“Santé  connectée”

ESA  –  CMI  /M2    BioSan  

Systèmes  embarqués  pour  la  Santé  

“Santé  connectée”  

Aymeric Histace

Professeur des Universités, ENSEA ^TOMORROW

YESTERDAY

Aymeric  Histace   2   Head: Pr. Mathias QUOY (UCP)

Co-Head: Pr. David DECLERCQ

14 Full Professors 29 Associate Professors

2 Full Researchers 3 Research Engineers

2 Engineers

1 Administrative Sec.

~ 20 Post-Doc

~ 30 PhD students Several MSc students

(internship)

Aymeric  Histace   3  

ICI MIDI Neuro

Information, Communication,

Imagery

Indexation et Masse de

Données Multimédia

Artifical Intelligence and Robotics

ASTRE

Electronics, Reconfigurable Computing, Image

Processing

Teams

ASTRE

So#ware  Radio   Reconfigurable  

Compu6ng  

Smart  Embedded   Systems  for  Health   Circuits  for  

Communicaions  

Smart A3, Fiability

NoC, LNA

Signal Processing

Sensors Network

Computer Vision

Embedded Systems

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SES

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Electronics (Analog, Digital)

Biology, Physiology Signal/Image

Processing

3D Confocal microscopy image analysis

Smart Autonomous Medical Device

Bioimpedance characterization

Videocapsule

SES

ECG Analysis

Spinal Implant CAD

Summary  

•   ETIS  

•  «Santé  connectée  »:  Context  and  Challenges  

•  Examples  Of  Ongoing  Projects  

•  The  SES@ENSEA  PlaQorm  

Aymeric  Histace   6  

Summary  

•   ETIS  

•  «Santé  connectée  »:  Context  and  Challenges  

•  Examples  Of  Ongoing  Projects  

•  The  SES@ENSEA  PlaQorm  

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Santé  connectée:  Context

Aymeric  Histace  

‘Medical’ Devices Secure Storage/Access Computer-Aided Diagnosis 8  

Connected Stent

Medical  Devices?

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Toys or

not toys ?

Medical  Devices?

WEI et al. Front. Energy Power Eng. China 2008

Autonomous Medical Devices

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Medical  Devices?

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Today:

Leadless pacemaker

Secured  Storage?

Aymeric  Histace  

•   France:  loi  n°2002-­‐303  du  4  mars  2002  

•  Objec6ves:    

-­‐  Confiden6ality,  integrity,  disponibility  of  the  data  

12  

« L’hébergeur agréé sera l’entité, personne physique ou morale, qui répondra de la conformité de l’opération globale

d’hébergement au regard des exigences de la loi »

Data must be stored in a country from EU except in particular motivated cases

CAD  in  2016?  

From  Nano  to  Macro

To understand manifestations of a same pathologic phenomenon from cell to organ scales (even nano) and draw some possible connections

Imaging Technic Signal and Image Processing Electronic Biology

Mathematics

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 Scien_fic  Challenges

•   Diagnosis  Capabili6es  

•  Biocompa6bility  

•  Energy  

•   Fiability  

•  The  Smaller,  the  BePer  

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Mul_disciplinarity

Embedded Computer- Aided Diagnosis

In Situ Diagnosis CAD

e-diagnosis Understanding

of living mechanisms

Embedded Systems

Medecine - Biology

Signal and Image Processing

Data Management

INTERACTION

Diagnosis Capabilities

Biocompatibility Low Power

Fiability

The Smaller, the Better

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Posi_onnings

Smart-Aided Diagnosis Full - CAD

Acquisition

Processing

Smart Visualisation

Storage

Intelligence Processing time

Embedded Acquisition

Smaller and Smaller Energy Harvesting Low Power

Emission (RF 66%)

Fully embedded Limited or No

Intelligence

‘Only emit what is important’

SES

Intelligence/Energy Compromise

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Summary  

•   ETIS  

•  «Santé  connectée  »  Context  and  Challenges  

•  Examples  Of  Ongoing  Projects  

•  FibroSES  

•  Smart  Videocapsule  

•  SmartEEG  

•   The  SES@ENSEA  PlaQorm  

Aymeric  Histace   17  

Fibrosis  Induced  by  Implant  

A  Common  Issue

18  

20/10/2016   Aymeric  Histace  

After 5 years T0

Pacemaker

State of the art

Fibrosis  Induced  by  Implant  

A  Common  Issue

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Bioimpedance : Cells anf Frequency

Fibrosis  Induced  by  Implant  

A  Common  Issue

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20/10/2016  

Bioimpedance

Z = R + 1 Q( j ω ⁾

Model:

Complex  impedance  spectroscopy  for  monitoring  6ssue  responses   to  inserted  neural  implants  

Fibrosis  Induced  by  Implant  

A  Common  Issue

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20/10/2016  

State of the art : Electrical Techniques

Fibrosis  Induced  by  Implant  

A  Common  Problema_c  

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14/12/16  

State of the art : ECIS (Electric Cell-substrate Impedance Sensing

Fibrosis  Induced  by  Implant  

A  Common  Problema_c  

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Bioimpedance : The Cole Model

