Channel Models for Medical Implant Communication

Kamran Sayrafian-Pour • Wen-Bin Yang •

John Hagedorn • Judith Terrill •

Kamya Yekeh Yazdandoost • Kiyoshi Hamaguchi

Received: 31 October 2010 / Accepted: 2 November 2010 / Published online: 9 December 2010

Springer Science+Business Media, LLC (outside the USA) 2010

Abstract Information regarding the propagation media is

typically gathered by conducting physical experiments,

measuring and processing the corresponding data to obtain

channel characteristics. When this propagation media is

human body, for example in case of medical implants, then

this approach might not be practical. In this paper, an immersive visualization environment is presented, which is

used as a scientific instrument that gives us the ability to

observe RF propagation from medical implants inside a

human body. This virtual environment allows for more

natural interaction between experts with different backgrounds, such as engineering and medical sciences. Here,

we show how this platform has been used to determine

channel models for medical implant communication

systems.

Keywords Channel model Implant communication

system Immersive visualization system Body area

networks

1 Introduction

Body Area Networks (BAN) consist of RF-enabled wearable and implantable sensory nodes for short range reliable

communication in or around a human body. This technology is poised to be a promising interdisciplinary technology with novel uses in pervasive information technology.

Pacemakers are the oldest and most successful example of

implantable medical devices. Among other applications for

such devices, we can point to smart pills for precision drug

delivery, glucose monitors and eye pressure sensing systems [1–4]. Similarly, wearable sensor nodes offer an

attractive set of applications such as medical/physiological

monitoring (e.g. electrocardiogram, temperature, respiration, heart rate, blood pressure), disability assistance and

human performance management. Integration of BAN with

the existing information infrastructure will create a truly

pervasive environment for many of these critical applications with great impact on improving the quality of life.

Some recent advances in microelectronics indicate that the

technology to achieve ultra-small and ultra-low power

devices for these applications is within reach [5, 6].

However, numerous regulatory and technical challenges

including efficient transceiver design, reliability, biocompatibility, size, cost, energy source, sensing/actuator technology, privacy and security issues still need to be resolved

[7, 8].

In 1999, the Federal Communications Commission

(FCC) allocated the frequencies in the 402–405 MHz range

to be used for Medical Implant Communication Services

K. Sayrafian-Pour (&) W.-B. Yang J. Hagedorn J. Terrill

Information Technology Laboratory, National Institute

of Standards and Technology (NIST), Gaithersburg,

MD, USA

e-mail: ksayrafian@nist.gov

W.-B. Yang

e-mail: wyang@nist.gov

J. Hagedorn

e-mail: hagedorn@nist.gov

J. Terrill

e-mail: Judith.terrill@nist.gov

K. Yekeh Yazdandoost K. Hamaguchi

National Institute of Information and Communications

Technology (NICT), Yokosuka, Japan

e-mail: yazdandoost@nict.go.jp

K. Hamaguchi

e-mail: hamaguti@nict.go.jp

123

Int J Wireless Inf Networks (2010) 17:105–112

DOI 10.1007/s10776-010-0124-y
(MICS). This is an ultra-low power, unlicensed mobile

radio service for transmitting data in support of diagnostic

or therapeutic functions associated with implanted medical

devices. The MICS permits individuals and medical practitioners to utilize ultra-low power medical implant devices, such as cardiac pacemakers and defibrillators without

causing interference to other users of the electromagnetic

radio spectrum. Among primary reasons for selecting these

frequencies, one can point to better propagation characteristics for medical implants, reasonable sized antennas

and worldwide availability. An international radio standard

for MICS will allow patients with implantable devices to

obtain necessary care worldwide.

Knowledge of RF propagation for implantable devices

will assist RF engineers to optimize physical layer design

and therefore, achieve better performance. Such information

is typically gathered by conducting physical experiments,

measuring and processing the corresponding data to obtain

channel characteristics. In case of medical implants, this

could be extremely difficult if at all possible. In this paper,

we present a sophisticated and innovative 3D virtual reality

simulation platform in order to study electromagnetic

propagation from medical implants. Communication with an

implanted device can be done from any direction inside or

outside the body due to various body postures and human

motion. Consequently, a true 3D environment is needed to

better capture, visualize and understand RF propagation

from/to implants. In the following sections, we describe

such a platform and show how it was used to extract a simple

statistical path loss model for MICS channels.

