A Model-Based Approach for

Realizing a Safe Wireless

Biotelemetry System

Kerron Duncan

Advisor: Dr. Ralph Etienne-Cummings

Johns Hopkins University

Department of Electrical and Computer Engineering

PhD candidate, John Hopkins University

OverviewA Model-Based Framework for Trade Space Exploration

• Development of a wireless biotelemetry communications system model thatenables early architecture trades and decision making for safety, performanceand power efficiency.

• This integrated system model is constructed using a model-based systemsengineering (MBSE) framework which enables the digital integration ofrequirements, architecture and disparate performance models to determinecandidate solutions for an implanted communication system.

• In the conceptual and early design phases of a biotelemetry system one mustconsider the constraints of implanted wireless devices in humans and animals,as well as those imposed on short range, high data rate wirelesscommunications.

• The integrated model greatly improves the ability of the designer to quicklyanalyze thousands of system permutations while considering systemrequirements.

2

What Is Biotelemetry?

• Measuring, recording and monitoring of real time physiological

parameters such as ECG, EOG, EMG and neural spikes, over

some distance via radio-frequency signals

• Why is it important ?

– Health Care, Neuroscience & Brain Machine Interfaces

• Why implant ?

– Reduce infections

– Higher fidelity3

Rx

Medical Care Advances

Family

Doctors

Router/Receiver

Implanted

Transmitter

Cloud

Un-tethered

Neuroscience

Experiments(eliminates wires, etc.)

Source: Guevremont et al, Neurophysiology 2007

Wireless Biotelemetry Standards

• Wireless Medical Telemetry Services (WMTS)

– On-Body Devices: Transmits at periodic intervals, short duration, low power consumption

– 608-614 MHz, 1395-1400 MHz & 1427-1432 MHz

• MedRadio ( was Medical Implant Communications Service (MICS) )

– In-Body Devices: Propagation characteristics conducive to transmission of RF through the body

– Little to no interference with other RF in band, compatible internationally

– Bandwidth: 401-406 MHz

• Industrial, Scientific & Medical (ISM)

– In-Body Devices: Used in conjunction with MedRadio devices, band differs internationally slightly

– Bandwidth : 433 MHz, 2.4-2.48 GHz, 5.725 GHz

• Ultra-wideband (UWB)

– In-Body and On-body devices

– Higher frequencies allow smaller antenna designs, higher data rates, but more losses

– Bandwidth : 3.1 – 10.6 GHz.

4

Wireless Standard Comparison Leads us to UWB

• UWB allows

– Wide bandwidth leads to higher data rates

– Low EIRP requirements which leads to low power consumption and dissipation

– Higher operational frequency leads to smaller possible antenna designs for ease of implantation

– Small penetration depth leads to short range comm. or implanted a few mm from skin surface but acceptable for most applications

5

Wireless Standard Bandwidth EIRP Wavelength (m)

Penetration

Depth (mm)MedRadio 5 MHz 25 uW 0.743 52

ISM 80 MHz 4 W 0.123 22.4

UWB 7500 MHz 556 uW 0.044 6

EIRP:

Equivalent Isotropic

Radiated Power

What is Ultra-wideband (UWB)?

6

(FCC Spectral Mask)

Source: Wang, EMC 2007

BW

of interest

Gaussian derivatives are used-41.3 dBm/MHz

or 556 uW

5th derivative Gaussian fits UWB spectral mask

7

UWB allows full occupation or sub-bands

Multiband UWB

Uses Sub-bands

IR-UWB signal

shaped for BW

Source: Gharpurey, UWB Circuits, Transceivers and Systems

The “Hostile” Implanted Environment Has Been Studied

81 2 3 4 5 6 7 8 9 10

x 109

0

10

20

30

40

50

60

70

Frequency (Hz)

Rela

tive P

erm

ittivity

Relative Permittivity vs. Frequency for Biological Materials

wet skin

dry skin

fat

grey matter

white matter

blood

cancellous bone

cortical bone

bone marrow

muscle

High Permittivity

(dielectric “constant”)

Conductivity

402 MHz Data

UWB Range Data

• We must understand the electrical characteristics

of biological material as they vary over frequency

• Dispersive

• High εr (muscle ~50, air is 1 )

• Conductive (blood ~ 1.35, sea water is 4.8)

Source: Jaehoon, MTT 2004Source: Gabriel, Phys Med Biol. 1996

Finite Element Analysis Tools Used To Model Implanted

Environ.

