Biomedical Engineering Trends in Electronics Communications and Software Part 3 potx

Biomedical Engineering Trends in Electronics, Communications and Software

70
sensing element. After mounting the sensors, the outputs signals are conditioned, filtered
and then digitized with a high resolution data acquisition card. A static calibration test has
been fulfilled to estimate the degree of linearity. Preliminary measurement has been carried
out concerning the fingertip forces grasping of hand during holding objects and the
distribution of impacts forces during foot contact.
2. Principe and sensor design
For the design of the sensor element, a Hall Effect sensor UGN3503 from Allegro Micro-
Systems was selected. This sensor is used for measuring magnetic flux densities with
extreme sensitivity and operates well in the temperature range from –20°C to +85°C and in
the frequency range from DC to 23 kHz. This device is widely used for measuring linear
position, angular position, velocity and rotational speed. Hall sensors are also commonly
incorporated into CD-ROM drive, hard disk drives, automotive ignition, electrical current
sensing and ABS braking systems as they are robust, unaffected by dirty environments and
low-cost (Ripka & Tipek, 2007). In contrast to other magnetic sensors, the manufacture of
Hall magnetic sensors does not require special fabrication techniques as they are compatible
with microelectronics technology. Most of the sensors are low-cost discrete devices but an
increasing proportion now come in the form of integrated circuits. The integrated Hall
magnetic sensors usually incorporate circuits for biasing, offset reduction, temperature
compensation, signal amplification and signal level discrimination. The most advanced Hall
sensors incorporate digital signal processing and are programmable such as HAL800 from
Micronas (Bushbaum & Plassmeier, 2007). The considered sensor element is constructed by
placing a magnet which produces a constant magnetic field nearby the selected Hall sensor.
The layer of thickness d between the magnet and the Hall sensor is realized with an elastic
polymer materiel (Fig.1). Special care was dedicated to the positioning of magnet in the
direction of the surface area of sensing in order to reduce the nonlinearity of the tactile
sensor (Ehrlich, 2000); (Kyberd & Chappel, 1993). After the placement of the different layers
composing the whole sensor element, a thin twisted pair wire is soldered to the Hall sensor

as the voltage produced is at low level and need low noise amplification.

Hall sensor
Soft magnet
Protective sheet
Polysiloxanes
d
Magnetic filed
B
F(x)




Fig. 1. The sensor element principle and realization
A Low Cost Instrumentation Based Sensor Array for Ankle Rehabilitation

71
First, the elastic polymer (polysiloxanes) and a piece taken from mouse mat were studied to
show the possibility of using this material in building the sensing element. A test bed with
Lutron FG-5000A was performed for this purpose and experimental data are reported in
Fig.2 for the two chosen materials.

01234
0
10
20
30
40
50

F1-Polysiloxanes
F2-Mouse mat
Stress:
σ
(N/cm²)
Strain: δx (mm)
Linear behaviour up to 1mm

Fig. 2. Characteristics of the materials
For the second material (mouse mat) a strong nonlinear behavior was observed for strain
greater than 2 mm. For strain up to 2 mm, the characteristic was quasi linear. The second
material exhibits a better monotony with soft nonlinearity. As a calibration curve the
following exponential growth was found with a correlation coefficient of about 0.997:


exp( / )Fxk=β+α× δ
(1)
A more precise calibration curve was obtained with a third-order polynomial with a
correlation coefficient of about 0.999, thus:


() () ()
23
01 2 3
.yFx a axa x a x=δ=+δ+ δ+ δ
(2)
As a nonlinear property is found for the studied material, a software routine was
implemented after digital signal acquisition to perform nonlinearity correction. From the

calibration curve of the sensor an equi-spaced 1-D look-up table is created and a quadratic
interpolation was used (Attari, 2004); (Dias Pereira et al., 2001) whose curve passes through
three points
11
(, )
kk
yxδ
−−
,
(, )
kk
yxδ
,
11
(, )
kk
yxδ
++
,

(
)
[
]
()()
[]
1
111
,
-,,

kkkk
kkkkk
xx yyfy y
yy y y f y y y
−
−
−+
δ=δ + − +
+−
(3)
with the second divided difference given by,
Biomedical Engineering Trends in Electronics, Communications and Software

72

[]
[]
[][]
1
1
1
11
11
11
,
,,
,,
kk
kk
kk

kk k k
kkk
kk
xx
fy y
yy
f
yy fy y
fy y y
yy
+
+
+
+−
−+
+−
δ
−δ
=
−
−
=
−
(4)
3. Signals conditioning and experimental
The outputs signals issued from the sensors elements are carried onto a low level
instrumentation amplifier (AD622, Analog Devices) with low offset voltage, low noise and
high CMRR. After analog conditioning, these signals are filtered with a second order
Butterwoth active filter and sampled and digitalized with a commercial National Instrument
data acquisition card (DaqBoard 1005) and then fed a PCI PC bus. Fig.3. show the analog and

digital part of the prototype circuit which is directly connected to each sensor element
where the output signals are multiplexed with the circuit included in the data acquisition
card. First step is to perform the static calibration characteristics by applying a variable force
from 1 to 10N provided by a test bed (Lutron FG-5000A). Fig.4 shows outputs signals from
five sensors elements. Least squares linear regression were performed to compute the
estimated linear calibrating curves and to determine the sensor sensitivity for each sensor.
After analyzing the sensors data, a linearity was observed for the range [0-10N] with a
correlation coefficient greater than 0.99. For forces up to 10N the responses become less
accurate against linearity shape and correction based on the method described above (Sec.2)
was performed for further investigation, for instance in 2D stress measurement for foot
reaction forces distribution. For dynamic experimentation two tests in real environment
have been realized.

