field programmable gate array based embedded system for non ...

INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS VOL. 6, NO. 3, JUNE 2013

FIELD PROGRAMMABLE GATE ARRAY BASED EMBEDDED SYSTEM FOR NON-INVASIVE ESTIMATION OF HEMOGLOBIN IN BLOOD USING PHOTOPLETHYSMOGRAPHY Chetan Sharma1, SachinKumar2 ,Anshul Bhargava3,Shubhajit Roy Chowdhury4 1,2,3,4

Center for VLSI and Embedded Systems Technology, IIIT Hyderabad, India

Emails: [email protected], [email protected], 3

[email protected],[email protected]

Submitted: Mar.21, 2013

Accepted: May 17, 2013

Published: June 5, 2013

Abstract - In this paper, a Field Programmable Gate Array (FPGA) based embedded system has been proposed for

non-invasive detection of hemoglobin in blood

using photoplethysmography.

Photoplethysmography (PPG) is a non-invasive and low-cost optical technique that can be used to detect blood volume changes in the micro-vascular bed of tissue. Our investigations revealed that volume of water present in blood considerably affects the reading of the concentration of blood hemoglobin. In our current work, the proposed device is developed with monitoring of PPG waves at three wavelengths so as

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Chetan Sharma, Sachin Kumar, Anshul Bhargava, and Shubhajit Roy Chowdhury, FIELD PROGRAMMABLE GATE ARRAY BASED EMBEDDED SYSTEM FOR NON-INVASIVE ESTIMATION OF HEMOGLOBIN IN BLOOD USING PHOTOPLETHYSMOGRAPHY

to compensate for the error due to absorption of near infrared radiation (NIR) by water in the blood. The device also has been developed to be robust enough to tolerate distortions in the waveform due to motion of the subject. In order to sense the motion of the subject a 3-axis accelerometer has been used and correction is suitably applied to the sensed waveform. As a result, it is possible to measure the concentration of hemoglobin in blood while the subject is on the move.

Index Terms: Field Programmable Gate Array, photoplethysmography, non invasive estimation, near infra red radiation

I.

INTRODUCTION

Hemoglobin is a metal protein present in the red blood cells of blood plasma. It is the component which facilitates the transport of oxygen from alveoli of lungs to the body cells, and of carbon dioxide from body cells back to the alveoli. It is present in the form of four compounds in blood, out of which Oxy Hemoglobin and Deoxy Hemoglobin are present in significant concentrations. Since it is a vital ingredient of blood which is necessary for respiratory processes, continuous monitoring of blood hemoglobin concentrations is necessary for monitoring health status of individuals, especially for pregnant women, anemic patients and newborn babies. Present day technology of detecting hemoglobin in blood requires invasive method, in which the finger of subject is pricked and a few drops of blood are taken out as sample. This technique, though prevalent and popular across the world, has several disadvantages too. There is always a possibility of spread of infection through the needles, since they are exposed to the surroundings and get to come in direct contact with blood. The chances of such an infection increase manifold in rainy season and in rural areas where cleanliness standards are not up to the mark. Infections may also spread if the needle is not properly sterilized before use, or is used on more than one subject. The above mentioned technique also involves the use of special chemicals and apparatus for testing. With the advancements in technology, chemical processes and calculations to determine hemoglobin concentration in blood are being done using machines which require trained operators. All these factors make it difficult to incorporate this facility in rural health centers and remote areas which suffer from lack of skill and funds. As a result, cases of anemia in pregnant women

