Analyzing Multivariate Calibration Techniques for Glucose Level Prediction in Non-invasive Human Tongue Spectra

Naqvi, Syed Rameez; Saeed, Muhammad; Azeemi, Naeem Zafar

doi:10.1109/biotechno.2008.26

Cited by 3 publications

(10 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…But, the smaller the number of factors, smaller shall be the computational delay. However, in our experience [5] we found it always useful, to sacrifice performance against computational delay while working with a critical device like glucose monitoring device.…”

Section: ) Optimal Factor Selection (Ofs)mentioning

confidence: 94%

“…Here, RE k represents real error as described in [5] [8]. Now, if (10) is run through k=1:g; where g is the number of components and IND is computed for all, the minimum value of IND shall correspond to the optimal number of factors to keep k.…”

Section: ) Optimal Factor Selection (Ofs)mentioning

confidence: 99%

“…V is a unitary vector called a Loadings vector, which is formed in such a way to return the original matrix using (1) from [3], [2], [5].…”

Section: ) Data Acquisition and Conditioning (Dac)mentioning

confidence: 99%

“…In [5], we examine four widely used multivariate calibration methods: Classical Least Square (CLS), Inverse Least Square (ILS), Principal Component (PC) and Partial Least Square (PLS). We discuss the impact of factor selection on the prediction of response for first overtone transmission spectra collected across human tongues.…”

Section: Introductionmentioning

confidence: 99%

“…Studies have revealed that a large amount of data is required to build glucose model that take into consideration the concentration range, sampling environment and other factors involved in the analysis [3], [4], [5]. In the same vein, an instrument must be designed that accurately detects glucose concentration.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Improving Accuracy of Non-Invasive Glucose Monitoring Through Non-local Data Denoising

Naqvi

Azeemi

Hameed

et al. 2008

2008 Cairo International Biomedical Engineering Conference

Self Cite

View full text Add to dashboard Cite

Correlation and clinical interpretation, in respect to the true glucose value of patient is imperative for optimum therapy and disease management. Accuracy of optical glucometer is hampered by many debilitating factors such as concentration range, sampling environment, tongue-to-spectrometer interface, changes in wavelength, polarization or intensity of light, to name a few. Regression techniques are used in such devices to build patient glucose model. This work is an extension to our previous work regarding multivariate calibration for glucose level prediction in non-invasive human tongue spectra. Here, we present our results for noise reduction and data conditioning during glucose spectrum isolation phase. We embed our 'Indicator Function (IF)' scheme into two popular techniques known as Outlier Sample Removal (OSR) and Descriptor Selection (DS). Methodology is tested on dataset 'OCATNE20' obtained from a public domain website and results are compared at both OSR and DS for a wide range of blood serum samples. Our results show that outlier samples identification and removal in early stage significantly increase the prediction of unknown samples typically in the range of 7.95% to 9.84%.

show abstract

Section: ) Optimal Factor Selection (Ofs)mentioning

confidence: 94%

Section: ) Optimal Factor Selection (Ofs)mentioning

confidence: 99%

“…V is a unitary vector called a Loadings vector, which is formed in such a way to return the original matrix using (1) from [3], [2], [5].…”

Section: ) Data Acquisition and Conditioning (Dac)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improving Accuracy of Non-Invasive Glucose Monitoring Through Non-local Data Denoising

Naqvi

Azeemi

Hameed

et al. 2008

2008 Cairo International Biomedical Engineering Conference

Self Cite

View full text Add to dashboard Cite

show abstract

Analyzing impact of outliers' detection and removal from the test sample in Blind Source Extraction using Multivariate Calibration Techniques

Naqvi

Rehman²,

Naqvi³

et al. 2009

2009 International Conference on Information and Communication Technologies

Self Cite

View full text Add to dashboard Cite

Blind Source Extraction (BSE) may be an essential but a challenging task where multiple sources are convolved and/or time delayed. In this article we discuss the performance of Multivariate Calibration Techniques that comprise of Classical Least Square (CLS), Inverse Linear Regression (ILS), Principal Component Regression (PCR) and Partial Least Square Regression (PLS) in achieving this task in robust speech recognition systems with varying Signal-to-Noise Ratios (SNR). We specifically analyze two methods for identifying and removing outliers from the sample, namely; Outlier Sample Removal (OSR) and Descriptor Selection (DS) for Classical Least Square and Factor Based Regression respectively, which results in higher correlation among predicted and the expected results. Our experiments suggest that factor based methods produce much reliable results than Classical Least Square Regression. However, Classical Least Square is much more immune to white noise as compared to Factor Based Regressions. Our results prove that successful detection and removal of outliers from the Sample Under Test (SUT) may result in as low as 37% and 56% improvement in prediction with Classical Least Square and Principal Component Regression respectively.978-1-4244-4609-4/09/$25.00 ©2009 IEEE

show abstract

Development of a non-invasive blood glucose monitor based on impedance measurements

Malinin¹

2012

IJBET

View full text Add to dashboard Cite

Analyzing Multivariate Calibration Techniques for Glucose Level Prediction in Non-invasive Human Tongue Spectra

Cited by 3 publications

References 1 publication

Improving Accuracy of Non-Invasive Glucose Monitoring Through Non-local Data Denoising

Improving Accuracy of Non-Invasive Glucose Monitoring Through Non-local Data Denoising

Analyzing impact of outliers' detection and removal from the test sample in Blind Source Extraction using Multivariate Calibration Techniques

Development of a non-invasive blood glucose monitor based on impedance measurements

Contact Info

Product

Resources

About