V
Hall-2
V
S2
AD622
+V
pp
-V
pp
R
R
P
Multiplexer
NI Daq-Board
1005
UGN3502
Butterworth

LPF
16 bits ADC
B
T
o

P
C
I
Bus
V
Hall-1
V
S1
AD622
+V
pp
-V
pp
R
R
P
UGN3502
Butterworth
LPF
B
V
Hall-k
V
Sk

AD622
+V
pp
-V
pp
R
R
P
UGN3502
Butterworth
LPF
B
D
i
s
t
r
i
b
u
t
i
o
n
of s
t
ress

Fig. 3. Conditioning circuit array and data acquisition
A Low Cost Instrumentation Based Sensor Array for Ankle Rehabilitation

73
3.1 Test during holding objects
For the test five sensors element are bonded onto the fingertips of human hand (Fig. 5).
Outputs signals are observed and a software program is developed to analyze the fingertips
movement during holding objects. Fig.6 shows the response corresponding to grasping of
the thumb, index, middle, ring and little fingertips during holding a bottle of mineral water.
The experimental results show that the changes of dynamic fingertips force affects the
transducers in the contact phase measurement. The thumb, index and middle are the fingers
that give the highest signal level as they exert high pressures regarding the two other
fingers. This observation is in concordance with the biomechanics of hand which verify the
feasibility of the proposed sensors arrays.

0,0 2,5 5,0 7,5 10,0 12,5 15,0
0
1
2
3
4
5


Voltage V
out
(V)
Stress
σ
(N/cm²)
Sensor.1
Sensor.2

Sensor.3
Sensor.4
Sensor.5


Fig. 4. Static calibration


Fig. 5. Tactile sensors bounded on fingers hand
Biomedical Engineering Trends in Electronics, Communications and Software

74
0 5 10 15 20
0,0
0,5
1,0
1,5
2,0
Voltage V
out
(V)

Time T (s)
Thumb
Index
Middle
Ring
Little
End of grasping

Fig. 6. Outputs signals of transducers during holding
3.2 Test for ankle rehabilitation
Second dynamic measurement in real environment has been carried out with eight realized
sensors which are bonded onto a flexible material as foot shape (Fig. 7). Fig. 8 shows the
apparatus constructed with wood beech dedicated for the rehabilitation of ankle. Fig.9
shows the response corresponding to eight tactile sensors distributed on the insole surface
during an experiment of ankle rehabilitation. The experimental results for 30s recording
show clearly the frequency swing of the wood substrate. Also, a delay time is observed for
example between sensors S1 and S8 during foot swing where the whole body is maintained
stable with one foot. This observation is in concordance with the geometry of the placement
of sensors and it is obvious to show that the time delay is approximately half time the time
of swinging, thus,


1
2
D
elay Swing
TT≈
(5)


Fig. 7. Placement of eight tactile sensors
A Low Cost Instrumentation Based Sensor Array for Ankle Rehabilitation

75


S1
S2

S3
S4
S5
S6
S7
S8

Fig. 8. Apparatus for ankle rehabilitation



S1
Time (s)
Shifted Signals (V)
S2
S3
S4
S5
S6
S7
S8
Delay

Fig. 9. The eight recorded signals

Futures investigations are oriented toward the realization of embedded bioinstrumentation
system for the measurement of foot reaction forces for a dedicated balanced platform. This
one will be the essential part of the test bed system for the determination of force shape of
foot during the rehabilitation of ankle. Fig. 10 shows the principle part of the whole system
which consists on positioning a numbers of sensors elements on a special platform fit with

dimension of a standard foot. The number of sensors will be determined with resolution
required for the foot reaction forces study (Boukhenous et al., 2006). For better flexibility of
data acquisition with high special resolution, the HAL800 digital programmable Hall Effect
device is preferred. The proposed printed circuit board (PCB) for the realizing of the whole
2D sensing system is shown in Fig. 11. Notice that the number of signals outputs pads is
equal to the number of sensors elements. Also, a special care will be considered in
positioning precisely the Hall devices with taken into account shielding and grounding of
the whole PCB. An epoxy resin will be deposited carefully on the sensors array in order to
standardize the first layer against the elastic material.
Biomedical Engineering Trends in Electronics, Communications and Software

76
Sensor Element
Foot Interaction
Distribution of Strain
Νx
R Pivot
L Pivot

Fig. 10. Tactile sensors array for ankle rehabilitation


Fig. 11. Placement of sensors elements in a rigid PCB
4. Conclusion
In this paper a low cost tactile sensors array for biomedical applications are presented. Each
sensor element was constructed separately and based on the use of Hall sensor devices. The
sensors were calibrate and trimmed before proceeding to the experimental tests. A
dedicated analog signal processing was designed and realized according to the specificity of
the realized sensor. Accurate settings have been achieved by offset and gain trimming for
zero crossing and required sensitivity. Outputs signals from the conditioning circuit of the

eight transducers are coupled to a high resolution data acquisition card. The software
program developed analyzes and calibrates the multisensors signals. Dynamic
experimentation for fingertips grasping of the hand during holding an objects and the
A Low Cost Instrumentation Based Sensor Array for Ankle Rehabilitation

77
distribution of impacts forces during foot contact for ankle rehabilitation shows a
satisfactory response and verify the feasibility of the proposed sensors array. After
analyzing the sensors, the data found in the range [0-10N] is the optimized interval for best
linearties. Future works are focused toward an intelligent calibration and processing of the
acquired signals using dedicated analog processor and FPGA implementation of a matrix of
sensors elements for the monitoring of ankle rehabilitation.
5. References
Attari, M. & Boukhenous, S. (2008). A Tactile Sensors Array for Biomedical Applications,
Proceeding of 5th International Multi-Conference on Systems, Signals and Devices, IEEE-
SSD’08, ISBN: 978-1-4244-2206-7, Amman, Jordanie, Juillet 20-23, 2008
Attari, M. (2004). Correction Techniques for Improving Accuracy in Measurements, State of
the Art, Proceeding of International Conference on Computer Theory and Applications,
ICCTA/2004, Alexandria, Egypt, September 2004
Beebe, D.J. & Denton, D.D. (1998). A silicon-based tactile sensor for finger-mounted
applications. IEEE Trans. Biomed. Eng., Vol. 45, pp. 151-159, Feb. 1998
Boukhenous, S. & Attari, M. (2007). A Low Cost Grip Transducer Based Instrument To
Quantify Fingertip Touch Force, Proceedings of IEEE Engineering in Medicine and
Biology Society, Science and Technologies for Health, EMB’2007, pp. 4834-4837,
ISBN: 1-4244-0788-5, ISSN: 1557-170X, , Lyon, France, Vol. 4, August 21-24, 2007
Boukhenous, S.; Attari, M. & Ababou, N. (2006). A Dynamic Study of Foot-to-Floor
Interaction During a Vertical Jumping. AMSE Journals, Modeling B, Vol.75, N°1,
April 2006, pp. 41-49, ISSN: 1259-5969
Buschbaum, A & Plassmeier,V.P. (2007). Angle measurement with a Hall effect sensor, Smart
Mater. Structl., Vol. 16, 2007, pp. 1120-1124