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and newborn children go undetected in rural areas, leading to high foetal-maternal death rate during pregnancy and childbirth. As an advancement in the field of pathology, numerous non-invasive techniques such as imaging [1-3], spectro-photometry [4,5], opto-acoustic spectroscopy [6-8], transmission spectroscopy [9,10], reflection spectroscopy [11,12] have been proposed in literature for the estimation of blood parameter . In this paper, an FPGA based implementation of a non-invasive device has been for the estimation of hemoglobin in blood, which is low power, cheap and portable. It is based on the principle of Photoplethysmography (PPG). Photoplyethysmography is a technique of estimation of blood components, which is based on the principle of partial absorption of light by the blood components. It works by detecting the blood volume changes in the micro-vascular bed of tissue, and the sampling is done in places where the tissue is close to the skin: such as finger tips, earlobes and finger webbings. The Photoplethysmography (PPG) waveform is a pulsatile which has both AC and DC parts. While the DC part is due to less varying physiological factors like respiration, Sympathetic Nervous System (SNS) activity and thermoregulation; the AC part is due to the rapidly varying volume in the arteries of blood. During the systolic phase, the arteries expand in diameter and the volume of blood in them increases. During the diastolic phase, the diameter of arteries decreases and hence the volume blood in them also decreases. This change of blood volume (twice in a heartbeat) is detected using infrared radiation, giving the characteristic PPG waveform. In the current research, a Field Programmable Gate Array (FPGA) based embedded system has been proposed for non-invasive detection of hemoglobin in blood using photoplethysmography. Our investigations revealed that volume of water present in blood considerably affects the reading of the concentration of blood hemoglobin. In our current work, the proposed device is developed with monitoring of PPG waves at three wavelengths so as to compensate for the error due to absorption of near infrared radiation (NIR) by water in the blood. The device also has been developed to be robust enough to tolerate distortions in the waveform due to motion of the subject. The paper is organized as follows. Section II focuses on the detection of blood parameters by photoplethysmography. In section III, active motion artifact cancellation of PPG waves has been

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discussed. Section IV focuses on the implementation of the electronic device. The results and discussions have been presented in section V.

II.

Detection of blood parameters by photoplethysmography :

The principle of detection of blood parameters by photoplethysmography is based on differential absorption of Infrared/Non-Infrared radiation by different blood components present in the blood of capillaries, when the subject’s finger is exposed to IR/NIR radiation. The absorbance of a particular blood component at a particular wavelength as obtained by Beer-Lambert’s law can be given by, A= €cL, …………………………………………...……………………………………..........…..(1) where A= absorbance caused by solute, € = molar absorptivity ofsolute, c=concentration of solute in solution, L=path length. However, in case of blood there are multiple components. For n different components, the total absorbance is given by: A= ∑

€(n)c(n)L…………………….……………………………………………………………(2)

where €(n)= molar absorptivity of nth solute, c(n)=concentration of nth solute in the solution Thus, the concentration of the solute can easily be calculated by calibrating the values of path lengthL, absorbance A and molar absorptivity €at a particular wavelength λ. Also, the absorbance is related with transmittance as follows: A= log(Io/I),………………………………………………………………………………………(3) where Io is the intensity of transmitted light, I is the intensity of received light.

So from the above equations, we get the relation between transmitted intensity and received intensity for a particular wavelength i.e.I = Io exp (-εLc)

(for 1 component only). Figure 1

shows the The spectral absorptivity of blood components hemoglobin, oxyhemoglobin, and water are as shown in Fig. 1, Fig. 2 and Fig.3 respectively. These graphs indicate that to discard significant absorbance melanin and water, the wavelength of IR light must be greater than 800nm

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but less than 1000nm. In between this range, the spectral absorptivities of hemoglobin and oxyhemoglobin are also significant [14]. Hence we take three wavelengths in this range- viz 810nm, 880nm and 940nm to arrive at three equations in three unknowns, which are the concentrations of hemoglobin, oxyhemoglobin and water.

Figure 1. Molar absorption coefficient at different wavelengths for melanin

Figure 2. Molar absorption coefficient at different wavelengths for Hb and HbO2

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Figure 3. Molar absorption coefficient at different wavelengths for water Thus, we get a set of three equations for three different wavelengths used, in three unknowns – hemoglobin, oxyhemoglobin and water. We can solve these equations to get the concentration of these components.

III.

