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Non Invasive Estimation of Blood Parameters using Fingertip PhotoplethysmographyAuthor: Swathi Reddy Ramasahayam Date: 2018-06-25 Report no: IIIT/TH/2018/12 Advisor:Shubhajit Roy Chowdhury AbstractThe non invasive measurement of chromophores in blood is in great need for diagnosis of major health hazards. Frequent monitoring of blood glucose levels, along with blood urea will allow early detection of diabetes, liver dysfunction and renal diseases. The non invasive instrument used for accomplishing these tasks must meet several criteria like, accuracy and precision of the measurement. Many efforts have been made by many research groups all over the world in making the procedure as much less invasive as possible by trying to reduce the blood drawn for test to micro liters or by reducing the cross sectional area of the suction needle. The device should be portable, low-cost and reliable overtime. There are many modes of diagnosis that satisfy these criteria but most of them are still in a developmental stage. Although many methods have been proposed and are available in literature, near infrared (NIR) spectroscopy has the advantage of meeting these rigorous demands.Initially, based on the intrinsic property of the analyte molecule, the NIR spectroscopy has been carried out and the wavelength of peak absorption has been identified. Using the identified wavelength a finger clip comprising of sensor and detector has been developed for collecting the in vivo PPG (photoplethsymograph) signals. These non invasive signals are used for estimating the concentration of specific blood analyte. Various statistical techniques have been used for carrying out the calibration of the in vivo signals obtained from the photoplethsymograph based sensing circuit. Secondly, these signals are calibrated using artificial neural networks to predict the analyte concentration. As typical blood analytes, blood glucose and urea are used to establish the proposed methodology to noninvasively estimate the blood parameters. Altogether 282 patients were available for measurement of glucose out of which for 100 samples the reference values are measured using Accucheck glucometer and for remaining 182 samples the reference values are measured using traditional lab based method. For the first 100 samples the testing has been carried out after taking informed consent from patients out of which 50 patient data have been used for training the neural networks and testing has been carried out on remaining 50 patients. The training of neural networks has been done offline using MATLAB and then implemented on FPGA to minimize the design circuitry. For estimating the concentration of urea in blood, samples are collected from total of 70 patients. For this invasive urea measurement is carried out using lab based method. For estimating the blood glucose concentration, the data has been calibrated using two types of neural network architectures conventional neuron model and the inverse delayed (ID) neuron model and the results are compared. It is found that the mean square error in estimating the glucose concentration was found to be 1.89mg/dL using ID function model of neuron as against 5.48mg/dL using conventional neuron model with 10 neurons in hidden layer for each model. In a similar way the blood urea concentration has been estimated and the mean square error is found to be 2.23mg/dL. For measuring the blood urea concentration, the artificial neural network based regression analysis has been carried out. However, different components in blood, although having different absorption spectrum, there is some overlap between absorption spectra of different components. Hence the photoplethsymography output is subjected to different statistical techniques like artificial neural networks(ANN), principal component analysis(PCA). Finally, an attempt has been made to identify the optimal orientation of optode pair with respect to the skin surface using Rayleigh Rice Vector Pertubation Surface Scattering theory, surface roughness models for skin surface roughness have been created and optimal orientation of the optode pair with the skin surface has been theoritically estimated. The results of the theoritical estimation has also been suitably verified with the help of experiments both in the case of reflective and transmissive photoplethsymography. Full thesis: pdf Centre for VLSI and Embeded Systems Technology |
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