Chi, Z. & Shida, K. (2004). A New Multifunctional Tactile Sensor for Three-Dimensional
Force Measurement. Sensors and Actuators, Vol. A111, 2004, pp. 172-179
Cowie, B.M.; Webb, D.J.; Tam, B.; Slack, P. & Brett, P.N. (2007). Fibre Bragg gratting sensors
for distributive tactile sensing. Journal of Meas. Sci. Technol., Vol. 18, 2007, pp. 138-
146
Da Silva, J.G.; Carvalho, A. A. & Silva, D. D. (2000). A strain gage tactile sensor for finger-
mounted applications, Proceeding of IEEE Instrum. Meas. Technol. Conf., IMTC/2000,
Baltimore, MD, May 1–4, 2000
Da Silva, J.G.; Carvalho, A. A. & Rodrigues, R. O. (2000). Development of a dynamometer
for hand clinical evaluation, Proceedings of Iberdiscap Conf., pp. 429-434, Portugal,
2000
Dias Pereira, J.M.; Silva Girão, P.M.B. & Postolache, O. (2001). Fitting Transducer
Characteristics to Measured Data. IEEE Instrumentation and Measurement Magazine,
pp. 26-39, December 2001
Ehrlich, A.C. (2000). The Hall Effect, In : The Electrical Engineering Handbook Ed. Richard
C. Dorf Boca Raton: CRC Press LLC, 2000
Hasegawa, Y.; Shikida, M.; Sasaka, H.; Itoigawa, K. & Sato, K. (2007). An active tactile
sensor for detecting mechanical charactyeristics of contacted objects. Journal.
Micromech. Microeng., Vol. 16, 2007, pp. 1625-1632
Jayawant, B.V. (1989). Tactile Sensors in Robotics. J. Phys. E: Sci. Instrum., Vol. 22, 1989, pp.
684-692
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Kyberd, P.J. & Chappell, P.H. (1993). A Force Sensor for Automatic manipulation Based on
the Hall Effect. Journal of Meas. Sci. Technol., Vol. 4, 1993, pp. 281-287
Mascaro, S. & Asada, H. H. (2001). Photoplethysmograph fingernail sensors for measuring
finger forces without haptic obstruction. IEEE Trans. Robot. Automat., Vol. 17, pp.
698–708, Oct. 2001
Nicholls, H.R. & Lee, M.H. (1989). A Survey of Robot Tactile Sensing Technology. Int.

Journal. Robotics Res, Vol. 8, N. 3, 1989, pp.3-30
Reston, R.R.; Kolesar, J.E. & Mascaro, S. (1990). Robotic tactile sensor array fabricated from
piezoelectric polyvinilidene fluoride film, Proceedings of Nat. Aerospace Electron.
Conf. (NAECON), pp. 1139-1144, 1990
Ripka, P. & Tipek, A. (2007). Modern Sensors Handbook, ISTE Ltd, UK, 2007, 536 p
Tarchanidis, G.K.N. & Lygouras, J. N. (2001). Data glove with a force sensor, Proceedings of
IEEE Instrum. Meas. Technol, Budapest, Hungary, May 21-23, 2001
Webster, J.G. (1998). Tactile Sensors for robotics and Medicine, J.G. Webster, Ed. Wiley, New
York
5
New Neurostimulation Strategy and
Corresponding Implantable Device to
Enhance Bladder Functions
Fayçal Mounaïm and Mohamad Sawan
Polystim Neurotechnologies Laboratory, Department of Electrical Engineering
École Polytechnique de Montréal
Canada
1. Introduction
Spinal cord injury (SCI) is one of the most complex and devastating medical conditions. Its
worldwide incidence ranges from 11 to 112 per 100,000 Population (Blumer & Quine, 1995;
DeVivo, 1997). SCI leads to different degrees of dysfunction of the lower urinary tract due to
a large variety of possible lesions. Immediately after SCI, flaccid paralysis sets in, followed
by the absence of reflexes and a complete loss of sensory and motor control below the level
of lesion, rendering the urinary bladder areflexic and atonic. This period, termed spinal
shock, can extend from a few days to several months (Chai & Steers, 1996). Most patients
with suprasacral SCI suffer from detrusor over-activity (DO) and detrusor sphincter
dyssynergia (DSD) (Blaivas et al., 1981). DSD leads to high intravesical pressure, high
residual urine, urinary tract infection, and deterioration of the upper urinary tract. In order
to recover the voluntary control of micturition, functional electrical stimulation (FES) has
been investigated at different sites of the urinary system: the bladder muscle (detrusor), the

pelvic nerves, the spinal cord and the sacral nerve roots. Among these, sacral nerve root
stimulation is considered the most efficient technique to induce micturition and has been
prevalent in clinical practice over the last two decades (Elabaddy et al., 1994). Using cuff-
electrodes, this technique offers the advantages of a safe and stable fixation of electrodes as
well as confinement of the spread of stimulation current within the targeted nerves.
However, the detrusor and the external urethral sphincter (EUS) muscles share the sacral
nerves as common innervations pathways, and stimulation of the entire sacral root induces
contraction of both. Thus, the efficiency of micturition by means of sacral neurostimulation
depends on the capability to contract the detrusor without triggering EUS contraction. In
order to improve this neurostimulation selectivity, several techniques have been proposed,
among which are rhizotomy, and EUS blockade using high-frequency stimulation.
Dorsal rhizotomy consists of selectively severing afferent sacral nerve roots that are
involved in pathological reflex arc in suprasacral SCI patients. Rhizotomy abolishes DO,
reduces DSD, and prevents autonomic dysreflexia. As a beneficial result, the uninhibited
bladder contractions are reduced, the bladder capacity and compliance are increased, urine
flow is improved, and consequently the upper urinary tract is protected from ureteral reflux
and hydronephrosis. In case of a complete SCI, dorsal rhizotomy is combined with an
Biomedical Engineering Trends in Electronics, Communications and Software