Active Motion Artifact Cancellation of PPG waves:

It has been noticed that PPG waves changes abruptly when there is a movement of the subject under test. Figure 11 and Figure 13 shows the variation in PPG waves with respect to the movement in different directions. Thus, if the patient moves his/her fingers whiles the reading is taken it can give very abrupt values and as a result the final answer could be wrong. Hence it has beenattempted to make this device robust enough so that it is not affected by motion of the finger while reading is being taken. In order to achieve this goal, the correlation between movement of finger and signal distortion has been calculated. We put the sensors on the fingers of both the hands of the subject. The left hand has been kept stationary and the motion has been observed in our right hand. For first 10 seconds, right hand is also kept stationary, then for next 10 seconds it is moved in horizontal direction( x-axis) with the approximate frequency of 2 Hz. This process is repeated for vertical (y-axis) and perpendicular (z-axis) too. The correlation coefficients between left hand signal (reference signal) and signal under motion is noted. Active motion artifact cancellation technique has been used to reduce error due to motion artifact.

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Figure 4. Block diagram of Active motion artifact cancellation using adaptive filtering The PPG signal obtained after filtering (o(n)) contains two components: desired signal sg(n) and also undesired motion artifact signal, n(n) [15].The undesired motion artifacts are due to the motion of subject while reading is being taken. Therefore, at the time when reading is taken if motion artifact signal n(n) is known then desired signal sg(n) can be obtained by subtracting noise signal n(n) from original signal o(n).The source of entire undesired signal is unknown to us, so entire undesiredsignal n(n) cannot be obtained and therefore instead of that an estimated motion artifact signal x’(n) has been used. The actual motion artifact signal x(n) has a linear relation with estimated motion artifact x’(n). Thus, using some filters and noise source x(n),estimated noise x’(n) is obtained . Now, by subtracting the estimated noise x’(n) from the original signal o(n), the estimated desired signal sg’(n) can be obtained. Therefore for the better estimation of the desired signal sg’(n) , the estimated motion artifact signal x’(n) should be more accurate. In the block diagram of active motion artifact, x(n) is the acceleration of finger, which closely represents the motion artifact signal that is added in the signal sg(n). This x(n) is obtained from accelerometer. Adaptive filtering of x(n) makes it more similar to the actual noise, which is then subtracted from the input to get motion artifactless signal. Also, the filter being adaptive, it changes it’s coefficients with changes in the input, ensuring effective motion artifact removal. Figure 8 shows the block diagram of the adaptive filter that has been used for active correction of motion artifacts. The correlation between the reference PPG and the PPG from the static hand (without the any movement) is always greater than 0.75. With movement in Y-direction and X-directions it is less than 0.5 depending on the acceleration of the hand. It has been found that the correlation in z-

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n Sharma, Sachin Kumar, Anshul and Shubhajit Roy Chowdhury, 24. FIELD FIELD PROGRAMMABLE GATEBASED ARRAYEMBEDDED BASED EMBEDDED SY Chetan Sharma, Sachin Kumar,Bhargava, Anshul Bhargava, and Shubhajit Roy Chowdhury, PROGRAMMABLE GATE ARRAY SYSTEM ON-INVASIVE ESTIMATION OF HEMOGLOBIN IN BLOOD USING PHOTOPLETHYSMOGRAPHY FOR NON-INVASIVE ESTIMATION OF HEMOGLOBIN IN BLOOD USING PHOTOPLETHYSMOGRAPHY

direction is always greater than 0.5. Therefore, we will only consider X and Y axis for our active correction of the motion artifacts. To process the signal, we take out the maxima and minima of the voltage values, and substitute their difference in the Beer Lambert’s Equations mentioned before. Thus, we get the concentrations of hemoglobin, oxyhemoglobin and water. We will be using a fourth order adaptive filter. The coefficients of the adaptive filter w(n) are given by the formula

…….…………………………………………………………….(3)

where e(n) is the difference of erroneous signal o(n) and noise y(n); and μx(n) is a number given by

…………………………………………………………………………………………………………………………………(4)

Hence by using the filter coefficients w(n), one can calculate the motion artifact as

……………………………………………………………………………………(5)

This motion artifact is again subtracted from the erroneous signal and this process continues till the output is stable and noiseless IV.