This paper is organized as follows. Section 2 will

describe the immersive 3D platform that we have constructed to study RF propagation from radio implants. The

simulated scenarios and description of our approach are

provided in Sect. 3. Then, the statistical path loss model is

described in Sect. 4, and finally concluding remarks and

future plans are expressed in Sect. 5.

2 A 3D Immersive Platform for Medical Implants

Block diagram of our simulation system is shown in Fig. 1.

The main components of this system include: a threedimensional human body model, the propagation engine

which is a three-dimensional full-wave electromagnetic

field simulator (i.e. HFSS1

), the 3D immersive and visualization platform, and finally an implantable (or body

surface antenna).

The 3D human body model includes frequency dependent dielectric properties of 300? parts in a male human

body. These properties are also user-definable if custom

changes or modifications are desired. The human body

model has a resolution of 2 mm. The HFSS propagation

engine enables us to compute a variety of different electromagnetic quantities such as the magnitude of electric

and magnetic fields and Specific Absorption Rate (SAR).

The 3D immersive platform as shown in Fig. 2 includes

several important components: three orthogonal screens

that provide the visual display, the motion tracked stereoscope glasses, and the hand-held motion tracked input

device. The screens are large projection video displays that

are placed edge-to-edge in a corner configuration. These

three screens are used to display a single three-dimensional

(3D) stereo scene. The scene is updated based on the

position of the user as determined by the motion tracker.

This allows the system to present to the user a 3D virtual

world within which the user can move and interact with the

virtual objects. The main interaction device is a hand-held

three button motion-tracked wand with a joystick.

This virtual environment allows for more natural interaction between experts with different backgrounds such as

engineering and medical sciences. The user can look at

data representations at any scale and position, move

through data, change orientation, and control the elements

Human Body

Model

Propagation Engine 3D Immersive

Platform

Input

Parameters

Human Body

Model

Propagation Engine 3D Immersive

Platform

Fig. 1 System block diagram

Fig. 2 A user in the NIST immersive visualization environment

1 HFSS is registered trademark of ANSYS Inc. The HFSS has been

used in this research to foster understanding. Such identification does

not imply recommendation or endorsement by the National Institute

of Standards and Technology, nor does it imply that this product is

necessarily the best available for the purpose.

106 Int J Wireless Inf Networks (2010) 17:105–112

123
of the virtual world using a variety of interactive measurement and analysis techniques [9]. All of these capabilities are extremely useful when studying RF propagation

to/from medical implants.

The final component of our system is the implant

antenna. The operating environment for an implant antenna

is quite different from the traditional free space communication. Designing an efficient antenna for implantable

devices is an essential requirement for reliable MICS

operation [10]. The dimension of the antenna must be very

small and it should be long term biocompatible [11]. The

antenna must also be electrically insulated from the body to

avoid possible short circuits [12].

Figures 3 and 4 display the implant antenna used in our

simulations. The antenna is composed of a single metallic

layer and is printed on a side of a D51 (NTK) substrate with

dielectric constant of er = 30, loss tangent of tan h =

0.000038, and thickness of 1 mm. The metallic layer is

copper with 0.036 mm thickness. The dimension of the

antenna is 8.2 9 8.1 9 1 mm which is quite appropriate for

some medical applications. The metallic layer is covered by

RH-5 substrate with dielectric constant of er = 1.0006, loss

tangent of tan h = 0, and thickness of 1 mm. The simulated

return loss of this antenna is shown in Fig. 5. Good impedance matching in the MICS frequency band (i.e.

402–405 MHz) is observed. Further details on the design

and performance of this antenna can be found in [13, 14].

Input parameters to our system include: antenna position, antenna orientation, operating frequency, transmit

power, resolution, range and the choice of the desired

output parameters. The operating frequency in this study

has been chosen to be 403.5 MHz which is the mid-point of

the MICS frequency band. Resolution of 4 mm has been

selected to run the simulation and a range (i.e. distance) of

50 cm from the transmitting antenna has been considered.

Figure 6 displays sample output images of our immersive system when an antenna is located in the stomach (e.g.

side and front views). The color map shows the signal

strength on a 2D plane which is perpendicular (Fig. 6a) or

parallel (Fig. 6b) to the antenna plane.

