An Instrumentation Design for Microsignal Output From Piezoresistive Microcantilever Biosensor For

Human Stress

Lee Yoot Khuan1 1Faculty of Electrical Engineering

Universiti Teknologi MARA Malaysia

leeyootkhuan@salam.uitm.edu.my

Mohd Ismarul A. Ismai11, Abdul Razak M S. Hamid1, Ilham Rustam1, Maureen S. A. Bujang1, Mohd Firdaus

Abdullah1, Nina Korlina Madzhi1, Anuar Ahmad2 2Faculty of Electrical Engineering

Universiti Selangor Malaysia Malaysia

Abstract— This paper concerns the development of a potentiometric instrumentation circuit for integration with a PZR microcantilever biosensor to detect human stress. It embodies the design of transduction, filtering, stabilization and linearization of micro-signals from a biosensor. The sensing principle is based on immobilization of the bio-receptor to produce a bio-chemical reaction. The novel biosensor integrated with the potentiometric instrumentation converts this biochemical event into a measurable electrical signal. Input is the level of salivary alpha amylase activity corresponds to human stress level. The first phase transduces the changes in enzymatic reaction into resistivity. The subsequent stages condition the signal to attain an output of 0-5V from a potentiometric input of 108.6 to -100mV, equivalent to sensor resistivity range of 1.2-1.3kΩ.. The design for filtering phase employs a first order high and low pass filter to capture stress signals, known with frequency between 0.15-0.4Hz. Performance of the circuit is evaluated with simulation and experimental study. On the average, discrepancy less than 3.69%, 4.5% and 3.7% is found between simulation and experimental results for the transduction, filtering and linearization phase respectively.

Keywords- Piezoresistive Microcantilever Biosensor, Potenti-ometric, Salivary Alpha Amylase, Stress

I. INTRODUCTION Stress is one inability to adjust to changes, resulting in

reactions such as quickened heart rate, tensed muscle, rising blood pressure and dilating pupil [1-2]. Stress measurement focuses on two major physiologic responses exhibit in the autonomic nervous system, i.e. the sympathetic adrenal medullary system (SAM) and hypo-thalamus pituitary adrenocorticol system (HPA) [3]. Heart rate and blood pressure have been widely used as indices to measure human psychologic stress for they can be easily and promptly measured. However, heart rate cannot be used to distinguish between eustress and distress, markedly affected by homeostasis and its changes are not large relative to its normal values [1]. Studies have shown that the amount of alpha amylase (SAA), an enzyme secreted by the salivary glands, can be related to the "fight or flight" response in SAM system to stress. It has been proven that the increase in SAA is more significant and reaction is more rapid than that of cortisol when

experimented with psychologic stressor, suggesting that it is a better index of stress [4]. In addition, from a measurement profile of physio- and salivary-based biomarkers obtained from a preliminary study conducted with five healthy volunteers performing the treadmill-based Ellestad protocol, it is found that the SAA or the changes in it, is relatively more significant in reflecting the stress state than other biomarkers such as heart rate, systolic and diastolic blood pressure. Besides, it is also found to have strong association with mood state with a correlation coefficient of 0.8 and significance of 0.01 [5].

Micro-Electro-Mechanical Systems (MEMS) has proven its importance in the design of micro-sensors. Microcantilever is one of such structures, which has been applied as a biosensor to monitor blood glucose [6], hybridization of DNA [7] etc. The use of piezoresistive (PZR) microcantilever biosensor for the measurement of human stress based on salivary amylase enzyme is new [8-9]. Biosensor uses biological materials in collaboration with appropriate instrumentation to determine the substrate in sample. The biological sensing element responds to the analyte being measured and the transducer converts this observed change into a measurable signal that is proportional to the concentration of the analyte [10]. They are easy-to-use, cheap and highly sensitive methods for recognition of biomolecules [11-12].

In this paper, development of a potentiometric instrumentation circuit, designed specifically to integrate with the PZR microcantilever to innovate a measurement method for human stress is re-ported. Potentiometric transduction measures a voltage by opposing to it a known potential drop by passing a known current through a resistor of known characteristics, which is the PZR microcantilever biosensor here. It has the advantage that their supply parameters do not affect their metrological characteristics. Design for the 3 phases of instrumentation: transduction, filtering and linearization, is first described. Then, testing on each of the phases to show how specification is satisfied is discussed.

II. METHODOLOGY

The 2011 Biomedical Engineering International Conference (BMEiCON-2011)

978-1-4577-2190-8/11/$26.00©2011 IEEE

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Fig. 1 summarises the architecture design of the potentiometric circuit, which will be discussed in the following sub-sections.

