full presentation - Med-e-Tel

Smart Diagnosis:
current applications
and future challenges
Med-e-Tel conference
Luxemburg
April 9, 2014
Prof. Sabine Van Huffel
Outline
• Introduction
• 3 case studies
• Future challenges
• Personalized: "customized" diagnosis and treatment
• Preventive: prevention is always better than curation,
tailored to the individual patient
• Predictive: precise predictions with modern technology,
determine risk profiles, predict progression and outcome
• Participatory: correct and complete information for the
patient to participate in the decision process
3
3
Lots of data
Temporal Data
Laboratory data
monitoring
Staff observations
9/04/2014
4
Process large
amounts of
time-dependent
data!
?
Brain monitoring for
neurological
diseases
Algorithms
(Technology)
Sensors
(Carriers)
Vital signs monitoring: sleep,
stress, cardio risk
stratification
Pathologies
(Applications)
Smart
Diagnosis
Oncology: cancer diagnosis
and prognosis
Chronic disease
management &
telemonitoring application
Research topics in Biomedical Data Processing @
•EMG & HRV signal processing for stress monitoring
•MR Spectroscopic quantitation & brain tumor recognition using MRS(I)
•(Functional) Near-Infrared Spectroscopy & neonatal brain monitoring
•Cardiovascular dynamics, sleep monitoring & HRV analysis
•Preoperative cancer diagnosis & prognosis: ovarian, prostate, brain, breast
•EEG based signal processing for epileptic monitoring and ERP analysis
•Video/accelerometry for epileptic seizure monitoring
•Spike classification in deep brain recordings & high-density surface EMG
NXT_SLEEP: next generation sleep monitoring platform
• Sleep‐related breathing disorders • Major impact on cardiovascular health
• Prevalence: around 4% in men and 2% in women.
• Severely under diagnosed
• Limited availability of clinical screening
• Polysomnography: time‐consuming, costly, uncomfortable
Insomnia
Nocturia
Fatigue
Snoring
Sleep
Breathing
Disorder
Morning
headache
Depression
Obstructive
Sleep Apnea
Dry mouth
throat
Attention
deficit
Memory
loss
NXT_SLEEP: next generation sleep monitoring platform
Apnea episodes in the heart rhythm
Apnea
1
0.5
0
0
10
20
30
Time (s)
40
50
60
Apnea episodes in the respiratory signal
1400
RR(ms)
1200
1000
800
600
0
10
20
30
Time (s)
40
50
60
NXT_SLEEP: next generation sleep monitoring platform
Epilepsy monitoring • EEG (clinic)  golden standard for epilepsy monitoring • Non‐EEG  video, audio, radar, ECG, respiration, EMG,ACM
• Commercially available systems:
→ Efficiency lacks (long, intense & repe ve movement only, excessive false alerts) or insufficiently proven
→ Few seizure logging (EpiWatcher, SmartWatch, EpiCare)
→ Single modality
→ No video data storage
Video‐accelerometry epilepsy monitoring at revalidation center Pulderbos
wrist‐band‐like wireless accelerometers
feature extraction from video
Automated motor seizure detection using accelerometers and video
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Video‐accelerometry epilepsy monitoring at revalidation center Pulderbos
Long‐term Epilepsy monitoring: first results camera
radar
• User friendliness: < program setting steps at start‐up
laptop for storage (Bluetooth) & offline analysis
ACM for wrists & ankles, put in elastic bracelets
• 4 patients with motor seizures monitored during 1 month
• 71 % of the data analyzed (loss due to technical problems)
• Comfort: < 4 bracelets & < easy to remove
•
Efficiency:
– 25 % missed (due to defects, mistakes in reporting, misclassified seizures)
– Not yet sufficiently reliable for detection or alarm but screening tool to detect the 50 % extra seizures
Future Challenges in Epilepsy monitoring:
Home Monitoring & multimodal data acquisition
EEG
Acceleration
ECG
9/04/2014
Video
EEG
ECG
EMG
EOG
video
audio
14
acceleration
NeoGuard: Neonatal Brain Monitoring
•
Partners
KU Leuven-ESAT (STADIUS and MICAS), UZ Leuven neonatology,
EMC Rotterdam, ZNA Middelheim, Ghent Univ.
