematurity … epilepsy… stress … prenatal anxiety
… etc
Tachogram
Separating respiratory influences from
the tachogramGoal: Gain insight in the interpretation of heart rate variability (HRV)
How? Separate respiration-related variations in the heart rate
Time (s)
Tachogram (= contains time between 2 heart beats)
Respiration-related part of the tachogram
Residual tachogram = not directly related to respiration
Respiration signal
Time (s)
Analyze RRresp and RRresidual separately when assessing HRV
Cardiorespiratory interactions
Time
Heart
Rate
Resp
• Inspiration Increase in heart rate
• Expiration Decrease in heart rate
Optimal pulmonary gas exchange
Quantifying cardiorespiratory interactionsE
CG
RR
(m
s)
Re
sp
Resp: Respiration measured or estimated
RR Resp
Methods Phase-Rectified Signal Averaging (PRSA) Information dynamics Subspace projections
Tensor based ECG
processing for the
prediction of sudden
cardiac death
Griet Goovaerts
UZ Leuven partners: Rik Willems
Sudden cardiac death
Prevention: implantable cardioverter-defibrillator
Main problem: patient selection
Sudden cardiac death:
• Unexpected, natural death
• Cardiac cause
• < 1h after start symptoms
• Person without previous problem
2nd most important cause of death: 15 000 deaths/year in
Belgium!
Current diagnosis
1. Echocardiography: LVEF
2. Electrocardiogram (ECG)
• Spatial variation: QT dispersion
• Temporal variation: T wave alternans
New approach: tensors
= ‘general’ matrix
Vector: 1 dimension
Matrix: 2 dimensions
Tensor: n dimensions
Approach
1. ECG segmentation: QRS + T-wave detection
2. Tensorisation: 2D ECG signal 3D tensor
3. Tensor decompostion
Canonical polyadic decomposition Block term decomposition
Sleep Monitoring
Carolina Varón & Tim Willemen
UZ Leuven partners: Bertien Buyse,
Dries Testelmans
Alterations in the airflow during sleep:
Hypopneas
Reduced airflow
Apneas
Complete absence of airflow
− Obstructive
− Central
− Mixed
Nocturia
Fatigue
Depression
Attention
deficit
Memory
loss
Morning
headache
Dry mouth
throat
Snoring
Insomnia
Sleep
Apnea
Sleep Apnea
Diagnosed using Polysomnography
– ECG
– Respiration
– Oxigenation
– EEG amongst others ...
Apnea episodes can be detected from the heart rate
0 10 20 30 40 50 60
0
0.5
1
Time (s)
0 10 20 30 40 50 60600
800
1000
1200
1400
Time (s)
RR
(ms
)
Apnea
Apnea in the respiratory signal
Sleep Apnea
Sleep Apnea Detection from the ECGGeneral procedure
Preprocessing:Develop algorithms to enhance the quality of the ECG
Goals
R-peak detection:Automated algorithms to accurately detect R-peaks
Heart rate variability (HRV):Derive informative features from the ECG and heart rate
Respiratory analysis:Extract respiratory information from the ECG(EDR: ECG derived respiration)
Feature selection:Identify the most relevant features to detect sleep apnea
Learning algorithms:Develop new methodologies to study respiratory disorders during sleep
NXT_SLEEP: next generation sleep
monitoring platform
Industrial partners: IMEC, NXP, LightSpeed, Custom8, Fifthplay
Academic partners: KUL STADIUS, KUL CUO, VUB SMIT, UA Sleep Lab
Diana M. Sima, Anca R. Croitor Sava,
Nicolas Sauwen, Adrian Ion-Margineanu,
Bharath HN, Michal Jablonski, Claudio
Stamile
UZ Leuven partners: Uwe Himmelreich,
Sofie Van Cauter
Stefan Sunaert
ESAT-PSI : Frederik Maes
Signal Processing and
Classification for
Magnetic Resonance
Spectroscopy and
multi-modal MRI
Metabolite quantification for
Magnetic Resonance Spectroscopy (MRS)
MRS quantification
Metabolite concentrations
Single-voxel MRS
Metabolite quantification for MRS Imaging
(MRSI)
Metabolite maps
NAA Myo Cr PCho
Glu Lac Lip1 Lip2
Ala Glc Tau
Multi-voxel MRS MRS quantification
using spatial information
Metabolite concentrations = biomarkers of disease
Supervised classification based on MRI and
MRS(I) for brain tumor diagnosisTraining data set: LS-SVM classifier,
Canonical correlation analysis
New patient
… ?…
yellow = grade II glioma, orange = grade III glioma, purple = meningioma, green = CSF, light blue = white matter, dark blue = grey matter
Nosologic imaging: segmentation and
supervised brain tumor classification
Unsupervised tissue type differentiation:
Blind Source Separation for MRSI dataX = matrix of spectra, X W H
min || X - W H || such that W 0, H 0
Non-negative matrix factorization (NMF)
MRSI
Applications
Brain tumor tissue typing
normal tumor necrosis
Prostate segmentation
Multi-parametric MR data processing and classification
methods for brain tumor diagnosis and follow-up
T2 T1 CBV Cerebral Blood
Volume
MD Mean
Diffusion
MK Mean
Kurtosis
NAA, Cre, tCho, Lip1,...