80
implantable sacral ventral root stimulator such as the Finetech-Brindley Bladder System
(also known as the VOCARE in North America) (Kutzenberger, 2007). In fact, this
neurostimulation system is the only commercialized and FDA-approved solution aiming for
micturition in SCI patients (Jezernik et al., 2002). Unfortunately, rhizotomy being
irreversible, it has a fundamental disadvantage which is the abolition of sexual and
defecation reflexes, as well as sacral sensations if still present in case of incomplete SCI.
High-frequency stimulation can be used to inhibit the contraction of the EUS muscle.
However, the mechanism by which the EUS inhibition is obtained is not well understood
and three explanations are possible: high-frequency stimulation may stop the propagation
of nerve action potentials, may maintain the motor end-plate (neuromuscular junction) in a

refractory status, or may fatigue the aimed muscle (Kilgore & Bhadra, 2004; Tai et al., 2005;
Williamson & Andrews, 2005). Frequencies from 300 Hz to 30 kHz can be used to achieve a
complete and reversible nerve conduction block depending on the stimulation amplitude
(Solomonow, 1984; Sievert et al., 2002; Schuettler et al., 2004; Bhadra et al., 2006). However,
below 1 kHz, a sinusoidal stimulation can generate action potentials at the same or a
submultiple rate. Increasing the frequency has the advantage of lowering the amount of
injected charge per-phase needed for a complete blockade. A graded blockade can also be
achieved as blockade of each axon within the nerve is influenced by its diameter and the
stimulation amplitude (Tai et al., 2005). If a graded blockade is applied distally in
combination with low-frequency stimulation, selectivity with respect to axon diameter can
be obtained by adjusting stimulation amplitude (Williamson & Andrews, 2005). Finally,
combining sacral root stimulation with bilateral high-frequency pudendal nerve block led to
effective micturition in male cats (Boger et al., 2008).
The efficiency of high-frequency blockade was studied with dog experiments using a
neurostimulator designed by Polystim Neurotechnologies Laboratory (Robin et al., 1998;
Shaker et al., 1998; Ba et al., 2002; Sawan et al., 2008b). The Polystim’s stimulator generated a
rectangular waveform combining two frequencies (e.g. 600 Hz and 30 Hz). It is important to
point out in this case, that stimulation and blockade are both applied simultaneously at the
same nerve site, with the same bipolar electrode. According to Kilgore et al. (Kilgore &
Bhadra, 2004), blockade at 600 Hz frequency with less than 2 mA current is probably due to
a muscle fatigue mechanism rather than nerve conduction blockade. The same
neurostimulator was also implanted in paraplegic dogs for chronic experiments where it
was demonstrated that the combination of low and high frequency stimuli resulted in 45 %
reduction in EUS activity and that urine evacuation improved up to 91 % of the mean
bladder capacity during the six months of chronic stimulation (Abdel-Gawad et al., 2001).
The latest Polystim’s neurostimulation prototypes using that stimulation strategy were
UroStim6 and UroStim7 presented in (Mounaim et al., 2006; Mounaim & Sawan, 2007)
respectively.
This chapter first describes a new sacral neurostimulation strategy to enhance micturition,
based on nerve conduction blockade using high frequency stimulation as an alternative to

rhizotomy. In order to test this strategy in chronic animal experiments, an implantable
neurostimulation device is required. Thus, this chapter presents the design, test, prototyping
and encapsulation of such neurostimulator (UroStim8) implementing the proposed
stimulation strategy and using only commercially available discrete components.
2. New stimulation strategy
The proposed multi-site sacral neurostimulation strategy is illustrated in Fig. 1 and based on
the following: High-frequency stimulation with an alternating waveform (such as sinusoidal
New Neurostimulation Strategy and
Corresponding Implantable Device to Enhance Bladder Functions

81
or rectangular) and optimum parameters, induces a blockade of the nerve (motor and/or
sensory) activity, that may be complete (all axons) or partial (large diameter axons only).
With a complete nerve blockade, the effect would be equivalent to that of rhizotomy while
being controlled and totally reversible. With a partial blockade, selective stimulation can be
achieved by blocking large axons only.

S1
S2
Possible nerve stimulation sites,
Low-frequency pulse waveform (e.g. 30Hz)
Right
Sacral roots
Possible nerve conduction blockade sites,
High-frequency sinusoidal waveform (> 1kHz)
Sp inal cord
Left
Sacral roots
Complete nerve blockade (all axons)
Selective blockade (large diameter axons only)

Stim. Stage 4Stim. Stage 3
Stim. Stage 2
Stim. Stage 1
Electrodes connected to the same stimulation stage

