Measuring of Oxygen Saturation Using Pulse Oximeter Based on Fuzzy Logic Gözde Ateş, Student Member, IEEE Department of Electrical and Electronics Engineering Abant İzzet Baysal University Bolu/TURKEY
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Abstract –The pulse oximeter is the most used device due to be a noninvasive method in measuring the oxygen level in blood. In the pulse oximeter, red and IR (Infrared) LEDs (light emitting diode) sent signals to the photodiode. An equation or relationship between the ratio (R) of signals (red and IR) received by photodiode with the oxygen saturation value (SpO2) needs to model the value of blood oxygen saturation. Normally, the calibration curve needs to create this equation. A calibration device is necessary to obtain this calibration curve. Since this device is very expensive, absorbation coefficients obtained from healthy individuals have been used both to create this curve and to decrease the cost of pulse oximeter. In this study, firstly, R (ratio) value is calculated according to absorption rates of signals, which are sent by red and IR LEDs in finger mounted oxygen saturation sensor. The obtained R value has been used as the input to fuzzy logic then SpO2 value has been calculated. Also, a linear relationship between SpO2 and R values has been created with absorption coefficients using linear regression method and compared with SpO2 values calculated by fuzzy logic method. The results show that the SpO2 values, which are calculated by fuzzy logic, are more reliable and healthy. Keywords: Pulse oximeter device, oxygen saturation, SpO2 sensor, fuzzy logic.
I.
INTRODUCTION
Pulse oximetry is measuring of the oxygen saturation as unremitting and noninvasive. This practical method usually allows detecting arterial hypoxemia which cannot detect with subjective observations early. Pulse oximeter devices are generally used in the diagnosis of sleep apnea and respiratory diseases such as pneumonia, asthma, chronic obstructive pulmonary disease, chronic bronchitis, emphysema, congestive heart failure and pulmonary edema [1]. The measurement can be taken by placing the well-perfuse tissues like finger or ear-lobe between
Kemal Polat Department of Electrical and Electronics Engineering Abant İzzet Baysal University Bolu/TURKEY
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the sensor consisting of a light source and light dedector. The basic aim in pulse oximeter is to distinguish between oxygen and reduced hemoglobin. This distinction is the rate of absorption of red and IR radiation with the help of a microcontroller in pulse oximeter. The red signal (660 nm wave-length) and IR signal (940 nm wave-length), sent by LEDs, are transmitted the photodiode which is in the opposite of the tissue and determined the degree of light absorption. Red light is absorbed by hemoglobin, while IR light is absorbed by oxyhemoglobin. When the probe is placed on the tissue touching the surface of the tissue, the photodiode measures the intensity of lights which are sent to opposite of the tissue by LEDs and records [2]. In the literature, many studies have been conducted related to the pulse oximeter. Among them, Reddy et al. have explained the relationship between the photoelectric plethysmographic and fundamentals of pulse oximeter [3]. Reddy et al. have used the absorbation coefficients to measure the SpO2 value instead of the calibration curve [4]. Shafique et al. have used the 20 patients and the compared for all types of pulse oximeter [5]. Adochiei’s et al. designed a wireless oximeter pulse using RF technology. Also, their pulse oximeter has been telemonitorised by using WiFi or GSM/GPRS technology [6]. It seems that the fuzzy logic method is never used to calculate SpO2 values. In this study, fuzzy logic method is used by us for calculating SpO2 values for the first time. In this study, the hardware and software parts of proposed pulse oximeter are described. In hardware part, the received signal from SpO2 sensor is processed with electronic circuits (current to voltage converter, filters, op-amps, and ADC). In software part, the fuzzy logic method is used for calculating oxygen saturation value. The system
used was written and compiled by PIC C Compiler program and buried into PIC (Peripheral Interface Controller) 16F877 Microcontroller. II. MATERIAL METHOD The pulse oximeter devised in this study comprised of 6 parts. These parts are as follows: receiving a signal (sensor), filtering, amplification, analog to digital convertor (ADC), microcontroller, fuzzy logic and displaying. Figure 1 shows the block diagram of pulse oximeter. The working of the system is as follows: (a) the receiving the total signal sent from the SpO2 sensor, (b) filtering process using low pass filter (LPF) for noise filtering, (c) using the high pass filter (HPF) to filter the DC components, (d) amplifying the remaining AC signal before sampled by the ADC (analog to digital converter), (e) converting the AC signal to digital signal which is readable for the microcontroller by ADC, (f) calculating the SpO2 value using fuzzy logic method embedded in the microcontroller, and (g) displaying the SpO2 value in LCD screen. These parts of pulse oximeter have been described in below. A. Sensor In the sensor part, the light signals sent from red and IR LEDs are received by photodiode through the finger. Red LED is 660 nm and IR LED is 940 nm wave-lengths. The FMT-RAS-NLC/L finger type sensor has been used in this study. Figure 2 demonstrates the operation of the sensor.
Fig. 3. The block diagram of low-pass filter
B.2. High-Pass Filter In the high-pass filter, cut-off frequency is 0.5 Hz. It is used for filtering DC components. Also, this filter has a pre-amplificator in the inverting entrance of the amplificator. Figure 4 illustrates the block diagram of high-pass filter.
Fig. 4. The block diagram of high-pass filter
C. Amplification The voltage gain of the circuit is 57. It is used for amplifying the signals which will be sent to ADC after filtering DC components. Figure 5 illustrates the block diagram of amplifier.
Fig. 5. The block diagram of amplifier
Fig. 2. Schematic representation of SpO2 sensor
B. Filtering B.1. Low-Pass Filter In the low-pass filter, cut-off frequency is 5 Hz. It is used for filtering the noise in signals received from the sensor. Figure 3 presents the block diagram of used low-pass filter.
