U.S. patent application number 16/183325 was filed with the patent office on 2021-11-04 for system and method for obtaining blood glucose concentration using temporal independent component analysis (ica).
This patent application is currently assigned to SAMSUNG ELECTRONICS CO., LTD.. The applicant listed for this patent is SAMSUNG ELECTRONICS CO., LTD.. Invention is credited to Gorish AGGARWAL, Kiran BYNAM, Sujit JOS, So Young LEE.
Application Number | 20210338115 16/183325 |
Document ID | / |
Family ID | 1000005910317 |
Filed Date | 2021-11-04 |
United States Patent
Application |
20210338115 |
Kind Code |
A9 |
AGGARWAL; Gorish ; et
al. |
November 4, 2021 |
SYSTEM AND METHOD FOR OBTAINING BLOOD GLUCOSE CONCENTRATION USING
TEMPORAL INDEPENDENT COMPONENT ANALYSIS (ICA)
Abstract
A method for obtaining blood glucose concentration using near
infrared spectroscopy (NIR) data is provided. The method includes
obtaining, by an independent component analysis (ICA) temporal
module, orthogonal pure spectra from human NIR spectra; performing,
by a processing module, one or more preprocessings and drift
removal on the human NIR spectra and the orthogonal pure spectra to
obtain preprocessed spectra; and obtaining, by a regression block,
the blood glucose concentration from the preprocessed spectra.
Inventors: |
AGGARWAL; Gorish;
(Karnataka, IN) ; BYNAM; Kiran; (Karnataka,
IN) ; JOS; Sujit; (Karnataka, IN) ; LEE; So
Young; (Daejeon, KR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
SAMSUNG ELECTRONICS CO., LTD. |
Suwon-si |
|
KR |
|
|
Assignee: |
SAMSUNG ELECTRONICS CO.,
LTD.
Suwon-si
KR
|
Prior
Publication: |
|
Document Identifier |
Publication Date |
|
US 20190159703 A1 |
May 30, 2019 |
|
|
Family ID: |
1000005910317 |
Appl. No.: |
16/183325 |
Filed: |
November 7, 2018 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/7257 20130101;
G01N 33/49 20130101; A61B 5/1455 20130101; A61B 5/725 20130101;
A61B 5/14532 20130101; G16H 10/40 20180101; A61B 5/7267 20130101;
G06N 20/00 20190101 |
International
Class: |
A61B 5/145 20060101
A61B005/145; G16H 10/40 20060101 G16H010/40; G06N 99/00 20060101
G06N099/00; G01N 33/49 20060101 G01N033/49; A61B 5/1455 20060101
A61B005/1455; A61B 5/00 20060101 A61B005/00 |
Foreign Application Data
Date |
Code |
Application Number |
Nov 29, 2017 |
IN |
201741042881 |
Jun 29, 2018 |
KR |
10-2018-0075311 |
Claims
1. A method for obtaining blood glucose concentration using near
infrared spectroscopy (NIR) data, the method comprising: obtaining,
by an independent component analysis (ICA) temporal module,
orthogonal pure spectra from human NIR spectra; performing, by a
processing module, one or more preprocessings and drift removal on
the human NIR spectra and the orthogonal pure spectra to obtain
preprocessed spectra; and obtaining, by a regression block, the
blood glucose concentration from the preprocessed spectra.
2. The method as claimed in claim 1, wherein the obtaining the
orthogonal pure spectra comprises: receiving, by a pre-data
whitening unit, the human NIR spectra; obtaining, by the pre-data
whitening unit, data whitened NIR spectra based on performing
transformation on the human NIR spectra; calculating, by an
iterative processing unit, an orthogonal pure spectrum from the
data whitened NIR spectra; calculating new deflated NIR spectra to
be transmitted to the iterative processing unit, to compute a new
orthogonal pure spectrum based on removal of an effect of the
previously calculated orthogonal pure spectrum; and combining one
or more computed orthogonal pure spectrums to obtain the orthogonal
pure spectra.