Fibrosis  Induced  by  Implant  

A  Common  Issue

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20/10/2016  

Embedded System

Fibrosis  Induced  by  Implant  

A  Common  Issue

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20/10/2016  

Embedded System In Vitro

P₁ P₂

P₃ P₄

Z₂₃ Z₁₄

In Vivo

Fibrosis  Induced  by  Implant  

A  Common  Issue

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20/10/2016  

Home made ECIS aparatus

Fibrosis  Induced  by  Implant  

A  Common  Problema_c  

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Home made ECIS aparatus

Fibrosis  Induced  by  Implant  

A  Common  Problema_c  

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In Vitro First Results

Fibrosis  Induced  by  Implant  

A  Common  Problema_c  

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Perspective and Challenges

Fibrosis  Induced  by  Implant  

A  Common  Problema_c  

•   Modeling  of  the  bioimpedance  electrical  proper6es  

•   Characteriza6on  of  fibrosis  markers  (fibronec6ne,  alpha-­‐

SMA,  Collagen)  

•   Correla6on  In  Vivo-­‐In  Vitro  

•   Embedded  Fibrosis  Regula6on  

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Main idea

To develop next generation of videocolonoscopy with embedded image processing capabilities

In situ detection of intestinal pathologies

Colorectal Cancer

Polyp

Chrone disease

Ulcer

Angioma

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Cyclope  project

Main idea

To develop next generation of videocolonoscopy with embedded image processing capabilities

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Cyclope  project

Cyclope  project

Main idea

To develop next generation of videocolonoscopy with embedded image processing capabilities

Context

Colorectal Cancer

Polyp Advantages

•  Total control

•  Possibility of biopsy's

•  Real-time analysis

Drawbacks

•  Anaesthesia

•  Hospitalization

Advantages

•  Painless

•  No sedation

•  No hospitalization

•  Just swallow it!

Drawbacks

•  Battery life

•  Low resolution

•  No control

•  ~150k images

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Cyclope  project

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A Multispectral WCE

Infrared

(Active Stereo Vision)

Visible

3D feature-based detection

2D feature based detection

3D  in  situ  Analysis

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20/10/2016  

Gray-­‐level  

Image   Thresholding   Labelling  

Feature   extrac_on  

(3D-­‐8)  

Candidate Polyps

Training  

examples   Classifiers  

Laser dot extraction

Learning by SVM

3D  

reconstruc_on  

3D  in  situ  Analysis  :  Data

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20/10/2016   In vitro bench:

•  185 polyps

•  40% for learning and 60% for testing

3D  in  situ  Analysis:  

Implementa_on

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20/10/2016  

Large Scale Demonstrator

SVM implemented

on FPGA platform

3D  in  situ  Analysis  :  Results

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20/10/2016  

Four groups of tested polyps

ROC Curve

Cyclope  project

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A Multispectral WCE

Infrared

(Active Stereo Vision)

Visible

3D feature-based detection

2D feature based detection

2D  Detec_on  

Gray-­‐level  

Image   Edge  

detec_on   Hough  

Transform  

Feature   extrac_on  

(Texture)  

Candidate Polyps

Training  

examples   Classifiers   ROI extraction

Learning by boosting

Co-occurrence matrix (26

features)

Database : 300 Positives

examples, 1200 Negatives ones

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2D  Detec_on

Results and Performance

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2D  Detec_on

Results and Performance (Real Time Tracking)

It is possible to design low complexity detection/recognition

algorithms in accordance with:

(i) embedding constraints, and (ii) expected performance

Sensibility Specificity False Positive Rate

88% 99%

1%

91% 95%

5%

Bernal Our Approach

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Challenges  

Energy

Computation

Size Interaction

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Real-­‐Time  Challenge  

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0   100   200   300   400  

Lenovo  Yoga  2  (47  

ms)   Dell  Precision  (27  

ms)   Raspberry  Pi2  (276  

ms)   Raspberry  Pi3  (131  

ms)   NVIDIA  Jetson  (89  

ms)   Altera  DE1  (344  ms)   Processing  Time  (ms)   Real  Time  Constraint  (40  ms)  

ms

Smart  Systems  for  In  Situ  Diagnosis  

What’s  next  (2020)?

Image processing

Localization

RF

Cellular level

Multispectral Imaging

Physiological data

Nanoparticles Drug Treatment

Control CyberPill

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Main idea

To develop a portable system for synchronized ExG Physiological Signal Acquisition

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SmartEEG  project

FUI 15

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SmartEEG  project

ExG-Video Synchronization

Real-Time Data Compression on Embedded System

E-diagnostic through secured storage

•  EEG,  video,  annota_on,  clinical  data  

Challenges

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SmartEEG  project

QRS Detection, PR and ST

estimation

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Perspec_ve

Check@Flash, StreamVision

Summary  

•   ETIS  

•  «Santé  connectée  »  Context  and  Challenges  

•  Examples  Of  Ongoing  Projects  

•  FibroSES  

•  Cyclope  

•  SmartEEG  

•   The  SES@ENSEA  PlaYorm  

Aymeric  Histace   50  

Aymeric  Histace   51  

SES@ENSEA:  Objec_ves

•  Technological   support  for    SES   projects  

  •  Interface  laboratory/

Companies  

•  Technological   Services  for   Companies  

•  Pedagogical  tool  

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SES@ENSEA

1   2    

3     4    

~60 m ²

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SES@ENSEA:  Equipment

PCB Engraver

3D Printer

(Biocompatible material) 4Ghz

Oscilloscopes with Standard BUS decoder, Jitter, OFDM

Several FPGA dvpt Boards for various

applications (signals, video, etc.)

THANK YOU FOR YOUR ATTENTION

Aymeric  Histace   54  

aymeric.histace@ensea.fr