An Artificial Intelligence Centred Multivariate Analysis for Blood Glucose Diagnosis

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1 ISSN (Online): ISSN (Print) : International Journal of Research and Innovations in Science and Technology International Journal of Research and Innovations in Science & Technology, SAINTGITS College of Engineering, INDIA Research paper, An Artificial Intelligence Centred Multivariate Analysis for Blood Glucose Diagnosis Senthil Kumar. A 1, Kavitha. S 2* 1 Professor and Head/EEE, Dr. Mahalingam college of engineering & Technology, Pollachi, India. 2 Assistant professor, Dr. Mahalingam college of engineering & Technology, Pollachi, India *Corresponding author kaviece33@gmail.com Copyright 2015 Senthil Kumar. A and Kavitha. S. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Diabetes is a disease of disquiet evolving as one of the major health care epidemics of contemporary era. Hence Painless control of blood glucose levels would improve the quality of life, propounding better ruling of hyperglycaemia and hypoglycaemia thereby avoiding the complications of present day lancet methods. Although many efforts have been taken by researchers in order to successfully launch a device that measures blood glucose noninvasively, the results seems to be intruded due to mismatch in correlation with the present day devices. This is so because the Noninvasive device designers mostly concentrate on optical methods which suffer from greater interferences due to the less absorbing property of glucose. Aim of the paper is to draw together two different optical techniques namely Absorption photometry and photo acoustics using embedded technology, so that the results obtained produce better correlation after data handling with artificial neural network. Baseline pre-processing is used which will eliminate the errors due to instrument handling and temperature instability. Results obtained shows acceptable precision but in order to get satisfactory standards, Non-invasive glucose monitoring requires further efforts. Keywords: LASER light, Neural network, Photo Acoustic, Photodiode, Transducers. 1. Introduction Diabetes is a major disease of concern in 21st century due to the lack of exercise and poor diet practices of people. World Health Organization has stated that the count of diabetic people is expected to grow from 246 million in 2006 to 380 million by 2005 and this figure would even rise to 438 million by So it s the right time for the Researchers to design a continuous blood glucose monitoring system that makes salutary alertness among the people about the terrifyingly growing disease of concern, so that people are barred from becoming diabetic. The present day technologies such as Spectrophotometric measurement or use of lancet devices require fresh blood samples ranging from few milliliters to few drops which limit continuous monitoring [1]. These methods are invasive and eluded by people due to the ins and outs such as pain involved; time consumed and increased recurring cost per measurement and also potential risk of spreading diseases like Hepatitis, HIV through interaction of needle with biotic fluids of human skin. All these aspects makes prime craving for what is called Non-invasiveness. Diabetes mellitus is a condition of deficiency rather than a disease because people are said to be diabetic when their Pancreas (a gland in the digestive system) no longer produces insulin o r when cells in the body stop responding to insulin. Insulin is used by human body to break and convert the raw food particles into glucose; this glucose provides energy which acts as fuel for human cells to do their work actively. Diabetes is classified into four types: Type 1 Diabetes: In this case the pancreas no longer produces insulin. This mostly occurs in childhood. Type 2 Diabetes: In this case, cells do not effectively use insulin that is produced. This accounts for vast majority of cases for about 90%-95% and is diagnosed in latter part of life at the age of forty. Gestational diabetes: This type is less common and occurs during pregnancy. Other forms of Diabetes: This includes Pre-Diabetes, Congenital Diabetes, Cystic fibrosis related Diabetes, Steroidal Diabetes. Out of all these first two types are most commonly noticed ones. The symptoms of Diabetes include frequent urination, unusual thirst, weight loss, unusual hunger, fatigue, blurred vision, etc. Glucose 18

