Obesense - Nano-Tera 2015

ObeSense Monitoring the Consequences of Obesity

Motivations

Obesity is associated with multiple health problems

Cardiovascular diseases

Atrial fibrillation

Hypertension

Obstructive sleep apnea

Diabetes

Certain types of cancer

Has been proven to reduce life expectancy

10% of premature adult deaths

Is reaching epidemic proportions

i. e. Switzerland: 48.7% overweight, 8.3% obese

7.3% of the total healthcare expenses

Guidelines about identification, evaluation and treatment exist

Those guidelines require long-term monitoring

Such monitoring systems do not exist

2

Objectives

Answer a clear medical need by joining research in

physiological markers sensors with clinical end-users

Develop innovative and non-invasive sensors.

Integrate them into single long-term monitoring systems adapted

to obese patients.

• Multi-parametric, low-power, allergy-free, comfortable, with online feedback.

Sophisticated software and algorithms.

Central involvement of end-users.

• Through 3 clinical scenarios

3

Monitoring system 4

Mo

nit

ori

ng

sys

tem

WP1:

respiratory rate and volume Flexible optical fibers

WP2:

cardiac output

Electrical Impedance Tomograohy

WP3:

energy expenditure NIRS

Anaerobic threshold WP4:

blood pressure

ICG, ECG, PPG WP5:

ECG T-shirt

Textile based ECG electrodes

WP6:

wireless body sensor network

WP7:

ECG analysis

Monitoring system 5

Mo

nit

ori

ng

sys

tem

WP1:


WP2:

cardiac output

Electrical Impedance Tomograohy

WP3:



blood pressure

ICG, ECG, PPG WP5:

ECG T-shirt


WP6:


WP7:

ECG analysis

Monitoring system

WP1: Monitoring of respiratory rate and volume

EMPA - CSEM

7

Monitoring system 8

Mo

nit

ori

ng

sys

tem

WP1:


WP2:

cardiac output

Electrical Impedance Tomography

WP3:



blood pressure

ICG, ECG, PPG WP5:

ECG T-shirt


WP6:


WP7:

ECG analysis

… Monitoring system

WP2: Cardiac output CSEM – EPFL/LTS5 – EPFL/LHTC

9

CO =

5 l/min EIT

feasibility of measuring cardiac output non-invasively via

electrical impedance tomography (EIT)

EIT

1. S

imu

lati

on

s

2. M

ea

su

rem

en

ts

4D Bio-Impedance Model

In planning…


Monitoring system 11

Mo

nit

ori

ng

sys

tem

WP1:


WP2:

cardiac output


WP3:

energy expenditure

Oxygen consumption by NIRS


blood pressure

ICG, ECG, PPG WP5:

ECG T-shirt


WP6:


WP7:

ECG analysis


WP3: Estimation of energy expenditure Detection of anaerobic threshold (AT)

IRR, CSEM, EPFL-ASPG

12

Respiratory variables recorded from

12 healthy subjects while exercising

incrementally.

BR and VT by ergospirometer,

HR by instrumented t-shirt (CSEM

SEW model).

13 … Monitoring system

…Estimation of energy expenditure

Platform with 3D accelerometer and ECG front-end

almost complete

Front view

Front view with

electronic

components

Back view

… Monitoring system 14

Energy expenditure

estimation based on

acceleration and ECG

compared to indirect

calorimetry.



Fick-based method

USZ

VO2 = 𝑐𝐻𝑏×co× SaO2

– SvO2

𝑘1

VO2: Oxygen consumption (mL/100g/min),

CO: Cardiac output (mL/100g/min),

cHb: Haemoglobin concentration (g/dL).

CO stroke volume × heart beat,

SV = EDV – ESV ≈ 70 𝑚𝐿,

SaO2 pulse oximetry,

SvO2 novel NIRS system.

measured as part

of other WPs



Fick-based method

USZ

16

Stroke volume

Energy expenditure

Respiration rate

Heart beat

Energy expenditure

Heart beat

(beats/min)

Respiration rate



Sensor design and cell-phone/laptop interface

17


Mo

nit

ori

ng

sys

tem

WP1:


WP2:

cardiac output


WP3:



blood pressure

ICG, ECG, PPG WP5:

ECG T-shirt


WP6:


WP7:

ECG analysis


WP4: Blood pressure (BP) CSEM

Estimation of BP based on Pulse Transit Time (PTT).

Non-invasive, continuous measurement based on ICG,

ECG, PPG.

19


Mo

nit

ori

ng

sys

tem

WP1:


WP2:

cardiac output


WP3:



blood pressure

ICG, ECG, PPG WP5:

ECG T-shirt


WP6:


WP7:

ECG analysis


WP5: Smart ECG T-shirts EMPA - CSEM

Textile based ECG electrodes with humidication pad,

Integration into T-shirt and short validation.

21


Mo

nit

ori

ng

sys

tem

WP1:


WP2:

cardiac output


WP3:



blood pressure

ICG, ECG, PPG WP5:

ECG T-shirt


WP6:


WP7:

ECG analysis


Wireless body sensor network

CSEM

23

Embedded architecture for processing of multiple bio-signals and the

integration of signal processing algorithms on the embedded hardware.


Multi-parameter sensing

EPFL - ESL Touch based/wearable:

1-lead ECG

Respiration

Skin conductance

Motion

Body fat and hydration

level

Emotions: mood

(valence/arousal), stress

Real time BT 4.0

communication, open

APIs.


Mo

nit

ori

ng

sys

tem

WP1:


WP2:

cardiac output


WP3:



blood pressure

ICG, ECG, PPG WP5:

ECG T-shirt


WP6:


WP7:

ECG analysis


ECG analysis

EPFL - ASPG

QRS complexes and fiducial points detection in the ECG by

means of mathematical morphology operators in an adaptive

manner.

26

Clinical scenarios

Scenario 1: physical activity & lifestyle

interventions

– Supervised by Dr O. Dériaz (IRR) and Dr U. Mäder

(SFISM) on patients following activity regimen in lab

settings and at home

Scenario 2: hospitalization monitoring

– Obesity and atrial fibrillation, hypertension and type-

2 diabetes

– Supervised by Dr E. Pruvot (CHUV)

Scenario 3: ambulatory monitoring

– Obesity and outpatient cardiovascular complications

– Supervised by Dr E. Pruvot (CHUV)

27

Documents

Obesense - Nano-Tera 2015