3. The method as claimed in claim 2, wherein the obtaining the data
whitened NIR spectra comprises: calculating Eigen vectors of the
human NIR spectra using a singular value decomposition; and
applying a whitening transformation using the Eigen vectors on the
human NIR spectra to obtain the data whitened NIR spectra.
4. The method as claimed in claim 3, wherein the calculating the
orthogonal pure spectrum comprises: computing, by a single
processing unit, an estimate spectrum, based on the data whitened
NIR spectra and a residual error; and reiterating the computing of
the estimate spectrum until convergence of learning parameters is
achieved to obtain the orthogonal pure spectrum.
5. The method as claimed in claim 4, wherein the computing the
estimate spectrum comprises: randomly initializing the learning
parameters, the learning parameters comprising a weight vector and
a bias vector; obtaining the estimate spectrum based on the weight
vector and the bias vector; and computing source statistics for the
estimate spectrum, the source statistics comprising Cross
correlation and Covariance matrices.
6. The method as claimed in claim 5, wherein the reiterating
comprises: calculating updated values of the weight vector and the
bias vector based on the source statistics of the estimate
spectrum; calculating an updated estimate spectrum based on the
updated values of the weight vector and the bias vector;
determining the updated estimate spectrum as the orthogonal pure
spectrum in response to the convergence being achieved for the
weight vector; and reiterating the computing of the estimate
spectrum in response to the convergence not being achieved for the
weight vector.
7. The method as claimed in claim 2, wherein the calculating the
new deflated NIR spectra comprises: deflating the data whitened NIR
spectra based on the orthogonal pure spectrum obtained from the
iterative processing unit; determining whether a certain number of
orthogonal pure spectrums are obtained; and transmitting the
deflated NIR spectra to the iterative processing unit to obtain a
new orthogonal pure spectrum in response to the certain number of
the orthogonal pure spectrums are not obtained.
8. The method as claimed in claim 1, wherein the obtaining the
preprocessed spectra comprises: performing an extended
multiplicative scatter correction (EMSC) method on the human NIR
spectra and the orthogonal pure spectra; performing, by using a
Fast Fourier Transform (FFT) block, a filtering method to obtain
filtered spectra; and performing, on the filtered spectra, drift
removal to obtain the preprocessed spectra.
9. The method as claimed in claim 8, wherein the obtaining the
filtered spectra comprises: subsequent to performing the EMSC
method, performing a Fourier domain filtering on the human NIR
spectra to reduce noise on the human NIR spectra by using a Hanning
Window; and removing drift by differentiating, with respect to a
wavelength, Fourier domain filtered spectra, to obtain the filtered
spectra.
10. The method as claimed in claim 1, wherein the obtaining the
blood glucose concentration comprises: extracting, by a feature
extraction block, one or more features from the preprocessed
spectra; obtaining a training data set and a validation data set
from the one or more features; and obtaining the blood glucose
concentration by performing regression on the training data set and
the validation data set.
11. A system for obtaining blood glucose concentration using near
infrared spectroscopy (NIR) data, the system comprising: at least
one processor comprising: an independent component analysis (ICA)
temporal module configured to obtain orthogonal pure spectra from
human NIR spectra; a processing module configured to perform one or
more preprocessings and drift removal on the human NIR spectra and
the orthogonal pure spectra to obtain preprocessed spectra; and a
regression block configured to obtain the blood glucose
concentration from the preprocessed spectra.
12. The system of claim 11, wherein the ICA temporal module
comprises: a pre-data whitening unit configured to: receive the
human NIR spectra; and obtain data whitened NIR spectra based on
performing transformation on the human NIR spectra; an iterative
processing unit configured to calculate an orthogonal pure spectrum
from the data whitened NIR spectra; a deflation module configured
to calculate new deflated NIR spectra to be transmitted to the
iterative processing unit, to compute a new orthogonal pure
spectrum based on removal of an effect of the previously calculated
orthogonal pure spectrum; and a learning algorithm unit configured
to combine one or more computed orthogonal pure spectrums to obtain
the orthogonal pure spectra.