Fig. 1. Proposed multi-sites sacral neurostimulation strategy (dog model)
In order to induce a contraction of the detrusor, a low-frequency (e.g. 30 Hz) pulse current
stimulation is applied to S2 sacral nerve(s) (or S1 eventually), unilaterally or bilaterally.
Adjusting the stimulation pulse amplitude and width, the degree of contraction can be
modulated. In most cases, the EUS contracts as well. The stimulation-evoked EUS
contraction may be explained by direct and/or reflex mechanisms due to efferent and/or
afferent fibers activation respectively. Both types of EUS activation can be avoided by
blocking axons innervating the EUS muscle with high-frequency (> 1 kHz) stimulation. A
selective blockade can be applied distally (between the low-frequency stimulation site and
the EUS) to inhibit direct EUS activation, while a complete blockade can be applied
proximally (between the low-frequency stimulation site and the spinal cord), to inhibit
reflex EUS activation. However, reflex EUS activation may involve sacral root(s) other than
the one(s) stimulated by the low-frequency waveform. In such case, they should be blocked
as well. Anatomically, the lower urinary tract innervations are the same from one animal to
another but there is a functional variability. It is possible that one type of EUS activation
mechanisms is dominant. For illustration purposes, Fig. 1 shows all possible blockade sites,
but it is also possible that one blockade site prove to be sufficient. In case of incomplete SCI,
conventional sacral nerve stimulation may lead to pain perception. Rhizotomy can be a way
to abolish the stimulation-evoked pain but will probably not be considered at the cost of
Biomedical Engineering Trends in Electronics, Communications and Software

82
losing important reflexes and sensations if still present. With the proposed stimulation
strategy, a complete proximal high-frequency blockade of sensory activity during low-
frequency stimulation can inhibit pain sensation as well. Polystim Lab. recently presented

preliminary results obtained with this strategy based on a dog model. Acute dog
experiments were carried out and EUS blockade has been achieved in 8 animals after spinal
cord transection (Mounaim et al. 2008; 2010). However, such experiments are not sufficient
to validate the strategy especially that spinal shock generally lasts several weeks after SCI.
Chronic experiments are mandatory in order to evaluate the long-term efficiency. This
obviously requires a custom implantable neurostimulator that implements the proposed
strategy, and will be capable of generating conventional stimulation waveforms as well as
high-frequency sinusoids simultaneously over multiple channels.
3. Discrete implantable neurostimulator
3.1 Neurostimulator architecture
The block diagram of Fig.2 illustrates the architecture of the implantable neurostimulator
UroStim8 dedicated to the new stimulation strategy. The neurostimulator has been designed
with commercially available off-the shelf components. The control unit is one of the latest
generation of Field Programmable Gate Arrays (FPGA) that presents advantageous low-
power and small-scale features (Igloo, ACTEL). This FPGA also offers an In-Sytem
Programming (ISP) feature that would allow (wired) subsequent code updates even after
encapsulation of the neurostimulator. Such option was not possible with anti-fuse FPGAs
used in previous prototypes (Ex, ACTEL) leading to the assembly of a new prototype for
each new code to be tested. With near-field inductive coupling of spiral antennas, energy
and data are wirelessly transmitted through the skin to the implanted stimulator using an
external controller. The inductive coupling frequency used in previous prototypes was 20
MHz, but to comply with the Industrial, Scientific and Medical (ISM) radio band, it is
reduced to 13.56 MHz. This frequency is chosen taking into account the coupling
attenuation through the skin tissues and the spiral inductors characteristics. The Power
Recovery stage rectifies and filters the inductive carrier signal to provide different regulated
power supplies to the stimulator. The Data Recovery stage demodulates the 600 kHz On-Off
Keying (OOK) modulated carrier to provide Manchester-coded data to the FPGA. As soon
as the inductive energy is present and the power supply sufficient, the FPGA starts
Manchester decoding to extract data at 300 Kbps and a synchronized clock at 300 kHz.
Transmission data frames are sent cyclically until the FPGA acknowledges that a valid one

is received without errors using a low power and short-range 1 kbps RF uplink at 433 MHz.
Depending on the received instruction and parameters, a specific mode is executed. This
could be a stimulation mode where one or multiple Stimulation Stages outputs can be
activated with chosen parameters, or a telemetry mode where impedance module and phase
of each electrode-nerve interface (ENI) can be measured at a chosen frequency. Even though
all stimulation stages are similar and can generate any waveform to a certain extent,
Stimulation Stage 1 is dedicated to the low-frequency pulse waveform while Stages 2 to 4
are dedicated to the high-frequency sinusoidal waveform. The stimulation frequency is
common to Stages 2 to 4 but the stimulation current amplitude can be adjusted
independently. The synchronized clock extracted from the Manchester-coded data was used
as a time base for stimuli generation in previous neurostimulators. However, this clock
suffers from time jitter due to inductive noise during data demodulation. Timing is very
New Neurostimulation Strategy and
Corresponding Implantable Device to Enhance Bladder Functions

83
important as for conventional biphasic stimulation for example, positive and negative
phases must have the same duration so that total charge injection into the ENI is null. The
oscillator in Fig. 2 is a low power component that brings a simple solution to this problem.
Frequency of oscillation is adjusted with one resistance and an internal divider setting. The
oscillator is activated for stimuli generation only and provides a stable clock of 300 kHz that
can be eventually increased or decreased (hardware modification, not through the FPGA)
depending on the available inductive power and the desired stimulation parameters.


Fig. 2. Architecture of the UroStim8 neurostimulator dedicated to the new strategy
3.2 Power and data recovery
The neurostimulator front-end is responsible for power and data recovery as shown in Fig.3.
Inductive energy transmitted by the external controller is recovered by the implanted
stimulator using a parallel LC network resonating at the same frequency. Inductance L is a

3-turn spiral antenna that is printed on a thin and flexible PCB with external diameter of less
than 4 cm and a trace width of 1 mm to reduce the series resistance. Capacitance C is made
of parallel combinations of ceramic NPO capacitors that offer high Q and high temperature
stability. The capacitors are also specified for 100 V in order to maintain acceptable values at
high voltages and high frequency. C
tune
is a miniature variable capacitor that allows fine
tuning of the resonant frequency to recover maximum energy with respect to the average
power consumption of the implant. The voltage across the resonating LC network is an
alternating signal that may exceed 60 V peak-to-peak in case of a high inductive coupling
and a weak load. This signal is rectified with diodes (D1, D2) and filtered with the capacitor
C
filter
which can be seen as the energy storage for the implant. Because of such high voltage,
this capacitor has been chosen with a compromise between voltage specification (50 V),
capacitance value (6.8 µF), and physical dimensions. When inductive coupling is suddenly
interrupted, reverse currents may occur, leading to negative voltages at the input of the first
regulator (Fig.3). Diode D4 protects the circuit from such situations.
As shown in Fig.3, three linear regulators provide different power supply voltages to the
neurostimulator. The first one is adjusted between 5 and 12 V for the supply of current
sources and the analog supply of CMOS switches in the Stimuli Stages (Fig.4). This regulator