9

Source:www.cst.com

• Industry standards are

• CST Microwave

• ANSYS HFSS

• Model antenna and implanted environment in 3-D

• Accurately models dispersive material

• Plot far/near field fields, S-parameters

• Post process SAR and Temp Rise

• Parameterized and Optimize Structures

• Requires minimal to significant computing power

• Expensive

CST Microwave Voxel

Human Model

Used in our research

E-field

SAR

Temperature

Device

Recent SAR Study IR-UWB Implanted In Head Model

10Source: Yuce, et al. MTT, 2013

CST Microwave Head Model w/ Radiating Fields

SAR From Voxel Model of Adult Human Head, SAR < 2 W/kg limit

Power Limits Increased main lobe direction 180˚

SAR Study on MICS & ISM for Retinal Prosthesis

11Source: Permana, et al. IET Microwaves, Antennas and Propagation Journal, 2013

CST Microwave Head Model

Head Model Tissue Stack-up

Antenna Design 2.4 GHz

Units in mm

SAR (W/kg) vs Input Power

Input Power

Calculated SAR

2.4 GHz has worst SAR in this study

2.4 GHz

Med Radio

SAR Margin

12

Biotelemetry

System

Modeling

System Block Diagram

13

PostProcessing

SignalAcquisition

DataModulation

Detection & De-

Modulation

Environment

Data Amplifier AmplifierRetrieved

Data

Transmitter Receiver

Acquires

Processes

Modulates

Amplifies

Transmits

Receives

Amplifies

Detects

Demodulates

Completes

Tissue

System Link Budget

14

Signal

Strength

(dBm)

Communications Path

Receiver

Sensitivity

Received

Signal

Rx

System Margin

Modulated

DataDe-Modulated

Data

2

4

dGGPP wb

rxtxtxrx

Signal link budget calculates

the power, gain and noise

figure at each step in the

system for transmit and

receive.

In Watts

Receive

Power

Transmit

PowerTransmit

GainReceive

Gain DistanceFree Space

Path Loss

Wide-Band

WavelengthModified Friis equation for

wideband signals computes the

received power at some

distance (d) between a

transmitter and receiver. Uses

the geometric mean of the

upper and lower frequencies.

Figures of Merit (FOM) include:• Equivalent Radiated Power (ERP)

• Sensitivity

• Signal-to-Noise Ratio (SNR)

• Specific Absorption Rate (SAR)

SAR

SNR

NF

ERP

System Performance Model – 1st Principles ApproachExpanded Friis Equation Including Statistical Variation

15

2

4

dGGPP wb

rxtxtxrx

Empirical Approach To Path Loss Modeling is Used

• Generally accepted path loss model (log-distance)

– Average receive signal power decreases logarithmically w/ distance

– Probability distribution function incorporates path loss measurements

16

Xd

dndPLdBPL

o

o

log10)()(

Reference Path Loss at distance, do

Path Loss Exponent

Distance-dependent mean

CDF of scattering within human chest fits zero-mean Gaussian

Path loss measurements within human chest fits log-distance eq.

Shadowing Effect R.V. (in dB)

Source: Khaleghi, et al. IET Journal, 2010

System Performance Model – 1st Principles ApproachExpanded Friis Equation Including Statistical Variation

17

2

4

dGGPP wb

rxtxtxrx

Xd

dn

d

cGGPP

o

dBrxdBtxdBtxdBrxdBin

10

0

10 log104

log20

System Safety Model – 1st Principles ApproachSpecific Absorption Rate

18

2E

dV

dW

dt

d

dm

dW

dt

dSAR

Product of the tissue conductivity (S/m) and electric field (V/m)

squared divided by the tissue density (kg/m3)

Average SAR (in W/kg) (or whole-body average SAR) is defined as the ratio of the total absorbed power in the exposed body to its mass where the local SAR refers to the absorbed energy value per unit volume or mass, which can be arbitrarily small

System Performance Model – 1st Principles ApproachReceiver Sensitivity & Data Modulation

19

SNRNFkTBdBmS )(min

BW

R

N

ESNR b

o

b

BWTkkTB

Sensitivity is the minimum detectable

signal of the receiver based on a given

bit error rate (BER)

Signal-to-Noise Ratio (SNR) of the receiver is a

function of the system modulation scheme and BER.

The noise power of the receiver is a function of

temperature (T) and Bandwidth (BW).