13. The system of claim 12, wherein the pre-data whitening unit is
further configured to: obtain Eigen vectors of the human NIR
spectra using a singular value decomposition; and apply a whitening
transformation using the Eigen vectors on the human NIR spectra to
obtain the data whitened NIR spectra.
14. The system of claim 13, wherein the learning algorithm unit is
configured to: compute, by a single processing unit included in the
learning algorithm unit, an estimate spectrum, based on the data
whitened NIR spectra and a residual error.
15. The system of claim 14, wherein the learning algorithm unit is
configured to compute the estimate spectrum by performing: randomly
initializing learning parameters, the learning parameters
comprising a weight vector and a bias vector; obtaining the
estimate spectrum based on the weight vector and the bias vector;
and computing a source statistics for the estimate spectrum, the
source statistics comprising Cross correlation and Covariance
matrices; and reiterating computing of the estimate spectrum until
convergence of the learning parameters is achieved to obtain the
orthogonal pure spectrum.
16. The system of claim 15, wherein the reiterating comprises:
calculating updated values of the weight vector and the bias vector
based on the source statistics of the estimate spectrum;
calculating an updated estimate spectrum based on the updated
values of the weight vector and the bias vector; determining the
updated estimate spectrum as the orthogonal pure spectrum in
response to the convergence being achieved for the weight vector;
and reiterating the updated values of the weight vector and the
bias vector to the single processing unit in response to the
convergence not being achieved for the weight vector.
17. The system of claim 12, wherein the deflation module is further
configured to: deflate the data whitened NIR spectra based on the
orthogonal pure spectrum obtained from the iterative processing
unit; determine whether a certain number of orthogonal pure
spectrums are obtained; and transmit the deflated NIR spectra to
the iterative processing unit to obtain a new orthogonal pure
spectrum in response to the certain number of the orthogonal pure
spectrums not being obtained.
18. The system of claim 11, wherein the processing module
comprises: an extended multiplicative scatter correction (EMSC)
module configured to perform an extended multiplicative scatter
correction (EMSC) method on the human NIR spectra and the
orthogonal pure spectra; a Fast Fourier Transform (FFT) block
configured to perform a filtering method to obtain filtered
spectra; and a drift removal module configured to perform drift
removal on the filtered spectra to obtain the preprocessed
spectra.
19. The system of claim 11, wherein the regression block further
comprises: a feature extraction block configured to extract one or
more features from the preprocessed spectra; a separation block
configured to obtain a training data set and a validation data set
from the one or more features; and a regression model identifier
block configured to obtain the blood glucose concentration based on
performing regression on the training data set and the validation
data set.
Description
CROSS-REFERENCE TO RELATED APPLICATION(S)
[0001] This application claims priority from Indian Patent
Application No. 201741042881, filed on Nov. 29, 2017 in the Indian
Intellectual Property Office, and Korean Patent Application No.
10-2018-0075331, filed on Jun. 29, 2018 in the Korean Intellectual
Property Office, the disclosures of which are hereby incorporated
in their entireties by reference.
BACKGROUND
1. Technical Field
[0002] Systems, devices, and methods consistent with exemplary
embodiments relate to glucose monitoring, and more particularly
relates to obtaining blood glucose concentration using temporal
independent component analysis (ICA).