A. PZR MicroCantilever Biosensor In our work, a microcantilever is employed to detect the

human stress. The microcantilever embeds a layer of PZR polysilicon material on its top surface. When the analyte is adsorbed by its receptor molecules on the functionalized surface of the microcantilever, the surface properties change, which causes it to deflect (see Fig. 1). The degree of deflection depends on the concentration of the analyte. Accompanied with the deflection is a change in stress which induces a change in the strain of the piezoresistor, thereby producing a change in the resistance [8-9, 13].

Figure. 1. Architecture design of potentiometric instrumentation circuit

B. Transduction Phase Having the PZR microcantilever biosensor converting the

deflection into resistivity, the amount of biochemical molecular adsorption on its top surface can be quantified. The PZR microcantilever biosensor is designated as an arm in a Wheatstone bridge configuration as in Fig. 2. Whenever the PZR microcantilever deflects, the stress-strain change in the piezoresistor causes a change in the resistance of R3 and hence produces a differential voltage. The deflection-balanced bridge method is adopted to determine this change, based on the amount of bridge imbalance [14]. However, at initialization, the null balanced bridge method is used, where R2 is adjusted until zero differential voltage and current is obtained, and the ratio of (R4/R3) equals the ratio of (R2/R1). When the null balanced bridge is disrupted with the change in enzyme concentration corresponding to human stress, the differential voltage is fed to a differential amplifier with two separate inputs, one inverting (Vin-) while the other non-inverting, (Vin+), of the same voltage gain but a common output terminal. Vin- is the measured signal from the PZR microcantilever biosensor while Vin+ is the output voltage between R1 and R2. The two voltages are summed and amplified by differential gain of the amplifier [14]. Op-27 is used for its low-noise with high precision performance. DC supply is at �12V. C1, C2, R9 and R10 stabilize the circuit, especially when the same supply is shared among few phases.

R1

R2 R4

R5

R8

R7

R6

R9

R10U1

OP-27

+3

-2

V+7

V-4

OUT 6OS1

1

OS2 8

R3

C1

C2

VCC

V-

V+

Vin-Vin+

Fig. 2. Transduction phase of potentiometric instrumentation for a PZR

microcantilever biosensor

C. Filtering Phase It is in this phase that signals frequency between 0.15-

0.4Hz, typical that of human stress signals [15], are retained, using a first order low and high pass filter. The low pass filter is used to eliminate frequency higher than 0.4Hz and noise interruption. It is usually configured as an inverting amplifier, popularly known as active low pass filter. The circuit passes the low frequency signals to its output gain but attenuates signal of higher frequencies [16-17]. Design for the low pass filter is shown in Fig. 3.

R2

0U2

OP-27

+3

-2

V+7

V-4

OUT 6OS1

1

OS2 8

R4

R3

C1

C2

V4

12Vdc

V5

12Vdc

0

0

R1

C3

e

Ou

Fig. 3. First order low pass filter

The frequency at which the gain starts to decrease is controlled by C3 and R2. The cut off frequency, 0.4Hz, defines the end of the passband and is normally specified at the point where the response drops -3dB from the passband response. The high pass filter is made of a first order low pass filter by interchanging component Ri and Ci [18] as in Fig. 4.

U3

+3

-2

V+7

V-4

OUT6

OS1 1

OS2 8

Rf

Ri

V712Vdc

V812Vdc

Output Voltage

Input Voltage

Ci

0

0

0

Fig. 4. First order high pass filter

PZR Microcantilever Deflection

27

It is the inverse of the low pass filter with frequency response mirror that of the latter. The filter is de-signed to accommodate signals higher than 0.15Hz, the cutoff frequency, which is the frequency at which the magnitude of the gain is 0.707 times its pass band value.

D. Voltage Follower It functions like a unity gain amplifier since its output

always tracking input to eliminate loading effects and interface impedances. The interposed buffer amplifier prevents the second circuit from loading the first circuit unacceptably and interfering with its desired operation [20].

0

U7

OP-27

+3

-2

V+7

V-4

OUT 6

OS11

OS2 8

V612Vdc

V712Vdc

0

Input v oltage

Output Voltage

Fig. 5. Voltage follower phase

E. Linearization Phase

In this phase, the basic circuit is modified so that the output approximates linear function of its input. Here, output range of volt-age follower is 0.1086 to -0.1V, yet a range of 0-5V is required of interface to an analog-to-digital converter. Through linearization, the following transfer function is obtained with a gain of about 24 and a bias of 2.603.

603.297.235 +−= VinVo (1)

Fig. 6 shows the schematic circuit that satisfies (1). It makes use of two operational amplifiers, OP1 operates in the non-inverting mode while OP2 as a simple differential amplifier, and a resistor network. OP1 is chosen to approximate the gain of -23.97. Resistor value for R5 and R6 are computed to obtain the exact gain. It is necessary to feed back part of Vo4 to negative input of op-amp via R6. A variable resistor R11 is selected to set the offset voltage. It is adjusted until bias reaches exactly 2.603V at the point of Voff (R7=R8=R9=R10), to compensate for loading of the divider by op-amp and variation in the supply from the exact 5V.