Implementation of electronic device for monitoring blood parameters :

Figure 5. Block Diagram of the entire system

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Figure 5 shows the block diagram of the entire system. Accordingly the device consists of three LEDs of different wavelengths- 810, 880 and 940nm respectively. As discussed in the previous section, three wavelengths have been chosen for PPG monitoring in the near infrared part of the spectrum, viz 810, 880 and 940 nm. We have selected these wavelengths in particular because it is at these wavelengths that the spectral absorptivity of hemoglobin and oxy-hemoglobin is considerable, whereas that of other blood components is negligible. The LEDs transmit IR rays which pass through the finger and some part of them is absorbed by the blood components like hemoglobin, oxyhemoglobin and water. The light that gets partially transmitted through blood is received on a photodiode. We have chosen the photodiode on the basis of sensitivity towards IR in the given wavelength region. Figure 6 shows the sensitivity of photodiode w.r.t wavelengths.Since the signal received on the photodiode is very weak, it is sent to a transimpedence amplifier with an amplification factor of 100. Theseamplified signals are sent to microcontroller for analog to digital conversion, thereafter transmitted to FPGA for filtering and calibration purpose.

Figure 6. Sensitivity of photodiodes at different wavelengths For the filtering and calibration, it is necessary to handle the floating point numbers too ,an algorithm to convert floating point numbers into mantissa and exponent using IEEE 754 single precision representation. This conversion is done on a microcontroller, and the values are sent to

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FPGA in the form of mantissa and exponent. The floating point processor for arithmetic operations on is implemented on FPGA. For the PPG wave, the frequency of the signal is synchronized with the frequency of the heartbeat. So for our low pass filter the cutoff frequency is 6 Hz. From the experimentation, it has been noticed that noise content is in higher frequencypart of spectrum, soa sharp transition band is not required. Hence, a low pass filter has been chosen with minimum ripples in the pass band at though transition band may be little more. We opted for 4th order butterworth filter. First the analog filter has been deduced and then using bilinear transformation the digital coefficients of filter is determined. FIR filter takes more coefficients and more calculation than IIR for the same filter response but we want to use the minimum resources for low power and optimization, we have implemented an IIR filter rather than FIR filter. Figure 7 shows the Direct form 1 representation of filter. After the filtering part to process the signal, if case of any motion the erroneous signal is passed to the Least Mean Square adaptive filter (LMS) to reduce the motion artifact in it.We take out the maxima and minima of the voltage values, and substitute their difference in the three equations mentioned before. Thus, we get the concentrations of deoxyhemoglobin and oxyhemoglobin. Figure 8 shows the photograph of the electronic gadget, that has been developed to estimate the concentration of hemoglobin in blood.

Figure 7. Direct Form 1 representation of filter

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Figure 8. Image of the electronic gadget V.

Results and discussions

Extensive experiments have been carried out with the device being developed. Figure 9 shows the raw PPG signal we have received from the photodiode when the hand is not in any motion and Figure 10 represents the same signal after filtering. Figure 11, shows the raw signal when the hand is kept motion less for first 5 seconds and then the movement in X direction , the movement has been captured using accelerometer. Figure 12 represents the same signal after passing through the filter. Similarly, figure 13 and figure 14 represent the unfiltered and filtered signals where the hand is kept constant for 5 seconds and then motion is Y-axis.

Figure 9. PPG signal without filtering

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Figure 10. PPG signal after filtering

Figure 11. PPG signal for first 5 sec , next 5 sec movement in horizontal direction (X-axis)

Figure 12. Filtered PPG signal for first 5 sec, next 5 sec movement in horizontal direction ( X-axis)

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Figure 13. PPG Signal for first 5 sec, next 5 movement in horizontal direction (Y-axis)

Figure 14. Filtered PPG Signal for first 5 sec, next 5 sec movement in horizontal direction (Y-axis) The results are accurate within the range of +/- 0.5 gm/dl. Apart from building the FPGA based filter, an analog filter has also been built using analog components such as op-amps, resistors and capacitors. Both the systems (FPGA based and analog) were tested with the same subject to ensure the correctness of our digital circuit. The RLT schematic of the signal FPGA is shown in figure 15. The device model used is Cyclone 2, EP2C20F484C7 and the total combinational functions used are 3,564 which is 19 % of total available functions. The supply voltage of the circuit has been kept at 3.3V and the average power dissipation of the circuit is 60 mW. The low power dissipation of the circuit makes it suitable for use as a hand held device.

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Figure 15. RTL Schematic of the FPGA based adaptive filter VI.