2. Description of the Related Art
[0003] Glucose monitoring is used for testing level of glucose
concentration in blood, and can be performed either invasively or
non-invasively. In the invasive method, skin of a person is pierced
to obtain blood sample for testing, and in the non-invasive method,
collection of blood sample is not required for obtaining the
glucose concentration. Some of the typical methods used for
non-invasive glucose monitoring include Mid Infrared (Mid IR), Near
Infrared (NIR), and Raman spectroscopy. In recent years, the NIR
method is commonly used for continuous glucose monitoring, in which
the IR waves are made to pass through the skin and absorption of
the IR waves by the subcutaneous portion of skin is used in
determining the glucose level. The absorption of the wave by the
sample is defined by BEER Lambert law:
A = log .function. ( I I 0 ) = .times. .times. Cd ( 1 )
##EQU00001##
[0004] Where E is absorption coefficient, C is concentration of
component in sample and d is penetration depth.
[0005] If the sample is composed of different constituents having
different coefficients ( .sub.1, .sub.2, . . . .sub.n) and
concentrations (C.sub.l, C.sub.2, . . . C.sub.n), then overall
absorption can be given as the following equation:
A= .sub.1C.sub.1d+ .sub.2C.sub.2d+ . . . + .sub.nC.sub.nd (2)
[0006] The NIR spectrum of the skin is composed of absorption of
the IR waves by several components such as water, fat (or
cholesterol), protein (e.g., collagen and keratin), amino acids,
elastin and glucose. Therefore, the NIR spectrum of the skin can be
obtained as the following equation:
A.sub.NIR=A.sub.Water+A.sub.Cholesterol+A.sub.Collagen+A.sub.Keratin+A.s-
ub.Elastin+A.sub.Acid+A.sub.Glucose
[0007] Monitoring of glucose concentration non-invasively is very
challenging as the concentration of glucose in blood is several
orders lesser than that of other constituents and many times, the
glucose information is buried under the noise and drift components
of the NIR spectra. The orders of concentration of different
constituents are shown in the below table:
TABLE-US-00001 Constituent Water Fat Protein Elastin/Acid Glucose
Order of concentration(~) 10{circumflex over ( )}0 10{circumflex
over ( )}-1 10{circumflex over ( )}-3 10{circumflex over ( )}-3
10{circumflex over ( )}-4
[0008] Related art methods for monitoring glucose levels using an
NIR spectrum includes a non-contact analysis method of solid
samples in NIR Diffusion reflectance measurement, wherein
Independent Component Analysis is performed on the mixed spectra to
separate the mixed spectra into the pure analyte spectra and their
concentration profiles. The related art method also uses a scatter
correction to remove the non-linear effects from the measured
spectra. However, the related art method does not address the issue
of instrumental/environmental residual drift reducing SNR in an
actual scenario. Further, the related art method uses independent
component analysis (ICA) algorithm that assumes statistically
independent source signals and non-zero kurtosis. However, in many
of the cases, bio medical source signals are dependent on each
other and have very low kurtosis value, which significantly
degrades the accuracy in monitoring glucose levels.
[0009] Another related art method for monitoring glucose levels is
based on an approach for analysis of Near Infrared (NIR) data using
Independent Component Analysis (ICA), wherein a Blind Source
Separation is performed on a non-analyte mixture to identify the
concentration of individual mixture. The method uses a mixture made
from starch, water and protein for experimentation. However, the
ICA algorithm used in the related art method assumes
interdependence of source signals and that the concentration of
each components of the mixture is not time varying. However, for a
human body, the concentration of components may change with time.
Further, the related art method does not address the challenge of
concentration of one component (e.g., glucose) being very low
compared to that of other components (e.g., water and protein),
which significantly degrades the accuracy of human skin NIR
analysis.
[0010] Another related art method for monitoring glucose levels
includes a method for measuring blood glucose using only the
portion of the IR spectrum which contains the NIR water absorption
peaks, wherein the related art method uses electromagnetic (EM)
radiation of a wavelength transmitted through the skin to the
measurement region, for example, a blood vessel. The collected
light is analyzed and compared against a stored reference
calibration curve to calculate blood glucose concentration.
However, the related art method assumes that the background
interference is common for an entire range of the near infrared
region. Further, the related art method uses a reference
calibration curve which varies from person to person and hence
universality is not guaranteed. It is assumed in the related art
method that all constituents of the human skin is known and well
understood in the NIR spectrum. This is not true for the case of
human skin. This will affect the accuracy in determination of the
glucose level.