3 Simulation Scenarios

Simulations have been performed for four near-surface

implants and two deep-tissue implants applications in a

typical male body. The near-surface scenarios include

applications such as Implantable Cardioverter-Defibrillator

(ICD) and Pacemaker (located below the left pectoral

Fig. 3 Front view of the implant antenna

Fig. 4 Side view of the implant antenna

Fig. 5 Return loss of the implant antenna

Fig. 6 Sample output image of the immersive system. a Side view.

b Front view

Int J Wireless Inf Networks (2010) 17:105–112 107

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muscle), Vagus Nerve Stimulation (Right Neck and

Shoulder) and two Motion Sensor applications located in

right hand and right leg. These locations are highlighted by

the white circles in Fig. 7.

The deep tissue implant scenarios considers endoscopy

capsule applications for upper stomach (95 mm below

body surface) and lower stomach (118 mm below body

surface) as highlighted by the red circles in Fig. 7.

For each scenario (i.e. TX location), the received power

was calculated for a grid of points within a cylinder area

around the body. Then, the resulting data was partitioned

into three sets: in-body to in-body, in-body to body surface,

and in-body to out-body propagation sets. The in-body to

in-body set includes all of the sample points that completely reside inside the body. Likewise, the in-body to

body surface set includes all points that reside within a

definable distance (i.e. 2 mm, 10 mm, and 20 mm) from

the body surface; and finally the in-body to out-body

propagation set distinguishes all of the points that reside

further away from the body surface.

4 Path Loss Model

Path loss at a distance d from the transmitting antenna, in

our calculation, is defined as:

PLðdÞ 1⁄4 GRPT

PRðdÞ

where PT is the transmit power, PR denotes the received

power and GR is the receiver antenna gain. Therefore, as

defined in the above equation, the path loss would include

the transmitter antenna gain. This is usually not the case for

channel models corresponding to most wireless systems,

but for MICS, the transmitting antenna is considered to be

part of the channel [15]. The path loss in dB at distance

d can be statistically modeled by the following equation:

PLðdÞ 1⁄4 PLðd0Þ þ 10n log10ðd=d0Þ þ S d  d0

where d0 is the reference distance (i.e. 50 mm), and n is the

path loss exponent which heavily depends on the environment where RF signal is propagating through. For,

example, it is well known that for free space n = 2. Human

body is an extremely lossy environment; therefore, much

higher value for the path loss exponent is expected. S is the

random scatter around the mean and represents deviation in

dB caused by different body materials (e.g. bone, muscle,

fat, etc.) and the antenna gain in different directions.

We first model the path loss by finding the values for

PL(d0), n and S for deep-tissue versus near-surface

implants separately. This will lead to four propagation

scenarios i.e. deep-tissue implant-to-implant, near-surface

implant-to-implant, deep-tissue implant to body surface

and finally near-surface implant to body surface. Next, we

combine the deep-tissue and near-surface implant scenarios

into one set of data and only consider the propagation

channel for the following two cases: implant-to-implant

and implant to body surface. This should give us more

insight into possible differences between communication

channels related to the deep tissue and near surface

implants.

Figure 8 shows the scatter plot for the path loss as a

function of TX–RX separation for deep tissue implant-toimplant scenarios. The mean value of the random path loss

has been displayed by a solid line. This is obtained by

Fig. 7 Various transmitter locations for obtaining signal strength

0 50 100 150 200 250 300 350 400 450 500

20

30

40

50

60

70

80

90

100

110

120

range (mm)

Path Loss (dB)

Fig. 8 Scatter plot of the path loss versus distance for deep tissue

implant to another implant

108 Int J Wireless Inf Networks (2010) 17:105–112

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fitting a least squares linear regression line through the

scatter of measured path loss sample points in dB such that

the root mean square deviation of sample points about the

regression line is minimized. Random shadowing effects of

the channel occur where the TX–RX separation is the

same, but have different directions or positions with respect

to each other. As shown in Fig. 9, this random variable has

a normal distribution with zero mean and standard deviation rs i.e. S * N(0, rs

2

).

Similarly, Figs. 10 and 11 represent the path loss scatter

plot and probability density function of the shadow fading

random variable for the deep tissue implant to body surface

scenarios. A distance of up to 20 mm directly from the

body surface has been considered in the definition of the

body surface sample points. We also tried a separation

distance of 2 and 10 mm and observed that the derived path

loss parameters did not vary much. One should keep in

mind that layers of clothing could cause additional loss to

the signal.

Table 1 summarizes the extracted parameters of the

statistical path loss models for the above scenarios.

Scatter plots and shadow fading distributions corresponding to the near-surface implant scenarios have been

provided in Figs. 12, 13, 14, and 15.