Conclusion

The current work proposes to estimate the concentration of hemoglobin in the blood noninvasively. This technique only requires the patient to put his fingers in the device. There is no pain, since it is non-invasive. The components used in this device are cheap, easily available and hence there is a considerable reduction in the cost. The final device that has been developed is small in size, easy to handle and can be carried anywhere easily. Since the device does not require any operational expertise, it can be operated by anyone and hence can even be applied in remote health centres for the detection of Anemia. Also, the device has been implemented to be robust to motion and gives correct reading even when the subject is under motion. Further work is going on.

VII.

References

[1] H. Kanashima, T. Yamane, T. Takubo, T. Kamitani, and M.Hino, “Evaluation of noninvasive hemoglobin monitoring for hematological disorders,” Journal of Clinical Laboratory Analysis, Vol. 19, pp. 1-5, 2005.

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[2]

K.Saigo, S. Imoto, M.Hashimoto, H. Mito, J. Moriya, T. Chinzei et al, “Noninvasive monitoring of hemoglobin: the effects of WBC counts on measurement,” American Journal Clinical Pathology, Vol. 121, pp. 51-55, 2004.

[3] R.G. Nadeau, W. Groner, “The role of a new noninvasive imaging technology in the diagnosis of anemia,” Journal of Nutrition, Vol. 131, pp. 1610S-1614S, 2001. [4] J.W. Winkelman, “Noninvasive blood bell measurements by imaging of the microcirculation,” American Journal of Clinical Pathology, Vol. 113, pp. 479-483, 2000. [5] M.J.Rice, J.R.H. Sweat, J.M. Rioux, W.T. Williams, W. Routt, “ Non-invasive measurement of blood components using retinal imaging,” United States Patent 2002:6477394. [6]

D.J. Deyo, R.O. Esenaliev, O. Hartrumpf, M. Motamedi, D.S. Prough, “Continuous noninvasive optoacoustic monitoring of hemoglobin concentration,” Anesthesiology Analgesia Vol. 92, pp. S139, 2001.

[7] R.O.Esenaliev, Y.Y. Petrov, O. Hartrumpf, D.J. Deyo, D.S. Prough, “Continuous, noninvasive monitoring of total hemoglobin concentration by an optoacoustic technique,” Applied Optics, Vol. 43, pp. 3401-3407, 2004. [8] I.Y. Petrova, R.O. Esenaliev, Y.Y. Petrov, H.P.E. Brecht, C.H. Svensen, J. Olsson, et al, “Optoacoustic monitoring of blood hemoglobin concentration: a pilot clinical study,” Optics Letters, Vol. 30, pp. 1677–1679, 2005. [9] T.K. Aldrich, M. Moosikasuwan, S.D. Shah, K.S. Deshpande, “Length-normalized pulse photoplethysmography: a noninvasive method to measure blood hemoglobin,” Annals in Biomedical Engineering, Vol. 30, pp. 1291-1298, 2002. [10] K.J. Jeon, S.J. Kim, K.K. Park, J.W. Kim, G. Yoon, “Noninvasive total hemoglobin measurement,” Journal of Biomedical Optics, Vol. 7, pp. 45-50, 2002. [11] X.M. Wu, S. Yeh, T.W. Jeng, O.S. Khalil, “Noninvasive determination of hemoglobin and hematocrit using a temperature-controlled localized reflectance tissue photometer,” Analytical Biochemistry, Vol. 287, pp. 284-293, 2000.

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[12] O.S.Khalil, S. Yeh, M.G. Lowery, X. Wu, C.F. Hanna, S. Kantor et al, “Temperature modulation of the visible and near infrared absorption and scattering coefficients of human skin,” Journal of Biomedical Optics, Vol. 8, pp. 191-205, 2003. [13]

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measurement”,Physiol. Meas. 28 (2007) R1–R39 [14] U. Timm, E. Lewis, D. McGrath, J. Kraitl and H. Ewald,”LED Based Sensor System for NonInvasive measurement of the Hemoglobin Concentration in Human Blood” ICBME 2008, Proceedings 23, pp. 825–828, 2009 [15] Hanhyonyeong, “Motion artifact reduction with active noise cancellation and accelerometer for

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