System Summary

20

System Model Architecture

21

System & Safety

Requirements

Integrated System Performance & Safety Model

Receiver (Ant, Amp)

Gtx

SAR

Ptx

n

σ

PLd0

Grx

NF

kTBSNR

Database of Key

Component

Characteristics (Model

or Measured)

Implanted Antenna

(Transmitter)

Environment Effects

Data Modulation

Database of Key

System Variables

& Parameters

comparisonTrade Space Visualization

& Analysis

Frequency

Bandwidth

Pin

Data Rate

SAR

Received Power

Sensitivity

ERP

Minimum Comm. Distance

Specific Absorption Rate

Equivalent Radiated Power

System Performance & Safety Roll-Up for Visualization & Analysis

Decision Makers

• Apply Model-Based Systems

Engineering (MBSE)

descriptive modeling

concepts using the systems

modeling language (sysML)

• Link descriptive and

analytical models using

IBM’s Rhapsody, PTC’s

Integrity Modeler and

ModelCenter®

22

Application of Model-Based Systems Engineering

Use Cases

Reqm’ts

Block

Definition

Diagrams

Parametric

Diagrams

System ModelingImplanted Transmit Antenna Model

23

1Determine System

& Safety

Requirements

Design & Optimization

of Implanted Antenna

using FEA

Sweep Frequency

Sweep Input Power

Add Tissue Stack-Up & Frequency Dependent

Characteristics

S11

f

2

3

Minimize Return Loss

S11(dB)

Across Band

Duncan, ISCAS 2017

System ModelingImplanted Transmit Antenna Model

24

1Determine System

& Safety

Requirements

Design & Optimization

of Implanted Antenna

using FEA

Gtx(f)

f

ηrad

Pin

S11

f

SAR(f)

fSAR(Pin)

Pin

Antenna Performance

and Safety as a function

of frequency (f) and input

power (Pin)

Ptx(Pin)

Pin

2

3

4a

4b

Minimize Return Loss

S11(dB)

Across Band

Multivariate Performance and Safety Results from FEA Simulations

Sweep Frequency

Sweep Input Power

Add Tissue Stack-Up & Frequency Dependent

Characteristics

25

Gtx(f)

f

ηrad

Pin

SAR(f)

fSAR(Pin)

Pin

Ptx(Pin)

Pin

System ModelingImplanted Transmit Antenna Model

Antenna Performance

and Safety as a function

of frequency (f) and input

power (Pin)

MATLAB model contains all the data and

interdependencies between the various inputs

and outputs of the transmit antenna model

Antenna

Performance

& Safety Database

),(

,,

frequencyPinf

SARGtxERP

*Could also include f(location, implant distance)

*

System ModelingEnvironment Model

26

Determine System

& Safety

Requirements

Non Line-of-Sight

Line-of-Sight

Sweep Frequency

Variability of the Environment

Path Loss as a

function of

frequency (f) and

environment

(NLOS, LOS)

)max,min,,/( distffNLOSLOSf

LossPath

LOS

NLOS

Assuming 10.6 – 3.1 GHz Bandwidth

Assuming 10.6 – 3.1 GHz Bandwidth

Assuming LOS Environment

3.6 – 3.1 GHz

10.6 – 3.1 GHz

27

Empirical Model

from Published

Works

Measured /

Modeled Database

Integrated Performance & Safety Model Modular and Extensible

Database of Key

Component

Characteristics

(Model or

Measured)

System & Safety

Requirements (ERP,

SAR, Distance)

System Parameters

(Frequency,

Temperature, …)

Analytical Models

Modelcenter® Integration Environment Used to Link Models

28

Model links and interdependencies

System Performance & Safety Calculation

Integrated Performance & Safety Model Parameters Linked w/ Interdependency

System & Safety

Requirements (ERP,

SAR, Distance)

System Parameters

(Frequency,

Temperature, …)

Modelcenter® Integration Environment Used to Link Models

29

Model links and interdependencies

System Performance & Safety Calculation

Integrated Performance & Safety Model Trade Space Exploration Enabled

Parallel Coordinates

J. Johansson and C. Forsell, "Evaluation of Parallel Coordinates: Overview, Categorization and Guidelines for Future Research," in IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 1, pp. 579-588, Jan. 31 2016.

Design of Experiments

Scatter Plots

T. H. Huang, M. L. Huang and K. Zhang, "An Interactive Scatter Plot Metrics Visualization for Decision Trend Analysis," 2012 11th International Conference on Machine Learning and Applications, Boca Raton, FL, 2012, pp. 258-264.