Table 2 summarizes the extracted parameters of the

statistical path loss models for the near surface implant

scenario.

Deriving a channel model for communication between

an implant and another node that resides further away from

the body is more challenging. In the simple case of an

environment where there are no objects or obstacles, a free

space path loss can be added to the above models to

account for the additional loss that the implant signal will

go through once it leaves the body. On the other hand, if

there are objects adjacent to the body in the surrounding

environment, further loss could occur and the model needs

to be adjusted accordingly. With appropriate assumptions

this channel could be considered as an aggregate of two

concatenated channels: implant to body surface and body

surface to the external node channels. Further analysis and

measurement data is needed to investigate this channel and

obtain the appropriate statistical models.

-30 -20 -10 0 10 20 30 0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.05

dB

PDF

Fig. 9 Distribution of the shadow fading for deep tissue implant to

another implant

0 50 100 150 200 250 300 350 400 450 500

20

30

40

50

60

70

80

90

100

110

120

range (mm)

Path Loss (dB)

Fig. 10 Scatter plot of the path loss versus distance for deep tissue

implant to body surface (within 20 mm of the skin)

-25 -20 -15 -10 -5 0 5 10 15 20 25

0

0.01

0.02

0.03

0.04

0.05

0.06

dB

PDF

Fig. 11 Distribution of the shadow fading for deep tissue implant to

body surface (within 20 mm of the skin)

Table 1 Parameters for the statistical path loss model for the case of

deep tissue implant

PL(d0) (dB) n rs (dB)

Deep tissue implant to another implant 35.04 6.26 8.18

Deep tissue implant to body surface 47.14 4.26 7.85

Int J Wireless Inf Networks (2010) 17:105–112 109

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5 Conclusion

We have presented an immersive visualization environment to conduct research in order to characterize RF

propagation from medical implants. Extensive simulations

have been performed to obtain a statistical path loss model

for MICS channels. The model is based on four near surface and two deep tissue implant applications in a typical

male human body. The authors recognize the fact that the

extracted parameters for the statistical model are based on

the simulation data; therefore, upon availability of measurement data from physical experiments, the results outlined in this paper should be further validated. However, no

such reference data set is currently available. Along this

line, efforts are underway to conduct and obtain body

surface measurements which can be emulated in the

immersive system for cross-verification purposes. The path

loss models obtained in this study have been adopted by the

IEEE802.15 task group TG6 on body area networks. The

accuracy and applicability of such models requires further

validation and investigation by scientists and engineers.

In general, studying a specific medical implant application with custom made antennas is also possible with this

platform. More in-depth research on this subject is

0 50 100 150 200 250 300 350 400 450 500

20

30

40

50

60

70

80

90

100

110

120

range (mm)

Path Loss (dB)

Fig. 12 Scatter plot of the path loss versus distance for near surface

implant to another implant

-30 -20 -10 0 10 20 30 40

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.05

dB

PDF

Fig. 13 Distribution of the shadow fading for near surface implant to

another implant

0 50 100 150 200 250 300 350 400 450 500

20

30

40

50

60

70

80

90

100

110

120

range (mm)

Path Loss (dB)

Fig. 14 Scatter plot of the path loss versus distance for near surface

implant to body surface (within 20 mm of the skin)

-30 -20 -10 0 10 20 30

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

dB

PDF

Fig. 15 Distribution of the shadow fading for near surface implant to

body surface (within 20 mm of the skin)

Table 2 Parameters for the statistical path loss model for the case of

near surface implant

PL(d0) (dB) n rs (dB)

Near surface implant to another implant 40.94 4.99 9.05

Near surface implant to body surface 49.81 4.22 6.81

110 Int J Wireless Inf Networks (2010) 17:105–112

123
undoubtedly required to further understand the characteristics of radio frequency propagation from medical

implants. The authors hope that the virtual reality environment introduced here would create a flexible platform

where more efficient collaboration between engineers and

medical experts would become possible.

Acknowledgment The authors would like to thank Mr. Steven

Satterfield from the high performance computing and visualization

group of the Applied and Computational Mathematics Division, ITL,

for his assistance in the system setup and his ongoing contribution in

the development of tools for the NIST immersive visualization system.

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Author Biographies

Kamran Sayrafian-Pour is a

program manager at the Information Technology Laboratory

of the National Institute of

Standards and Technology

(NIST) located in Gaithersburg,

Maryland. He leads several

strategic projects that are

focused on Pervasive Computing technologies in Healthcare.

