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nano-tera.ch Body Powered SenSE Zero Power Medical Devices

Annual Plenary Meeting Bern, May 4th, 2015

P.-A. Farine, J. Gutknecht, T. Gross, C. Hierold, D. Atienza, D. Briand, M. Knyazeva, G. Wohlrab Milad Ataei, PhD student, ESPLAB, EPFL

Introduction

•  Patient’s physiological state requires long-term monitoring in order to precise a diagnosis or to evaluate the efficacy of an on-going treatment. •  Today’s wearable solutions invade the user’s normal life as sensing platforms have batteries issues including size, weight, operating lifetime or convenience.

BodyPoweredSense:

L’EXPRESS -‐ L’IMPARTIAL MERCREDI 17 JUILLET 2013

o  Improve Health Care through Smart, Convenient Wearable Sensors o  Advance Human Energy Harvesting towards a “fit and forget” and “no recharge” goal. o  Target Energy Optimization and Usage o  Apply the technology in Real and Demanding Clinical Use cases, ensuring fitness for purpose o  Involve Industry in multi-disciplinary IT research

Convenient optimized system in Energy and Monitoring functionality from low levels to high levels 04/06/15

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BodyPoweredSense –Partitioning and Challenges Amplified and digitized E EG and ECG signals

Energy Harvesters

Power Supply

Personal Environmental monitoring powered by solar cells 04/06/15

ADC

-‐ Optimize the methods of analysis for long-‐term low-‐ density EEG

Energy info

Integrated Circuit ESPLAB

Energy

-‐ Development and o ptimization of compliant nano-‐composite piezoelectric materials

AC/DC Converter

Sensor Data

MNS LAB

DC/DC Converter

Amp

-‐ Optimized output power -‐ A lightweight, compact and comfortable wearable system

-‐ Modelling and predication of energy harvesting -‐ Energy-‐aware runtime for smart medical sensors

Low Power Processor unit

µC DSP

Apply the technology in Real and Demanding Clinical Use cases -‐ EEG recording and data analysis for Epilepsy in children -‐ Advice on hospital best practice

Self Optimising Firmware maximising the results/energy ratio

Optimised Power Conversion from the Energy Harvesting (EH) sources to storage

-‐ Power converter efficiency -‐ Limited sizes -‐ Ultra-‐low power design

-‐ Energy-‐ efficient a nd real-‐time multi-‐ lead biosignal analysis

Low Power Processor unit

µC Low power

RAM

-‐ Provide a complete medical Human Centered Design -‐ Provide medical processing platform

Low Density EEG

-‐ Support data capture trials

PFIM

Epilepsy in children (KISPI), Alzheimer’s disease in the elderly (CHUV) NanoTera BodyPoweredSense

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Thermoelectric energy harvesting of human body heat, MNS Local energy harves/ng and conversion Heat sink TEG Heat transfer

Electronics IsolaFon Electrode 12 mm

•  Autonomous sensor nodes •  Biopoten/al readout 04/06/15

Thielen, et al. ICT 2014

Large-‐area harves/ng (20 cm2)

Electronics Reference electrode

Heat sinks Thielen, et al. NT Annual MeeFng 2014

•  Data processing •  Wireless communica/on

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EEG electrode for application in hair with TEG, MNS Current status: -‐  Prototyping -‐  TesFng of new TEGs

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Heat sink Stacked TEG Thermal insulaFon Heat transfer structure AgCl pins

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Compliant piezoelectric harvesting band, SAMLAB

q  Synthesis of homogeneous piezoelectric nanocomposites via stabilized nanoparFcle suspensions. q  UFlizaFon of large area fabricaFon methods: Inkjet prinFng for electrode deposiFon, bar casFng for acFve layer deposiFon. Piezoelectric NPs / PDMS Piezoelectric Composite Material opFmizaFon

•  •  •

Ball milled BaTiO3 NPs Carbon ﬁller (CNT, CB) PDMS, SU-‐8

Milled and ﬁltered BaTiO3 a0er 24hours

Nanocomposite (PDMS/BaTiO3/CNT)

NP/CNT/PDMS mixture Au/Ti Nanocomposite (PDMS/BaTiO3/CNT)

