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

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In Clinical Practice

where α, β represent the extinction coefficient for the HbO2 and HHb respectively.

Near-Infrared Spectroscopy

[%] 100 x HHbHbO

HbOTOI

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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

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Subspace

Projections

Input space

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Nonlinear regression problem

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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

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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 …

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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

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RR

(ms

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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

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Se

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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

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S1

S2

Beam-former

S1+S2+noise

Attention detection

S1

EEG-steered beamforming in a cocktail-party scenario

EEG

Pre

-p

roce

ssin

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Audio domain EEG domain

Correlate

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ito

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Au

dio

Pre

-p

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ssin

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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