Biomedical Engineering Trends in Electronics, Communications and Software

84
High input
voltage regulator
5 to 12V
LCC
tune

C
filter
D1
D4
D2
D3
T1
R1
LDO voltage
regulator 3.3V
LDO voltage
regulator 1.5V
Demodulated
Data
to Stimulation &
Monitoring
Stages
to FPGA I/O,
Oscillator &
remaining
components
to FPGA core

Fig. 3. Power and data recovery in UroStim8
can tolerate high input voltages up to 80 V. The second regulator provides 3.3 V that is the
main supply used by the FPGA Input/Outputs buffers, the DAC, the logic supply of CMOS
switches in the Stimuli Stages, and the remaining components. This regulator provides a
Power-OK (POK) signal that indicates to the FPGA that the 3.3V supply is available and
well regulated. No stimulation will be started unless the POK signal is high. Finally, the
third supply of 1.5 V is used by the FPGA core only to reduce its power consumption.

To protect the system from a high induced voltage, power recovery circuits use voltage
clipping, Zener diodes or shunt regulators (Schneider, 2001; Ba et al., 2002; Ba, 2004; Yunlei
& Jin, 2005; Balachandran & Barnett, 2006). In previous neurostimulators, a shunt regulator
was adjusted to be able to provide the required voltage supply in the worst case that is
maximum stimulator current consumption and minimum available inductive energy.
However, except in this case, it is not an efficient solution because the shunt regulator
simply short-cuts the excess current. With the high input voltage of the first regulator, there
is no need for voltage limiting, and the excess of inductive energy translates to voltage
instead of current. Voltage is indirectly limited by the maximum available inductive energy
and the minimum stimulator current consumption. Compared to the zener shunt regulator,
it is a more efficient solution that also allows recovering high voltage supply for stimulation
without using step-up DC/DC converters. For data recovery, the OOK demodulator is a
simple envelope detector which is implemented as an amplification of small variations
across diode D3 that is stacked in series between the rectifier diodes (D1, D2) and the
common ground. These variations are due to the carrier modulation and are amplified with
the NPN transistor T1 in a common-base configuration. A pull-up resistor R1 limits the
current when the demodulated data signal is low but also limits its rising time. The design
simplicity of this demodulator is the reason behind the choice of such modulation scheme
for data transmission. However, the OOK modulation turns-off the coupling carrier with a
duty cycle of around 50 % for each Manchester-coded bit. Consequently, inductive energy is
wasted because of the simultaneous data transfer. Now that an oscillator provides a stable
clock, the recovered clock is not needed anymore for stimuli generation. Thus, as soon as the
FPGA acknowledges to the external controller a valid transmission, the downlink data
transfer is stopped while keeping the inductive coupling. That way, more inductive energy
is available for stimulation or telemetry.
New Neurostimulation Strategy and
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85
3.3 Stimulation stages

UroStim8 neurostimulator has 4 stimulation stages. As presented in Fig.4, Stage 1 is
dedicated to the low-frequency pulse stimulation, offers 4 bipolar outputs, and includes an
8-bit Digital to Analog Converter (DAC), an Operational Amplifier (OpAmp) used as a
current source, as well as CMOS analog switches for biphasic stimulation and outputs
multiplexing.

DAC 1
Signal
Demultiplexer
Stage 1
4 bipolar
outputs
Signal
Demultiplexer
Amp1
Stage 2
4 bipolar
outputs
Stage 3
2 bipolar
outputs
Stage 4
2 bipolar
outputs
+
-
DAC 2
+
-
+

-
+
-
Signal
Demultiplexer
H-Bridge
Res1
ZERO4 UP4 DOWN4
UP3 DOWN3
UP2 DOWN2
UP1 DOWN1
ZERO3
ZERO2
ZERO1
SEL1
SEL2
SEL3 SEL4
Amp2
Res2
Res3
Res4
3.3 V 5 to 12 V 5 to 12 V Analog Supply 3.3 V Logic Supply
CMOS Analog Switches
Vin1-
Vout1
Vout2
Vout3
Vout4
Vin2-
Vin3-

Vin4-

Fig. 4. Stimulation stages in UroStim8
The four outputs of Stage 1 share the same frequency and can be activated individually or in
any combination. Even though meant for simultaneous stimulation, the four low-frequency
pulse outputs are sequentially activated with a small delay to avoid cumulative power
consumption load peaks. Thus, pulse amplitude can be programmed independently which
is important because the impedance of the cuff-electrodes may be different. Before each
stimulation pulse, the FPGA sends the amplitude code to the DAC that provides a
proportional voltage V
DAC
between 0 and a reference voltage of 1.2 V. This voltage is then
converted into current by the OpAmp and resistance Res1 that operates as a current source.
Constant current is injected into the nerve via CMOS analog switches that enables reversing
the current for biphasic stimulation. The stimulation current is equal to Istim=V
DAC1
/Res1,
as long as the OpAmp is not saturated. Resistance Res1 has been chosen equal to 600 Ω to
provide a maximum current of 2 mA (1.2 V/600 Ω). For an ENI impedance of 1 kΩ, a
voltage supply of 3.3 V would have been sufficient for the OpAmp. However, previous
chronic animal experiments proved that the ENI impedance may become higher than 4 kΩ
Biomedical Engineering Trends in Electronics, Communications and Software