Au/Ti

PDMS dielectric

Kapton

Kapton Measured Piezoelectric Constant: 5 pC/N McCall et al. (2014): > 100 pC/N

04/06/15

0.1 µW / cm2 (x 1000 cm2) = 0.1 mW

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10.7mm

T op view

9.2mm

R equired C omponents

D esigned P C B with a small form factor

Energy conversion implementation, ESPLAB

E E G/ E C G A ctive E lectrode

H eat sink T E G power converter and E E G signal conditioning T E G transducer

O .C . O utput T E G V oltage of output T EG Impedance T hermal 1 20-‐40mV 2.5Ω T hermal 2 80-‐200mV 182Ω T hermal 3 100-‐400mV 50Ω 04/06/15

B ottom view

S witches

P ulse Generators

C ontrol S ections

260µm

O scillator

T E G A vailable P ower 40-‐160µW 8.8-‐54µW 50-‐800µW

P ower needed for amplifier 5µW 5µW 5µW

NanoTera BodyPoweredSense

W eight= 2.8g

530µm

T hermal isolator ring T hermal contact E E G electrode 12mm

M easured E fficiency U sability for O utput P ower of system load of system 22-‐128µW 54% OK 5.3-‐44µW 60-‐80% OK 35-‐458µW 58-‐70% OK 7/16

Energy conversion design and measurements, ESPLAB

Challenges: §  Harvesters produce small power at low voltages, processing are power hungry: Power converter efficiency §  It will fit in size of an electrode: Integrated circuit with limited passive sizes To realize a high efficient system here, 4 criteria have to keep in mind: 1- Ultra low power blocks in circuit level

C arlson, B andyopadhyay S hrivastava, T his work J S S C 2010 , J S S C 2012 J S S C 2015 measurement

2- Minimizing loss 3- Limited form factor

T opology

Inductor based

4- Maximizing power transfer

Inductor based

T echnology

0.13µm

0.35µm

0.13µm

0.18µm

V oltage C onversion

20mV ~ 100 ~ 100mV to 2V mV to 1V

20mV ~ 300 mV to 1V

~ 10mV to 0.9V

O utput P ower

25µW @20mV

1.3mW @100mV

-‐

22µW @20mV

Q uiescent P ower

1.3µW

-‐

0.3µW

1.6µW

ɳ at 20mV

46%

40%

21%

54%

ɳ at 100mV

68%

65%

68%

71%

C ore area

0.12mm 2

2.5mm 2

0.12mm 2

0.14mm 2

04/06/15

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Demonstrator, LST

•  Will demonstrate •  TEG and Solar energy harvesFng •  Modeling and PredicFon of energy harvesFng •  EEG recording and seizure predicFon using energy aware run Fme

Final version with ESPLAB’s chip

ACTUAL PCBs of Processor Unit and AcFve electrode 04/06/15

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Real-world testing, MNS and LST

First feedback: -‐  Improvements on wire connec/ons needed for kids -‐  Results vary strongly for diﬀerent subjects à StaFsFcs needed

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Smart WBSN concept for ECG, ESL Smart Node

Displays the received data and relays to medical personnel

ECG

Noise ﬁltering

ECG delineaFon

ECG Analysis (arrhythmia)

ECG-‐aware compression Energy-‐eﬃcient and real-‐Fme mulF-‐lead ECG biosignal analysis (1)  (2)  (3)  (4)

Low-‐complexity ﬁltering methods using integer compuFng and bio-‐signal operators Limited-‐memory methods to dynamically adapt ECG thresholds for each person Low-‐energy mulF-‐lead ECG arrhythmia analysis in real-‐Fme Energy-‐eﬃcient communicaFon. storage exploiFng biosignal-‐adapted compression

Automated ECG diagnosis

DetecFon of normal beats (N), premature ventricular contracFon (V), led bundle branch block (L), right bundle branch block (R), and atrial premature beats (A)

ClassiﬁcaFon by majority vo/ng

Baeery of n(n-‐1)/2 one-‐vs-‐one classiﬁers Neuro fuzzy classiﬁer accuracy: ~ 84% Support vector machine accuracy: ~ 91% 04/06/15

Filtering

Lead 1 Delineation

NanoTera BodyPoweredSense

Filtered ECG DWT Transform Fiducial Points

Random Projection Random Projection

Classification

Diagnosis (‘N’, ‘V’, ‘L’, ‘R’, ‘A’) 11/16

High- to low-density EEG monitoring

Ø  Goal: To OpFmize methods for monitoring of some pathological condiFons with low density EEG. Ø  Method: To use funcFonal connecFvity based on source analysis of EEG. Ø  Steps: To adjust these methods from high density to low density EEG. Step 1

Func/onal connec/vity based on source analysis of high-‐densty EEG: To use an usual analysis of hdEEG.