[0011] Therefore, there is a need for a method for identifying the
pure spectra of various skin components directly from the NIR
spectra. Further, there is a need for a method for modifying the
original ICA algorithm to obtain representations of orthogonal pure
spectra even if the actual pure spectra are dependent on each
other. Further, there is a need for a method, in which the obtained
spectra is used by the temporal ICA algorithm to obtain the glucose
concentration in the NIR spectra without the need for in vitro pure
spectra. Further, there is need for a method for obtaining blood
glucose concentration using temporal independent component analysis
(ICA).
SUMMARY
[0012] One or more exemplary embodiments provide a method for
obtaining blood glucose concentration using temporal independent
component analysis (ICA).
[0013] According to an aspect of an exemplary embodiment, there is
provided a method for obtaining blood glucose concentration using
near infrared spectroscopy (NIR) data, the method including:
obtaining, by an independent component analysis (ICA) temporal
module, orthogonal pure spectra from human NIR spectra; performing,
by a processing module, one or more preproces sings and drift
removal on the human NIR spectra and the orthogonal pure spectra to
obtain preprocessed spectra; and obtaining, by a regression block,
the blood glucose concentration from the preprocessed spectra.
[0014] The obtaining the orthogonal pure spectra may include:
receiving, by a pre-data whitening unit, the human NIR spectra;
obtaining, by the pre-data whitening unit, data whitened NIR
spectra based on performing transformation on the human NIR
spectra; calculating, by an iterative processing unit, an
orthogonal pure spectrum from the data whitened NIR spectra;
calculating new deflated NIR spectra to be transmitted to the
iterative processing unit, to compute a new orthogonal pure
spectrum based on removal of an effect of the previously calculated
orthogonal pure spectrum; and combining one or more computed
orthogonal pure spectrums to obtain the orthogonal pure
spectra.
[0015] The obtaining the data whitened NIR spectra may include:
calculating Eigen vectors of the human NIR spectra using a singular
value decomposition; and applying a whitening transformation using
the Eigen vectors on the human NIR spectra to obtain the data
whitened NIR spectra.
[0016] The calculating the orthogonal pure spectrum may include:
computing, by a single processing unit, an estimate spectrum, based
on the data whitened NIR spectra and a residual error; and
reiterating the computing of the estimate spectrum until
convergence of learning parameters is achieved to obtain the
orthogonal pure spectrum.
[0017] The computing the estimate spectrum may include: randomly
initializing the learning parameters, the learning parameters
including a weight vector and a bias vector; obtaining the estimate
spectrum based on the weight vector and the bias vector; and
computing source statistics for the estimate spectrum, the source
statistics including Cross correlation and Covariance matrix.
[0018] The reiterating may include: calculating updated values of
the weight vector and the bias vector based on the source
statistics of the estimate spectrum; calculating an updated
estimate spectrum based on the updated values of the weight vector
and the bias vector; determining the updated estimate spectrum as
the orthogonal pure spectrum in response to the convergence being
achieved for the weight vector; and reiterating the computing of
the estimate spectrum in response to the convergence not being
achieved for the weight vector.
[0019] The calculating the new deflated NIR spectra may include:
deflating the data whitened NIR spectra based on the orthogonal
pure spectrum obtained from the iterative processing unit;
determining whether a certain number of orthogonal pure spectrums
are obtained; and transmitting the deflated NIR spectra to the
iterative processing unit to obtain a new orthogonal pure spectrum
in response to the certain number of the orthogonal pure spectrums
are not obtained.
[0020] The obtaining the preprocessed spectra may include:
performing an extended multiplicative scatter correction (EMSC)
method on the human NIR spectra and the orthogonal pure spectra;
performing, by using a Fast Fourier Transform (FFT) block, a
filtering method to obtain filtered spectra; and performing, on the
filtered spectra, drift removal to obtain the preprocessed
spectra.