Conventional MRI ~ tissue structure
Perfusion MRI ~ vascularization
Diffusion MRI ~ water mobility
MRSI ~ metabolite
concentrations
Fields of study:
• Classification of brain tumors, characterizing heterogeneity
• Follow-up study: early detection of success of therapy,
pseudoprogression ↔ recurrence
Non-negative Matrix Factorization on multi-modal
MRI data for tissue differentiation in brain tumors
NMF: X W H
X: multi-modal input data
W: tissue-specific patterns
H: tissue abundance maps
Representation of Spectra in a tensor
Non-negative canonical polyadic decomposition
HD : Tissue abundancies maps
W: Tissue-specific Spectra
Recover the spectra 𝑊 = (𝐻†𝑋𝑇)𝑇.Recover the un-normalized abundancies, HD
S 0, H 0
Models for clinical decision
support
Vanya Van Belle, Laure Wynants and Lieven
Billiet
UZ Leuven partners: Dirk Timmerman
Ben Van Calster
ESAT-Stadius: Johan Suykens
Classification of medical dataOvarian tumors: in collaboration with University Hospitals K.U.Leuven and the IOTA consortiumPregnancy viability: in collaboration with London-based hospitals, e.g. Queen Charlotte’s and Chelsea
Data + outcome(training data, possibly from multiple hospitals)
Variable selectionModel training withprobabilistic output
Model evaluation (discrimination, calibration, clinical utility)- test data- bootstrapping
Model visualization
Binary and multiclasslogistic regression models
Generalized linear mixed models
(Interpretable) support vector machines
0 0.2 0.4 0.6 0.8 10
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0.5
0.6
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0.8
0.9
1
1 - Specificity
Se
ns
itiv
ity
ROC curve
Probability of malignancy
Test Data
Multicenter studies
Multicenter data is often collected to enhance the generalizability of findings.
Building prediction models
that capture
between-center differences
Determining the required sample size for a multicenter study
Quantifying
between-
center
differences
in
measurements
Evaluating the
prediction
performance,
accounting for
heterogeneity
Interpretable models I
Interval Coded Score system for diagnosing adnexal masses:
• Variables→ binary inputs decoding a large number of intervals
• Functional form = step function
• Optimization → sparse results
• Score = sum of points
Interpretable models II
White-box RBF kernel to predict pregnancy viability:
• Expand RBF kernel to• main effects• two-way
interaction effects• discard the rest
• Sparsity mechanism for feature selection
• Visualization of the results
→ Interpretable + flexible!!
General model representation
• dark green: low impact on risk prediction
• light green: large impact on risk prediction
Visualizing risk prediction models for clinical
interpretation
Patient-specific model representation
Length of the bars indicate the contribution to the score
IDO: Sensor-based Platform for the
Accurate and Remote monitoring of
Kine(ma)tics Linked to E-health
(SPARKLE)
Lieven Billiet
UZ Leuven: Rene Westhovens
Kurt De Vlam
ESAT-MICAS: Bob Puers
Revalidation Sc.: B. Van Wanseele
W. Dankaerts
Groep T: Luc Geurts
Activity recognition & pose estimation
WalkingJogging
Accelerometers
Magnetic field
sensors
Gyroscopes
Assessment of capacity
AXIAL
SPONDYLOARTHRITIS
ASSESSMENT
ACTIVITY LIMITATIONS
Patient-reported
outcomesPerformance-based
tests
Bath Ankylosing
Spondylitis
Functional Index
(BASFI) OR
Instrumented BASFI
Timing
Complex
features
Objectivity
SPARKLE
Inte
rdis
cip
lina
ry
EEG-based hearing
screening and auditory
attention detection for
hearing prostheses
Neetha Das, Wouter Biesmans,
Alexander Bertrand
UZ Leuven partners: Tom Francart
Jan Wouters
ESAT-STADIUS: Marc Moonen
Objective hearing screening with EEG
• Determine hearing thresholds by detecting auditory steady-state responses
(ASSRs) in EEG
• Due to low SNR: long measurement time, low sensitivity
Auditory stimulus: sine of 1000Hz,
modulated at 41Hz
EEG spectrum: peak at 41Hz
Objective hearing screening with EEG
High-density EEG + data-driven multi-channel signal estimation techniques
reduced measurement time (at low stimulus levels)
improved sensitivity
Neuro-steered beamforming in a cocktail-
party scenario
?
S1
S2
Beam-former
S1+S2+noise
Attention detection
S1
EEG-steered beamforming in a cocktail-party scenario
EEG
Pre
-p
roce
ssin
g
Audio domain EEG domain
Correlate
S1
S2A
ud
ito
ry m
od
el
Au
dio
Pre
-p
roce
ssin
g
Speech mixtures
de
cod
er
Attention detection
Influence of auditory models on attention detection (pilot study)
Detailed auditory models‘Cheap’ pragmatic model
Biesmans et al. (2015)
Distributed signal
processing for EEG
sensor networks
Alexander Bertrand
Neuromonitoring ‘around the clock’
Chronic neuromonitoring:
• small
• wireless
• energy-efficient
• Heavy, very bulky, highly visible
• Wireless, but insufficient autonomy
• Motion artifacts due to heavy headsets
State-of-the-art mobile EEG systems:
Towards miniaturized EEG modules
Kidmose et al.
(Imperial College)Sclabassi et al.
(Univ. Pittsburgh)
Wireless EEG ‘e-skin’ patches
Rogers et al.
(Univ. Illionois)
‘Skin-grabbing’ electrodes
Subcutaneous leads (below skin)
Hyposafe (Denmark) Do Valle et al. (MIT)
In-ear EEG
Bleichner et al.
(Univ. Oldenburg)
Cochlear implants
Modular EEG systems
wireless EEG sensor networks
Many modules with many channels:
Bandwidth
Transmission energy
Computational energy
Distributed signal processing to the rescue
+ + +
Adaptive filter Adaptive filter Adaptive filter
Eye-blinks in channels 1-4
Eye-blinks in channels 5-8
Eye-blinks in channels 9-12