D. Analog to Digital Convertor (ADC) The ADC circuit in the PIC 16F877 Microcontroller is used as ADC. This ADC has 8 channels, 10 bits. The reference voltage supply is provided through the microcontroller. E. PIC Microcontroller and Fuzzy Logic PIC 16F877 microcontroller has been used as the controller. The digital signal received from ADC has been converted into the oxygen saturation value which is displayed on LCD screen with the program embedded into the microcontroller. To calculate this value, the fuzzy logic method has been used. While R (ratio) value has been used as
the input value, SpO2 value has been used as the output value in fuzzy logic. Figure 6 illustrates the flowchart of used fuzzy logic control. The input value R has been illustrated as triangle membership functions (mf) consisting 21 mfs. The output value SpO2 has been illustrated as triangle membership functions (mf) consisting 21 mfs. The causing of choosing 21 mfs and triangle membership function is that getting high SpO2 values for low R values. The rule base is given in Table 1. For defuzzification the SpO2 values from rules, the centroid of area (COA) defuzzification method is used. Fuzzy control part is embedded in the microcontroller.
The voltage signals received from photodiode is simulated by PROTEUSTM. The results of simulation and red and IR signals received from photodiode are illustrated in Fig. 7.
Fig. 7. Red and IR signals received from photodiode
Fig. 6. The flowchart of used fuzzy logic control TABLE I RULE BASE OF USED FUZZY CONTROL Number of rules
Rules
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
If R is mf1 then S is mf21 If R is mf2 then S is mf20 If R is mf3 then S is mf19 If R is mf4 then S is mf18 If R is mf5 then S is mf17 If R is mf6 then S is mf16 If R is mf7 then S is mf15 If R is mf8 then S is mf14 If R is mf9 then S is mf13 If R is mf10 then S is mf12 If R is mf11 then S is mf11 If R is mf12 then S is mf10 If R is mf13 then S is mf9 If R is mf14 then S is mf8 If R is mf15 then S is mf7 If R is mf16 then S is mf6 If R is mf17 then S is mf5 If R is mf18 then S is mf4 If R is mf19 then S is mf3 If R is mf20 then S is mf2 If R is mf21 then S is mf1
F. Display It is used to display the oxygen saturation value obtained from microcontroller. III. EXPERIMENTAL RESULTS Both the regression method and fuzzy logic method is used to calculate value of oxygen saturation in the proposed pulse oximeter.
R value is calculated in Equation 1 according to AC (Vpp) and DC (Vrms) values of the signal.
R = ( AC RED / DC RED ) /( AC IR / DC IR ) The obtained SpO2 equation according absorption coefficient is given Equation 2.
SpO2 = (692.44 * R ) /( −520.56 * R − 2906.96)
(1) to
(2)
In this study, absorption coefficients for calculating of oxygen saturation value are used. For modeling of oxygen saturation value according to R value, the following equation 3 is obtained with linear regression method. The obtained R and SpO2 values using this equation are demonstrated in Figure 7.
SpO2 = −22.60 * R + 95.842
(3)
As can be seen from the curve in Figure 7, the maximum oxygen saturation value is 91.322 (R is the value of 0.2). Normally, the maximum saturation values should be between the values of 95 and 100, so this result is not an acceptable result.
[2] M. E. Altuğ, R. Gönenci, “Pulse Oksimetre ile Arteriyel Oksijenasyonun İzlenmesi”, Veteriner Cerrahi Dergisi (2003), 9 (3-4), 58-62. [3] K. A. Reddy, B. George, N. M. Mohan, V. J. Kumar, “A Novel Method for The Measurement of Oxygen Saturation in Arterial Blood”, Instrumentation and Measurement Technology Conference Proceedings, 2008. IMTC 2008. IEEE, 2008. Fig. 7. The obtained calibration curve using linear regression method on the modeling of R and SpO2
Figure 8 presents the calibration curve of the obtained R value and SpO2 values using fuzzy logic.
[4] K. A. Reddy, B. George, N. M. Mohan, V. J. Kumar, “A Novel Calibration-Free Method of Measurement of Oxygen Saturation in Arterial Blood”, IEEE Transactions on Instrumentation and Measurement, 58(5), 2009, 1699-1705. [5] M. Shafique, P.A. Kyriacou, S K Pal, “Investigation Of Pulse Oximeter Failure Rates During Artificial Hypoperfusion Utilising A Custom Made Multimode Pulse Oximetry Sensor”, 33rd Annual International Conference of the IEEE MEMBS, August 30 – September 3, 2011.
Fig. 8. The obtained calibration curve using fuzzy logic method on the modeling of R and SpO2
These results indicated that fuzzy logic has been obtained more healthy results than regression method in the calculating and displaying of SpO2 value. IV. CONCLUSION The main objective of this study is reducing the costs of pulse oximeter and to get more realistic results in measurement, using absorption coefficients and fuzzy logic methods. In this study, the cost is reduced because calibration device is not used. Using fuzzy logic method instead of linear regression method is gotten more realistic results. V. ACKNOWLEDGEMENTS This study is supported by the Scientific Research Projects (BAP) of Abant Izzet Baysal University (Project number: 2011.09.05.464). VI. REFERENCES [1] http://www.yaramazadam.com/pulse-oksimetreve-probu/ (Last Access: December, 2011)
[6] F. Adochiei, C. Rotariu, R. Ciobotariu, H. Costin, “A Wireless Low-Power Pulse Oximetry System for Patient Telemonitoring”, The 7th International Symposium on Advanced Topics In Electrical Engineering, May 12-14, 2011.
Fig. 1. Block diagram of the system