2 Diagnosing can be carried out under three ways: - Invasive technique, minimally invasive technique and Non-invasive technique [2]. Glucose monitoring is a crucial factor for self-management of diabetes. Thus the invasive or Semi-invasive technique seems to be annoying because of the cost and pain involved and the common people make use of this diagnosis at no other way. So, Non-invasive blood glucose measurement is mostly expected to take over the era, because it involves no pain. This can be accomplished by two ways either optically or Non-optically. Non-optical means of measuring blood glucose involves acquiring glucose values from Sweat, breathe, tears or urine which does not directly reflect blood glucose value and hence most of the research works concentrates on optical means[3 5]. By optical means, we can consider two methods for measuring blood glucose, one by using optical activity (such as polarization) and other by determination of the refractive index of the glucose. Both of the above methods can be used to determine glucose concentration with varying degree of sensitivity and ranges of applicability. One of the challenging tasks of Non-invasiveness is detection of weak blood signals that lose energy through intervening tissues and in addition separating glucose s information from other overlapping constituents with much higher concentration (protein, urea, uric acid, hemoglobin, water, etc ) this task can be performed by using high quality transducers or sensors, so that the sensors can measure either directly based on chemical structure of glucose or indirectly by measuring blood sugar based on secondary terms like temperature or PH changes [6, 7]. The great volume of research works at present concentrates on developing a Non-invasive device using one of the following techniques such as: - Reverse Iontophorosis, Polarimetry, Metabolic Heat Conformation, Ultrasound Spectroscopy, Thermal Emission, Electromagnetic technique, Photo acoustics, Raman Spectroscopy and Bioimpedance Spectroscopy. Few of the works concentrates on Multivariate analysis for monitoring of blood glucose. Out of the above mentioned techniques Complex Impedance and Optical Spectroscopy shows most promising results. Appealing works based on impedance spectroscopy was proposed by Caduff s group in 2003 but a limitation of this work is that it requires an equilibrium process, where the patient must rest for at least 60 minutes before going for measurements [8,9]. Apart from the technology one must also rely on several influencing factors like skin temperature, pressure, sampling duration, breathing artifacts, blood flow rate, body movements, tissue thickness, surface roughness, sweat and skin colour while going for calibration of such device. In this paper two of the optical techniques namely Absorption Spectroscopy and Photo acoustics Spectroscopy because of their superiority, have been compared and considered for refinement in order to improve the efficiency of the equipment. This paper deliberates the design and outcomes of Photoacoustic spectroscopy technique in section 2 and the upshots of absorption spectroscopy in section Photo-acoustic Spectroscopy In 1880 Alexander Graham Bell testified the transfiguration of light energy into acoustic energy by Photo-acoustic (PA) effect. Later on in 1970s, Allen brothers furnished R-G theory that added hypothetical credit to Photo-acoustic effect in solids. In PA technique, the glucose in blood is agitated by short spell LASER pulse. Light absorption produces stress in the internal tissues, ensuing in heating of the medium as a result of which a pressure wave is generated that travels outwards that can be picked up by a transducer. The amplitude of the pressure wave gives the concentration of glucose. In concurrence with Beer Lambert law, when a laser beam having light intensity I is incident on the tissue, its intensity decreases exponentially as a function of penetration depth due to the absorption of light energy by the medium. Figure1: Photo Acoustic signal generation mechanism 19

3 Figure1 shows the mechanism for PA signal generation in the human tissue and the amplitude of the pressure wave generated is given by the following equation. Where R = the distance betweenthe PA source and α = 1 + light penetration depth β = thermal expansion coefficient C p = specific heat τl = optic pulse width v = acoustic velocity the observation point From the equation it s clear that the glucose concentration dependent terms are β, v and Cp. It s found that the terms β and v increases as glucose concentration increases but Cp decreases as glucose concentration increases. As composition of blood is complex with many constituents the wavelength must be chosen with judicial care. This region ranging between 600nm 1300nm is called the optical window of biological tissue. In this region the penetration depth of light is several millimeters in the tissue [10-12]. Absorption of light by oxy-haemoglobin and deoxy-haemoglobin are different above 1000nm but the difference is fairly small around 900nm so this wavelength range around 900nm is best opted for glucose measurement optically. Piezoelectric Transducer made of Lead Zirconate Titanate (PZT) has been made use of for detecting the PA signal. PA signal can also be given the provision of being stored and processed using a Digital Storage Oscilloscope (DSO) at specific intervals. Output of the transducer is amplified by Low Noise Amplifier (LNA). Baseline preprocessing is done to filter random noise and interferences by averaging the PA signals over 1024 frames to achieve appreciable Signal to Noise Ratio (SNR). Oral Glucose Tolerance Test (OGTT) was carried out on several entities for testing the system functionality and the PA signals were recorded at regular intervals and processed using MATLAB to obtain the peak value. The Peak values were plotted and were compared with the measured values which provided with nearly equivalent values. Thus Photo acoustic technique has been used to design a new prototype for non-invasive measurement of blood glucose. 3. Absorption Spectroscopy Absorption Spectroscopy is a workhorse expertise extensively used in many research fields mainly due to its innovative approach. This technique involves light source to impinge light on to the subject under study and a photo detector for processing the sensed signal. This method of measurement seems to be attractive and smart because of the advantages like: easy and convenient operation, low cost, safety, increasing availability of LASER light source and real time assessment. Huge number of studies have been proposed in visible ( nm) and Near Infra-Red ranges. These wavelengths are found in the remedial window for deep tissue measurements [13,14]. The light focused on the skin is partly absorbed and scattered due to its interaction with the chemical components present in the tissue. Attenuation of light is given by I = IO exp (-µeff d), where I and IO are reflected and incident light intensity and the effective attenuation coefficient is given by µeff = f (µa, µs), where µa and µs are absorption and scattering coefficients respectively. Changes in glucose concentration affect µa and µs and hence the intensity of light, I gets varied [15]. 20