Various Approaches Exist for Multivariable Visualization & Analysis

30

Parametric Diagrams Developed of Each Module in Rhapsody

31

Parametric Diagrams Developed of Each Module in RhapsodyReceiver Noise Power Example (kTB) Linked to Model Center

Integrated Performance & Safety Model Descriptive Integrated to Analytical Models

32

• Varied

– Maximum frequency from 3.6 GHz to 10.6 GHz (500 – 7500 MHz)

– Input power to transmit antenna from 1e-5 to 1e-3 mW

– Transmit to Receive distance from 1 to 10 meters

• Analyzed SAR, ERP, Prx, Sensitivity

• Ran 6000 design of experiments (DOE) – 1 ½ hours

• Used Latin hypercube sampling (LHS)

– A statistical method for generating a near-random sample of parameter values

from a multidimensional distribution. -Wikipedia

33

Integrated Performance & Safety Model Trade Space Exploration Setup

34

Receiver Sensitivity (dBm) increases (receiver is less

sensitive) as bandwidth increases (500 MHz to 7500 MHz)

Integrated Performance & Safety Model Trade Space Analysis - Sensitivity

Smin = -62 dBm

Bandwidth (MHz)

Smin = -73.8 dBm

Se

ns

itiv

ity (

dB

m)

Sensitivity vs. Bandwidth

35

Integrated Performance & Safety Model Trade Space Analysis – Received Power

Received Power vs Distance

Distance (meters)

Prx trend

Sensitivity vs.

Bandwidth

10 m1 m

Re

ce

ive

d P

ow

er

(dB

m)

36

Integrated Performance & Safety Model Trade Space Analysis – Received Power (Apply Constraints)

Received Power vs Distance

Smin = -73.8 dBm

@ 500 MHz

Prx trend

Model shows

minimum transmit

distance < 10 meters

10 m1 m 5 m

Re

ce

ive

d P

ow

er

(dB

m)

Distance (meters)

37

Integrated Performance & Safety Model Trade Space Analysis – Received Power (Apply Constraints)

Received Power vs Distance

Re

ce

ive

d P

ow

er

(dB

m)

Prx vs Distance trend

Model shows

minimum transmit

distance < 5 meters

1 m

Smin = -62 dBm

& 7500 MHz

5 m 10 m

Distance (meters)

38

Integrated Performance & Safety Model Trade Space Analysis – ERP

ERP is visualized to

show its dependency

on bandwidth and

input power

ERP vs Input Power

Input Power (mW)

ER

P (

dB

m)

39

Integrated Performance & Safety Model Trade Space Analysis – Specific Absorption Rate

SAR vs Input Power

Input Power (mW)

SA

R (

W/k

g)

SAR is visualized to

show its dependency

on bandwidth and

input power

40

Integrated Performance & Safety Model Trade Space Analysis – SAR (Apply Constraints)

SAR vs Input Power

Input Power (mW)

SA

R (

W/k

g)

Prx > Smin= -73.82 dBm @ 500 MHz

Constraints on the received

power affects SAR

41

Integrated Performance & Safety Model Trade Space Analysis – SAR (Apply Constraints)

SAR vs Input Power

Input Power (mW)

SA

R (

W/k

g)

Prx > Smin= -62 dBm @ 7500 MHz

Constraints on the received

power affects SAR

42

Integrated Performance & Safety Model Trade Space Analysis – SAR (Apply Constraints)

SAR vs Input Power

Input Power (mW)

SA

R (

W/k

g)

Prx > Smin= -62 dBm @ 7500 MHz

SAR < 1.6 W/kg

Apply the SAR requirement

of < 1.6 W/kg

43

Integrated Performance & Safety Model Trade Space Analysis – SAR (Apply Constraints)

SAR vs Input Power

Input Power (mW)

SA

R (

W/k

g)

Prx > Smin= -62 dBm @ 7500 MHz

SAR < 1.6 W/kg

Pin < 2.38 mW

Determine the max input

power to meet SAR

requirement

Apply the SAR requirement

of < 1.6 W/kg

Integrated Performance & Safety Model Trade Space Visualization & Analysis

Parallel Coordinates

DOE Cube

Carpet Plots

Scatter Matrix

• A decision support tool has been developed to support a broad trade space

of implanted wireless communication system permutations using MBSE

• Allows the user to simulate numerous alternatives early in the system

lifecycle

• Leverages system requirements and constraints to prune and explore the

results to determine the best solutions for the system

• Enables the use of models or data to inform the user such as to consider

what-if analysis

45

Conclusion

Summary & Future WorkComparison of Published Implant Positions vs Performance

Implanted Antenna

(Transmitter)

• Leverage published UWB implant

data and locations

• Add double-hope system trades

• Add thermal effects on modeled

performance

• Add power efficiency calculation for

implanted system-on-chip

• Add implant size as a constraint

Receiver (Ant, Amp)

)()( bTTBASARTkt

TpC

Brain

Neck

Body Cavity

Arm

47

Thank You!

Questions?