Vo1

0

0

R11SET = 0.5Vdc

0

Vo2Input v oltage

OP1

+3

-2

V+

7V-

4

OUT6

OS11

OS28

OP2

+3

-2

V+

7V

-4

OUT6

OS11

OS28

R5

R6

R7

R8

R9R10

Voff

V1312Vdc

0

V1712Vdc

0

V1812Vdc

0

0

V1412Vdc

Fig. 6. Linearization phase

III. RESULT AND DISCUSSION Design for the potentiometric instrumentation circuit was

initiated with theoretical simulation and implemented for experimental study.

The software design and simulation was performed on OrCAD Capture CIS 15.7. The bias point and DC sweep simulation mode is adopted. The Bias Voltage Display and Bias Current Display function enable the voltage and current at any point to be tapped for results as shown in Fig. 7-10.

TABLE 1 NULLING of WHEATSTOEN BRIDGE R2

(k ) Vo (mV)

Simulation Vo (mV)

Experiment Discrepancy

(%) 1.2 0 0.5 -

1.204 4.122 4.20 -0.96 1.21 10.28 10.90 -5.11

1.214 14.37 14.900 -2.83 1.22 20.48 21.10 -2.13

As mentioned in Section IIB, the null balanced bridge results when ratio of resistance on the left and right arm equals, by adjusting R2. It has been found that the PZR microcantilever biosensor with boron implanted from surface micromachining fabrication has a resistance of about 1.2kΩ [9], at which the Wheatstone bridge is balanced. Table I shows the voltage output about the null bridge condition. It is observed that the null bridge condition is obtained when R2 equals 1.2kΩ. Average discrepancy is 4.54% between simulation and experimental study, which could be attributed to tolerances of electronic components and wiring.

Fig. 7 shows voltage output from differential amplifier of the transduction stage linearly related to the resistance of biosensor. The offset rate at null bridge condition is 10mV/Ω. Variation in PZR biosensor is selected between 1.1-1.3k kΩ, cited from our previous works [8-9, 13]. Corresponding output from the differential amplifier is between -100 to 100mV. The null bridge condition is observed at 1.2k kΩ, concordant with finding in Table 1. A discrepancy of 3.69% on the average is found between simulation and experiment.

Fig. 7. Simulation and experimental study for transduction phase

Theoretical calculation for designing the filter with unity gain yields a passband of 0.142-0.458Hz, with discrepancy of -4.23% and -0.43% (see Table 2), due to the capacitor and resistor values. It is necessary for the component value of choice to be realistic for design on circuit board to materialize.

Fig. 8 depicts the outcome from filtering-voltage follower. As observed, the magnitude and polarity of the output voltage follow those of input. On the average, a discrepancy of 4.5% is noted between the simulation and experimental study.

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Fig. 8. Simulation and experimental study for filtering-follower

TABLE 2 FILTER DESIGN Cut-off frequency (Hz) High Low

Theory 0.142 0.458 Simulation 0.148 0.46

Discrepancy (%) -4.23% -0.43% The output voltage from the linearization phase is shown in

Fig. 9, which displays clearly the voltage has been conditioned to 0-5V linearly.

Fig. 9. Simulation and experimental study for linearization phase

The simulation and experimental result from linearisation-voltage follower is presented in Fig. 10. It is found that the magnitude and the polarity of the output voltage follow the input. An average discrepancy of 3.7% is observed between the results.

Fig. 10. Simulation and experimental study for linearisation-follower

IV. CONCLUSION A potentiometric instrumentation for a piezoresistive

microcantilever biosensor to detect human stress is developed. The circuit has been designed and implemented with simulation and experimental study. The transduction phase enables small change in resistivity due to enzymatic reaction to be detected. It has been found that for a biosensor input of 1.2-1.3k kΩ, an output of -100 to +100mV is obtained. The filtering phase provides a passband of 0.148Hz-0.46Hz, populating the human stress signals. It is found that output from filter-voltage follower lies between 111.74 to -97.47mV. The linearization phase linearly augments the output to -0.145V to 5.15V to meet the requirement to interface with an analog-to-digital converter. On the average, a discrepancy less than 3.69%, 4.5% and 3.7% is found between simulation and experimental results for the three phases respectively.

ACKNOWLEDGMENT The authors would like to acknowledge the Minister of

Science, Technology and Innovation (MOSTEE), Malaysia and Dana Cemerlang 600-RMI/ST/DANA 5/3/Dst (33/2009), Universiti Teknologi MARA, Malaysia for their financial and equipment assistant for the project research.