4 When a person tries to diagnose his/her blood sugar level, he/she must initially switch on the LASER light operating under visible range and place the finger on the sensor and the reflected light intensity will be detected by the photodiode. The photodiode converts the received light intensity into electrical analog voltage. In order to get larger variants and to set apart, it was necessary to produce the digital equivalent of the analog voltage which was accomplished using an ADC. The setup was tested for about 250 patients in JEBI Diagnostic Centre at repeated intervals for different categories of people Normal, Hyperglycaemia and hypoglycaemia persons and it was compared with Spectro-photometric value obtained from the same lab. To our surprise the peak values likely matched the real values for most of the cases. The digital equivalent was converted into direct blood sugar value in mg/dl by normal extrapolation and interpolation methods and was used to frame a Look-Up Table (LUT) which provided a correlation coefficient, R of Artificial Intelligence in Glucose Diagnosis As an improvement of the above work, this paper highlights the use of artificial intelligence based Neural Networks to diagnose blood glucose values. The network makes use of the lab data (blood glucose value in mg/dl) taken as target vectors and the digital value obtained from the photodiode as input vectors to train the network. Levenberg-Marquart back propagation algorithm is used here to update the weight and bias values. This Algorithm is highly recommended as a first choice supervised learning algorithm because of its fastest computational capability. The working of neural network in weight and threshold updation is shown below in figure 2. Figure 2: Neural Network Block Diagram Table 1: Comparison chart of photodiode output (in volts) & conventional meter (mg/dl) Sl. no Person Conventional Meter Value Photo Diode Output Digital value (mg/dl) (In Volts) From ADC 1 A B C D E Initially the input vector and test vector must be provided and the bias and weight are assigned. Depending on the error the weight and threshold must be adjusted to improve the accuracy. The weight change rule is developed from the perceptron learning rule. The output unit error is used to alter the weights on the output unit. The hidden layer errors can be calculated by back propagating the errors at the output unit and the hidden layer weights are altered with those errors. For each data set, the forward pass and backward pass is continued until the error become tolerable. Unlike other networks, in back propagation network the errors can be back propagated to the hidden layers, so that more accurate results can be obtained. In this paper a single layer network is considered, that means one input layer, one hidden layer and one output layer. To get the more accurate results hundreds of iterations should be done to achieve the target output. The training vectors constituted the readings of about 250 persons taken from the lab from both the proposed prototype and the conventional apparatus. Thus the conversion is done and the look up table is constructed from the neural network and the look up table is burned into an external EEPROM and interfaced to the microcontroller to display direct blood glucose value in mg/dl. The performance and result obtained from neural network training in MATLAB is shown in figure3 and 4 respectively. 21

5 Figure 3: Performance chart of Trained Network Figure 4: MATLAB Output of Trained Network 5. Results and Discussion Testing of the Non-invasive device after training with neural network was done for about 50 subjects in the same diagnostic centre and the results obtained for each person was cross checked with the conventional blood test taken in the same lab and it was found that the device correlated well with the improved correlation coefficient, R of Table 2: ADC value for the fasting and post prandial blood sugar Name Blood Sugar Value ( mg/dl) ADC Value Blood Sugar Value (mg/dl) ADC Value Fasting Post Prandial A B C Table 2 indicates the variation in ADC value as the fasting and post prandial blood sugar values vary. The experimental setup showing direct blood glucose reading in mg/dl is shown in Figure 5 respectively. Table 3 shows the readings obtained in the laboratory using the newly designed Non- invasive blood glucose monitor for about 15 persons out of50 subjects. Figure 5: Image of the Proposed System Displaying the Output after Interfacing 22