High Density EEG The diﬀerences of funcFonal connecFvity were observed in PNES paFents vs. healthy controls [1].

[1] Barzegaran, E., Knyazeva, M., JNNP, accepted, 2015. 04/06/15

The result of funcFonal connecFvity analysis, using corFcal parFal coherence, were diﬀerent with diﬀerent EEG densiFes.

110 sensors 61 sensors 18 sensors

Step 2

Func/onal connec/vity based on cor/cal par/al coherence analysis of high-‐densty and low-‐ densty EEG: To use a method designed to work with high and low density EEG.

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Step 3

validate cor/cal par/al coherence method for ldEEG using simulated data: To simulate EEG data with diﬀerent locaFons of acFve sources in the brain, and evaluate the method for esFmaFon of sources

Low Density EEG

This Method performs accurately on simulated data when the locaFon of sources are close to the electrodes.

This method does not perform accurately when the locaFon of sources are not close to the electrodes. 12/16

EEG signal analysis

•  Supported partners with EEG signal capture of BPS test subjects •  CollaboraFon with ETH-‐NSG with Real recording experience and data analysis for the early detecFon of epilepsy •  Provided ESPLAB with training data for good and bad electrodes analysis •  Provided advice on hospital procedures and best pracFce •  Provided signal to noise data to ESPLAB for circuit design consideraFons

04/06/15

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Industrial partners •

PFIM, Support to clinical approach and to data capture trials , providing children as test subjects and clinical aeendance

•

ByElement, has provide a complete medical HCD Engineering consisFng of several developer workshops with medical experts, applying HCD. Methodology, idenFﬁcaFon of criFcal acceptance elements, idenFﬁcaFon and descripFon of medical usage scenarios, including device acceptance dimensions.

Children: Wearable Energy HarvesFng Measurement and EEG capture

Focus group meeFng ETH Zurich

•

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Human Centred Design in pracFce

EXPLORIS, collaboraFng with ETH-‐NSG and with CHUV provide medical processing plaoorm NanoTera BodyPoweredSense

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System Energy Budgets EEG Sensor

EsFmated Power Consump9on 1.5mW to 3mW, over 24 hours 44.5mWH for 64 electrodes EsFmated Energy Harves9ng levels: TEG 72 cm2 1.2mW, Solar 5 Cm2 75mWH, esFmated total circa 100mWH

Available Power: 4mW Converted power: 2mW

ECG Sensor

Memory

EsFmated Power Consump9on 0.5mW to 1 mW , 24mWH EsFmated Energy Harves9ng levels: 9 cm2 TEG 0.2mW, Piezo 1000 Cm2 1mW, esFmated total circa 30mWH

Electrodes uC Amplifier ADC Data flow Raw data size 24h uC frequency uC Consumption Sub total (uC+A+ADC) Energy per day Flash storage e nergy Total per day 04/06/15

# # uW uW kbps Gb MHz uW mW mWh mWh mWh

1 -‐ 5 10 1.2 0.1

16 1 80 160 19.2 1.7 1 400 0.6 15.4 0.6 15.9

32 1 160 320 38.4 3.3 1 400 0.9 21.1 1.2 22.3

64 2 320 640 76.8 6.6 1 800 1.8 42.2 2.3 44.5

128 4 640 1280 153.6 13.3 1 1600 3.5 84.5 4.6 89.1

NanoTera BodyPoweredSense

ADC

Amp

MSP430FR5739 uC active power Flash memory Flash memory

uW/MHz

400

MB/J Gb/mWh

100 2.88 15/16

END OF THE PRESENTATION

Conclusion Smart Node

530µm

ECG

P ulse Generators

Moritz Thielen MNS, ETHZ

S witches

Christopher Borsa SAMLAB, EPFL

C ontrol S ections

260µm

O scillator

Milad Ataei ESPLAB, EPFL

Francisco Rincon ESL, EPFL

Ivana Unkovic Elham Barzegaran LST, ETHZ CHUV

Thank you for your attention 04/06/15

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