•
Addressing main problems with infant EEG
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EEG electrodes not baby friendly
Manual (subjective) interpretation
24/7 availability of EEG experts
Goals:
–
–
–
Produce EEG instrument for continuous
brain monitoring (real-time, bedside)
Validate automated seizure detection &
background classification program
Automated clinical decision support
NeoGuard : Clinical Research Despotovic et al,
Human Brain
mapping, 2013
Neonatal epileptic seizures
Koolen et al,
Clini.Neurophys.
Accepted, 2014
Inter-burst intervals in
premature EEG
NeoGuard: Future Challenges in Development
Monitor development
Algorithm development and
optimization
Clinical studies
(combined with EEG and MRI findings)
Multimodality monitoring
• Multilevel: Bedside version for daily clinical use (only show
clinical relevant information) and Extensive research version
• Validation: improved interobserver agreement
• Telemedicine kit for mobile neonatal healthcare
• EEG background activity quantification : more features,
multichannel, connectivity, dynamics evolution
• Source localization: including dynamics of seizures
• Patient customized settings
• Adding non-linear entropy analysis, autoregulation
• Improvement outcome by monitoring:
• Improved understanding clinical relevance seizure types :
spike versus oscillation, rhythmicity, frequency, etc
• Improved understanding clinical relevance EEG background
characteristics: SW cycling, frequency content bursts,
sharp waves, etc
• NIRS
• EEG cap: dry electrodes, smart textile
• Polygraphy: ECG, HRV, Respiration, SaO2, CO2
• Movement (automated video recognition?)
integrated,
BAN, wireless
improved
comfort
Future Challenges in Tech Transfer
Technology transfer to market in medical technology
is a though job !!
o many different stakeholders
o non-trivial business models
o hyper-regulated environment with
separated policy levels
o totally different systems in other countries
o many ethical and legal issues
o niche market of medical diagnostics
• societal valorization is often more important than a
pure economic point of view
• non-straightforward CE labeling or FDA approval
economic
societal
societal
Innovative
solutions
for chronic
diseases
Telehealth facilities
Intelligent
devices
Intelligent systems
supporting decisionmaking
Intelligent organisation
of care
12 partners from the Netherlands, United Kingdom and Belgium join forces to overcome
problems/barriers linked to lack of support for businesses to market new, smart telehealth
products.
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Brainport Development N.V. [NL]
Technische Universiteit Eindhoven [NL]
Catharina-ziekenhuis [NL]
Katholieke Hogeschool Kempen [BE]
Stad Turnhout [BE]
Academisch Ziekenhuis Maastricht [NL]
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Stichting IMEC Nederland (IMEC-NL) [NL]
Health Enterprise East Ltd. [UK]
KU Leuven, Research and development [BE]
Algemeen Ziekenhuis Turnhout [BE]
Universiteit Maastricht [NL]
North East London Foundation Trust [UK]
www.regionalcareportals.eu
Please join our RECAP symposium this
afternoon in Conference room 1!
14:00-15:30hr - RECAP Symposium: Intelligent
16:00-17:30hr - RECAP Symposium: Intelligent
Systems Supporting Decision-Making (1) –
Systems Supporting Decision-Making (2) –
Conference room 1
Conference room 1
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Introduction to the RECAP (Regional Care Portals) Project
Telehealth for Patients with Heart Failure: A Comparison
Marcel de Pender (Programme Manager), Brainport
Between Telehealth Systems
Development, The Netherlands
Carolina Varon & Prof. Sabine Van Huffel, Department of
Philips Motiva System
Electrical Engineering, KU Leuven, Belgium
Sharon Donald (Business Development Manager Telehealth
UK and EU Telehealth Market Analysis
UK & Ireland), Philips Healthcare, UK
Nicholas Offer (SBRI Project Manager) & Stuart Thomson (Head
Doc@Home System
of Medical Technology), Health Enterprise East, UK
Adrian Flowerday (Chief Executive) Docobo, UK
RECAP Telehealth Project - The Patient Perspective
RECAP Telehealth Integration into Community Care
Michelle Stapleton (Integrated Care Director) & Jan Minter (Nurse
Michelle Stapleton (Integrated Care Director), Jan Minter
Consultant, Lead for Community Heart Failure) North East London
(Nurse Consultant, Lead for Community Heart Failure) North
NHS Foundation Trust (NELFT), UK
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East London NHS Foundation Trust (NELFT), UK