86
leading to lower stimulation currents because of the OpAmp saturation. Hence, its voltage
supply can be increased up to 12 V so that a current of 2 mA could be injected into an ENI
impedance up to 5.4 kΩ. Stimulation Stages 2 to 4 share the same DAC that will generate the
sinusoidal waveform required for nerve conduction blockade. They offer 8 bipolar outputs
that are grouped according to the stimulation strategy (Fig.1). For the three groups of
outputs, the blockade amplitude can be adjusted independently through digital

potentiometers Res2 to 4. The stimulation stages are controlled by the FPGA similarly but
separately. Signals UP and DOWN sets the current direction with an H-Bridge that is made
of four switches mounted as a mixer. Signal ZERO controls a fifth switch that shortcuts the
OpAmp output with its negative input before activating one of the UP or DOWN signals.
That way, before and after each pulse, the same voltage is applied on both electrodes (of
each bipolar output) before releasing the ZERO switch (Mounaim & Sawan, 2007). The
output CMOS analog switches are critical elements. If they must transmit currents under
voltages as high as 12 V, they still need to be controlled by 3.3 V signals directly from the
FPGA. Thus, they have been chosen with dual power supplies: a logic supply of 3.3 V and
an analog supply up to 12 V.
3.4 Telemetry
The goal of the implemented telemetry is to verify the capacity of the implant to stimulate
each connected nerve. Thus, it is important to monitor the load impedance presented by
each ENI as it must not be too high for the desired stimulation current (Sawan et al., 2007,
2008a).

Signal
Multiplexer
+
-
ADC
Logic Supply
3.3 V
Analog Supply
5 to 12 V
3.3 V
Limiter
Vout2
Vin2-
Vout3

Vin3-
Vout4
Vin4-
Vin1-
Vout1
3.3 V
IA
Control
Unit
FPGA
SEL5
Res5
C
driving
current
+
-
driving
voltage
TX Module

Fig. 5. Telemetry in UroStim8
The neurostimulator has a total of 12 bipolar outputs. Making use of the demultiplexers
already present in the stimulation stages, monitoring can be done at the current source
OpAmp output of each stage by activating one single bipolar output at a time. As shown in
Fig.5, the four differential OpAmp outputs voltages are multiplexed, differentially
measured with an instrumentation amplifier and then sampled with an Analog to Digital
Converter (ADC) before being sent to the FPGA. The stimulus used for AC impedance
measurement is a sinusoidal waveform that each stimulation stage is capable of generating.
After a programmable number of cycles, the maximum amplitude and zero-crossing time of

the voltage difference across the ENI, are used with the programmed stimulation
parameters to estimate the impedance module and phase respectively. Once these
measurements are ready, they are sent to the external controller thanks to a miniature
transmission module. It is an RF emitter oscillating at 433 MHz and OOK modulated at 1
kHz. The transmission range can be adjusted with a digital potentiometer (Res5) that limits
the driving current.
New Neurostimulation Strategy and
Corresponding Implantable Device to Enhance Bladder Functions

87
5. Results
The complete UroStim8 neurostimulator prototype has been assembled on a large
breadboard for design and tests. Table 1 presents the achieved stimulation parameters and
Fig. 6 presents different oscilloscope screen captures. Fig. 6a shows the low-frequency pulse
stimulation waveform generated by Stimulation Stage 1. Single-end outputs are probed by
oscilloscope channels Ch1 and Ch2 respectively. The differential output (Ch1-Ch2) is shown
by the Math curve (M). Control signals ZERO1 and UP1 (according to Fig. 4) are probed by
channels Ch3 and Ch4 respectively. The waveform is not a conventional biphasic one but
rather an alternating monophasic waveform as proposed in (Mounaim & Sawan, 2007). Fig.
6b shows the Stimulation Stage 1 OpAmp's output Vout1 (Ch1) when all four bipolar
outputs are activated. Ch2 to 4 probe three of them (single-ends only). Stimulation on the
four outputs is not "truly" simultaneous but rather alternated with a small delay between
pulses. This has the advantage of avoiding large current consumption peaks but also
allowing different pulse amplitudes for each output. Fig. 6c and 6d show the high-frequency
sinusoidal waveform at the minimum and maximum achieved frequencies respectively. For
both figures, single-end outputs are probed by Ch1 and Ch2, control signals UP and DOWN
(according to Fig. 4) by channels Ch3 and Ch4 respectively, while the differential output is
shown by the Math curve (M).

Waveform Pulse Sinusoid

Parameters Amp. Width Frequency Frequency Amp.
Max
2 mA 217 µs
8.9 kHz (with min
width)
1 kHz (with max width)
8.6 kHz 2 mA
Min
0 3.39 µs 18 Hz 1 kHz 0
Resolution
8 µA Time resolution = 3.39 µs (clock = 295 kHz) 8 µA
Table 1. UroStim8 measured stimulation parameters
A normalized half-period of the waveform is stored as a map table of 1024 amplitude
samples. To change the frequency of stimulation, the map table is read with a memory
address step as it is scanned with the 300 kHz clock. The general equation determining the
digitally programmed sinusoidal frequency is given by equation (1).

1
21024
300 5
7
Frequency kHz
F
−
∗
⎛⎞
=∗ +
⎜⎟
+
⎝⎠

(1)
where F is the decimal equivalent of a programmable 6-bit binary code. As the frequency is
increased, the resulting total number of amplitude steps is reduced from more than 256
(=2*1024/8) to less than 32 (=2*1024/64). Any other stimulation waveform and/or mapping
strategy can be easily implemented by reprogramming the FPGA. Table 2 presents the
measured system total current consumption at different conditions. With all stimulation
stages and all their outputs activated, total system current consumption is 4.54 mA (rms) at
30 Hz pulse (2 mA, 217 µs) and 1 kHz sinusoidal frequencies. For Stimulation Stages 2-4, 1
mA current is distributed over outputs of each stage. Thus, stimulation parameters must be
adjusted taking into account the available inductive power energy. The FPGA core current
consumption in this prototype is less than 100 µA.
Biomedical Engineering Trends in Electronics, Communications and Software

88

Stim. Stage 1
single-end outputs
Stim. Stage 1
OpAmp output
(Vout1)

(a) (b)
UP
DOWN
single-end
outputs
differential output (Ch1 – Ch2)


(c) (d)