He holds Ph.D., M.S. and B.S.

degrees in Electrical and Computer Engineering from University of Maryland, Villanova

University and Sharif University of Technology, respectively. Prior to joining NIST, he was the

cofounder of Zagros Networks, Inc. a fabless semiconductor company

based in Rockville, Maryland where he served as President and senior

member of the architecture team. Dr. Sayrafian-Pour is the coinventor/inventor of four U.S. patents. He is a senior member of IEEE

and an adjunct faculty of the University of Maryland. He has served

as invited member of technical program committee and co-chair of

many international conferences and workshops. His research interests

include body area networks, mobile sensor networks and RF-based

indoor positioning. He has published over 50 conference and journal

papers, and book chapters in these areas. He was the recipient of the

IEEE PIMRC 2009 best paper award and has also been recognized as

the outstanding faculty of 2010 by the office of advanced engineering

education at the University of Maryland, College Park. He has been a

contributing member and the co-editor of the channel modeling

document of the IEEE802.15.6 international standardization on body

area networks.

Wen-Bin Yang received his

Ph.D. degree in Electrical

Engineering from the University

of Maryland at College Park in

1993. Since 2007, he has been

with the National Institute of

Standards and Technology,

where he focuses on body area

networks technology. He

worked on underwater acoustic

communications in the Naval

Research Laboratory in Washington DC from 2002 to 2007.

His current research interests

include wireless communications technology and applications. He was the recipient of the IEEE

PIMRC 2009 best paper award.

Int J Wireless Inf Networks (2010) 17:105–112 111

123
John Hagedorn received his

B.A. and M.S. degrees in

Mathematics from the University of Virginia in 1975 and

Rutgers University in 1980

respectively. He has worked in

the area of scientific computing

at Cornell University, NASA,

and Circuit Studios. He is currently at the Information Technology laboratory of the

National Institute of Standards

and Technology working in a

variety of technical areas

including scientific visualization

and high performance computing. He was the recipient of the IEEE

PIMRC 2009 best paper award.

Judith Terrill is a Computer

Scientist and the Leader of the

high performance computing

and visualization group of the

Applied and Computational

Mathematics Division of the

Information Technology Laboratory at NIST. Her research

interests include machine discovery in scientific data, quantitative scientific visualization,

and parallelization of complex

computational codes. She was

the recipient of the IEEE PIMRC 2009 best paper award.

Kamya Yekeh Yazdandoost

received his Ph.D. degree from

University of Pune, India, in

2000. He joined the Communications Research Laboratory

(CRL), Japan in 2003 (now,

National Institute of Information and Communications

Technology: NICT), and has

been engaged in research on

Ultra WideBand (UWB) and

Wireless Body Area Network

(BAN) technology. He is currently working as an Expert

Researcher at the Medical-ICT

group, National Institute of Information and Communications

Technology, Japan. He is an adjunct Professor at University of Oulu,

Finland and he is the recipient of Finland Distinguished Professor

Program (FIDIPRO) for the periods of 2010–2014. He was invited

Guest Professor at the Asian Institute of Technology in Bangkok

Thailand Feb. 2007. He is the Chair of the channel modeling committee of the IEEE.802.15.6 on Body Area Network Standardization

and he is a life member of the IEEE Microwave Theory and Techniques Society. He is a representative of the NICT to the European

COST2100 Action (Management Committee Meeting on Pervasive

Mobile and Ambient Wireless Communications). He is also a

Member of European Microwave Association. He is holding four

patents and his six other patents are pending. He is also Associate

Editor of International Journal of Microwave Science and Technology. He has been the TPC member and session chair of various

international conferences. He was the recipient of the IEEE PIMRC

2009 best paper award.

Kiyoshi Hamaguchi received

the B.S. and M.S. degrees in

electrical engineering from Science University of Tokyo in

1989 and 1991, respectively. He

also received the Ph.D. degree

in electrical engineering from

Osaka University in 2000. Since

1993 he has been with the

National Institute of Information and Communications

Technology (NICT), Japan,

where he has been engaged in

research and development on

wireless telecommunication

systems. From 2002 to 2003, he was a visiting researcher at University of Southampton, U.K. He received the Young Engineer Award

from IEICE in 1997, the Young Scientist Award from Ministry of

Education, Culture, Sports, Science and Technology of Japan in 2006

and the Radio Achievement Award from the Association of Radio

Industries and Businesses (ARIB) in 2010. He is currently a group

leader of Medical ICT group in NICT.

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