6 Table 3: Comparison between Conventional and Designed Prototype S.No Conventional Blood Sugar Value Experimental Kit Value Deviation (mg/dl) (mg/dl) (δ) The deviations for one or two persons may occur due to the reasons such as: - tough skin tone, very thick skin surface and noise due to external light source. The errors might have occurred because there is no means of aligning the light source with the 1 mm2 area of photodiode. So there is a chance for scattering to take place. Figure 6 shows the accuracy between conventional and newly developed prototype. Figure 6: Graph Showing Accuracy of Conventional and New Prototype developed 6. Future Enhancements Thus a device for measuring blood glucose non-invasively using a novel approach is designed and it is found to be very simple, painless and much economical. The result of the proposed Non-Invasive equipment is comparable with the conventional lab results. Compared to the previous works, in this proposal the accuracy has been increased to a greater extent with much less cost eliminating the need for complex digital processing circuit and the reference signal. More over the prototype has been tested, for about 50 persons in the JEBI Diagnostic lab out of which above 35 persons got near accurate value with ±3mg/dL to 5mg/dL and other 15 persons with ±20mg/dl deviations which are advised to be tolerable. In conclusion this project has suggested a means for Non-invasive diabetes diagnosis. Though it s at the initial stage it will take a lead in near future eliminating the need for Painful and costly test strips. The proposed system accuracy is limited due to varying skin thickness of the persons using the system and also due to the external light disturbances that affects the sensitivity of the photodiode. So the future work may rely on the usage of hybrid optical (optical-electrical) technique to reach 100% accuracy. Hence the future work may possibly aim to bring together both the absorption spectroscopy and Photo- Acoustic techniques in to a single block and making use of the fuzzy-genetic algorithm such as fire/flight to make best decision and then to train the neural network to get 100% accurate Non-invasive device as a boon to the people for diabetic diagnosis. 23

7 References [1] Carlos F Amaral, et al (2009) Multiparamete r Technique for Non-Invasive Measurement of Blood Glucose, Sensors and Actuators, Vol. 140, pp [2] Carlos Eduardo Ferrnate Do Amaral and Benhard Wolf (2008) Current Development in Non-Invasive Glucose Monitoring, Medical Engineering and Physics, Vol.30, pp [3] Andrea Tura, et al (2006) Non Invasive Glucose Monitoring: Assessment of Technologies and devices according to quantitative criteria - Diabetes Research and clinical practice, Vol.77, pp [4] Bachem A and Reed C.I (1931) The Penetration of light through human skin, Amer.J.Physiol, vol.97, pp [5] Cote, et al (1992) Non - Invasive Optical Polarimetric glucose sensing using a true phase measurement technique, IEEE Transactions on Biomedical Engineering, vol.39, No.7, pp [6] Crothall Y. Yu. K. D, et al. (2003) LASER diode applications in a continuous blood glucose sensor, proc. SPIE, vol.4996, pp [7] David C. Klonoff (1997) Non Invasive Blood Glucose Monitoring - Journal of Diabetes Care, Vol.20, No.3, pp [8] Lili Zhu, et al (2013), Non-Invasive blood glucose measurement by ultrasound-modulated optical technique Chinese optics letter, vol.11, No.2, pp [9] Sargent M. and M.O.Scully (1972) Theory of LASER operation- An outline, LASER handbook, vol. I, pp [10] Zhao. Z and Myllyl. R (2001), Photoacoustic blood glucose and tissue measurements based on optical scattering effect, Proceedings of the SPIE, Vol. 4707, pp [11] Bashkatov A. N (2005), Optical Properties of human skin, subcutaneous and mucous t issues in the wavelength range from 400 to 2000 nm, Journal of Physics D:Applied Physics, Vol. 38, pp [12] Zhao. Z and R. Myllyl, (2002), Pulsed Photoacoustic investigations in liquid and Tissue, Molecular and Quantum Acoustics, Vol. 23, pp [13] McNichols J.R. and Cote L.G (2000) Optical glucose sensing in biological fluids: An overview - Journal of biomedical Optics, vol.5, No.1, pp [14] Khalil O.S. (1999), Spectroscopic and clinical aspects of Non -Invasive glucose measurements, clinical chemistry, vol.45, No. 2, pp [15] Amerov A.K, et al (2005) Scattering and Absorption effects in the determination of glucose in whole body by near infrared spectroscopy Anal. Chem, No.77, pp