Fig. 6. Oscilloscope captures showing (a) alternating monophasic stimulation waveform and
control signals, (b) Stimulation Stage 1 OpAmp output and three single-ends outputs, and
sinusoidal waveform at (c) 1 kHz and (d) 8.6 kHz frequencies

Conditions Current consumption
Stimulation Stage 1 Stimulation Stages 2-4 mA (rms)
OFF OFF 1.83
30 Hz OFF 2.12
1 kHz OFF 4.59
30 Hz 1 kHz 4.54
30 Hz 8.6 kHz 5.33
1 kHz 8.6 kHz 7.80
Table 2. UroStim8 measured system total current consumption (rms) with following
stimulation conditions: Stage 1 (2 mA, 217 µs); Stages 2-4 (1 mA each, current is distributed
over outputs of each stage)
UroStim8 neurostimulator’s printed circuit board have been designed, fabricated and
assembled as shown in Fig. 7. UroStim8's PCB is 38 mm diameter and can host a FPGA in
New Neurostimulation Strategy and
Corresponding Implantable Device to Enhance Bladder Functions

89
12x12 Fine Pitch Ball Grid Array (FBGA) of 13x13 mm dimensions and 1 mm pitch. Because
of the relatively large number of discrete components and the limited space, the design of
such PCB is challenging. It required eight PCB layers and numerous blind vias for a
complete routing of the system. For chronic animal implantation, the prototype will be
encapsulated in two layers of different materials. The first layer is a rigid epoxy that protects
the implant from infiltration of fluids and offers a reliable isolation for the electronic
components. The second layer is a biocompatible silicone that offers a soft contact for
corporal tissues. Encapsulation is done using custom made Teflon or aluminum moulds.
Fig. 8 shows the targeted encapsulation dimensions for the neurostimulator. The

encapsulated UroStim8 will be thinner than previous prototypes that had embedded
batteries (10 mm compared to 16 mm).

Top view Bottom view Inductor



Fig. 7. UroStim8 printed circuit board

40 mm10 mm
59 mm

UroStim8
Fig. 8. UroStim8 encapsulation dimensions
6. Conclusion
This chapter presented a new sacral neurostimulation strategy to enhance micturition in
spinal cord injured patients. In order to carry-on chronic animal experiments, a discrete
Biomedical Engineering Trends in Electronics, Communications and Software

90
implantable neurostimulator has been designed implementing the proposed stimulation
strategy and using commercially available discrete components. Measurements and
prototyping results were presented. The discrete prototype is capable of generating a low
frequency pulse waveform as low as 18 Hz with a simultaneous high frequency alternating
waveform as high as 8.6 kHz, and that over different and multiple channels. With all
stimulation stages and all their outputs activated, total system current consumption is
around 4.5 mA (rms) at 30 Hz pulse (2 mA, 217 µs) and 1 kHz sinusoidal frequencies. In the
same conditions, using a sinusoidal stimulation at the highest frequency of 8.6 kHz,
increases current consumption up to 7.8 mA. With 50 mW of available inductive power for
example and 4.5 mA current consumption, the high voltage regulator can be set to 10 V

allowing 2 mA stimulation of 4.4 kΩ electrode-nerve impedance. However, with 7.8mA
current consumption, the high voltage regulator will have to be set to 6 V reducing the
maximum possible stimulation current to 1 mA for a 4.4 kΩ electrode-nerve impedance.
Thus, the effective number of activated outputs and the maximum achievable stimulation
parameters are limited by the available energy provided by the inductive link and the
impedance of the electrode-nerve interfaces. Future developments will include chronic
animal experiments after full characterization of the encapsulated and implanted
neurostimulation prototype, taking into account the resulting inductive link efficiency.
7. Acknowledgement
Authors would like to acknowledge the financial support from the Natural Sciences and
Engineering Research Council of Canada (NSERC), the Mycrosystems Strategic Alliance of
Quebec (ReSMiQ), and the Canada Research Chair on Smart Medical Devices. Also, thanks
are due to all Polystim’s members and students that have participated in the design of the
UroStim8 prototype and to Laurent Mouden for its assembly.
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6
Implementation of Microsensor Interface for
Biomonitoring of Human Cognitive Processes
E. Vavrinsky

1
, P. Solarikova
2
, V. Stopjakova
1
, V. Tvarozek
1
and I. Brezina
2
,
1
Department of Microelectronics, Slovak University of Technology,
2
Department of Psychology, Comenius University,
Slovakia
1. Introduction
Miniaturization of biomedical sensors has increased the importance of microsystem
technology in medical applications, particularly microelectronics and micromachining. This
work presents a new approach to biomedical monitoring and analysis of selected human
cognitive processes. The system is based on our preliminary described theory and
experiments (Vavrinsky et al. 2010). We are primarily interested in biomonitoring of human
cognitive processes and psychophysiological conditions of car drivers in order to enhance
road safety.
Actually often used method is evaluation of abnormal car driver actions (sudden changes of
direction with no direction indicators or too hard cornering). Main disadvantage of such a
system is that they offer no prediction. More effective are prediction systems, which offer
enough reaction time before undesirable situations, and so they can minimize human error
factors and improve road-traffic safety.
Our present research is focused on sensing, processing and analysis of selected
physiological signals for mental and medical condition recognition. They are known some

studies describing interface between emotional condition and physiological responses, and
we want also present some, since new ideas and research in psychological recognition and
biomonitoring are very welcome. It is also proved that human decisions and reactions are
affected by emotional and physical comfort. Emotional reconnoiter of a car driver conditions
is influenced by many cognitive processes, such as mind organization, vigilance, planning
or fatigue. Nervous and angry people can be very dangerous for traffic road safety.
In our experiments, we have monitored:
- psycho-galvanic reflex (PGR) – skin conductivity changes,
- heart rate + electrocardiogram (ECG),
- body temperature,
- respiration frequency,
- emotions.
To improve the reliability of our measurements, these parameters have been monitored
often by duplicate methods, sometimes at macro level, sometimes by local microsystems
technologies. In first step, we implemented our technology to the virtual reality driving
simulator but preparations for real implementation have been already started, and the final
car implementation will follow.