REFERENCES

[1] M.Yamaguchi, T.Kenemori, M.Kanemeru, N.Takai, Y.Mizuno and H.Yoshida, "Performance Evaluation of salivary amylase activity monitor," Biosensor and Bioelectronics, vol. 20, pp. 491-497, 2004.

[2] M.H.Lee, H.K.Lee and S.Bang, "Development Stress monitoring System based on Personal Digital Assistant," 26th Annual Interna-tional Conference of the IEEE EMBS, 2004.

[3] G.C.H.D.Bond, "Lifestyle-Specific Outcome Measures," The Interna-tional Electronic Journal of Health Education, pp. 159-168, 2000.

[4] N.Takai, T.Aragaki, K.Eto, K.Uchihashi and Y.Nishikawa, "Effect of psychological stress on the salivary cortisol and amylase levels in healthy young adults," Achieves of Oral Biology, pp. 963-968, 2004.

[5] L.Y.Khuan, A.Zaaba, N.K.Madzhi and A.Ahmad, “A study into Salivary-based Measurement of Human Stress Subjected to Ellestad Stress Test Protocol,” 31st International IEEE EMBS Conference, USA, 2009

[6] H.Kudo, T.Sawada, E.Kazawa, H.Yoshida, Y.Iwasaki and K.Mitsubayashi, "A flexible and wearable glucose sensor based on functional polymers with Soft-MEMS techniques," Biosensors and Bioelectronics, vol. 22, pp. 558-562, 2006.

[7] E.H.L.J.J.Gau, B.Dunn, C.M.Ho, J.C.S.Woo, " A MEMS based amperometric detector for E. Coli bacteria using self-assembled mo-nolayers," Biosensor and Bioelectronics, vol. 16, pp. 745-755, 2001.

[8] N.K.Madzhi, A.Ahmad, L.Y.Khuan and R.A. Rani, “Polysilicon based Piezoresistive microcantilever for Biological Sensing,” 17th Conference on Composites and Nano Engineering (ICCE 17), 2009.

[9] N.K.Madzhi, A.Ahmad, L.Y.Khuan, R.A.Rani and F.Abdullah, "Design and Fabrication of Polysilicon-Based Piezoresistive Micro-cantilever for Biological Sensing," International Conference on Na-noScience and NanoTechnology, 2008.

[10] E.M.Boujamaa, Y.Soulie, F.Mailly, L.Latorre and P.Nouet, "Rejec-tion of Power Supply Noise in Wheatstone Bridges:Application to Piezoresistive MEMS," DTIP of MEMS and MOEMS, France, 2008..

[11] B.Wongkittisukka, C.Limsakul, P.Thavarungkul, P.Kanatharana and P.Asawatreratanaku, "A Conductimetric Transducer for Biosensor System," NECTEC, vol. 4, pp. 369-374.

[12] M.A.Bhatti, L.Y.Zhong and A.N.Abdalla, "Design and Finite Element Analysis of Piezoresistive Cantilever with Stress Concentration Holes," 2007.

[13] N.K.Madzhi, A.Ahmad, L.Y.Khuan and R.A.Rani, "Design Simula-tion and Finite Element Analysis of Piezoresistive Microcantilever for Human Stress Measurement," Journal of Electrical and Electronic Systems Research, FKE, UiTM, June 2009.

[14] J. G. Webster, "Bridge Circuit," Medical Instrumentation, 3rd edition, USA: Wiley, pp.51-53, 1998.

[15] S.Cerutti, A.M.Bianchi and H.Reiter, "Analysis of sleep and stress profiles from biomedical signal processing in wearable devices," 28th International Conference of the IEEE-EMBS, pp.6530-6532, 2006.

[16] D.A.Bell, "Active Filter," Operational Amplifier, Application, Trou-bleshooting and Design, 1st edition, USA: Prentice Hall, pp.259-288, 1990.

[17] J.J.Carr, "Loop Filter Circuits," Integrated Electronics:Operational Amplifier and Linear ICs with application, 1st edition, USA: Hab-court Brace Jovanovich, pp.484-485, 1990.

[18] H.S.Kalsi, "Filters," Electronic Instrumentation, New Delhi: Tata McGraw-Hill, pp.500-534, 2004.

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[19] D.A.Bell, "Introduction to operational Amplifier," Operational Am-plifier, Application, Troubleshooting and Design, 1st edition, USA: Prentice Hall, pp.1-20, 1990.

[20] T.L.Floyd and D.Buchla, "Voltage Follower," Op-Amp Configura-tions with Negative Feedback, 2nd edition, USA: Prentice Hall, pp.70-71, 1999.

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