[0021] The obtaining the filtered spectra may include: subsequent
to performing the EMSC method, performing a Fourier domain
filtering on the human NIR spectra to reduce noise on the human NIR
spectra by using a Hanning Window; and removing drift by
differentiating, with respect to a wavelength, Fourier domain
filtered spectra, to obtain the filtered spectra.
[0022] The obtaining the blood glucose concentration may include:
extracting, by a feature extraction block, one or more features
from the preprocessed spectra; obtaining a training data set and a
validation data set from the one or more features; and obtaining
the blood glucose concentration by performing regression on the
training data set and the validation data set.
[0023] According to an aspect of another exemplary embodiment,
there is provided a system for obtaining blood glucose
concentration using near infrared spectroscopy (NIR) data, the
system including: an independent component analysis (ICA) temporal
module configured to obtain orthogonal pure spectra from human NIR
spectra; a processing module configured to perform one or more
preproces sings and drift removal on the human NIR spectra and the
orthogonal pure spectra to obtain preprocessed spectra; and a
regression block configured to obtain the blood glucose
concentration from the preprocessed spectra.
[0024] The ICA temporal module may include: a pre-data whitening
unit configured to: receive the human NIR spectra; and obtain data
whitened NIR spectra based on performing transformation on the
human NIR spectra; an iterative processing unit configured to
calculate an orthogonal pure spectrum from the data whitened NIR
spectra; a deflation module configured to calculate new deflated
NIR spectra to be transmitted to the iterative processing unit, to
compute a new orthogonal pure spectrum based on removal of an
effect of the previously calculated orthogonal pure spectrum; and a
learning algorithm unit configured to combine one or more computed
orthogonal pure spectrums to obtain the orthogonal pure
spectra.
[0025] The pre-data whitening unit may obtain Eigen vectors of the
human NIR spectra using a singular value decomposition, and apply a
whitening transformation using the Eigen vectors on the human NIR
spectra to obtain the data whitened NIR spectra.
[0026] The learning algorithm unit may compute, by a single
processing unit included in the learning algorithm unit, an
estimate spectrum, based on the data whitened NIR spectra and a
residual error.
[0027] The learning algorithm unit may compute the estimate
spectrum by performing: randomly initializing learning parameters,
the learning parameters including a weight vector and a bias
vector; obtaining the estimate spectrum based on the weight vector
and the bias vector; and computing a source statistics for the
estimate spectrum, the source statistics including Cross
correlation and Covariance matrix; and reiterating computing of the
estimate spectrum until convergence of the learning parameters is
achieved to obtain the orthogonal pure spectrum.
[0028] The reiterating may include: calculating updated values of
the weight vector and the bias vector based on the source
statistics of the estimate spectrum; calculating an updated
estimate spectrum based on the updated values of the weight vector
and the bias vector; determining the updated estimate spectrum as
the orthogonal pure spectrum in response to the convergence being
achieved for the weight vector; and reiterating the updated values
of the weight vector and the bias vector to the single processing
unit in response to the convergence not being achieved for the
weight vector.
[0029] The deflation module may deflate the data whitened NIR
spectra based on the orthogonal pure spectrum obtained from the
iterative processing unit; determine whether a certain number of
orthogonal pure spectrums are obtained, and transmit the deflated
NIR spectra to the iterative processing unit to obtain a new
orthogonal pure spectrum in response to the certain number of the
orthogonal pure spectrums not being obtained.
[0030] The processing module may include: an extended
multiplicative scatter correction (EMSC) module configured to
perform an extended multiplicative scatter correction (EMSC) method
on the human NIR spectra and the orthogonal pure spectra; a Fast
Fourier Transform (FFT) block configured to perform a filtering
method to obtain filtered spectra; and a drift removal module
configured to perform drift removal on the filtered spectra to
obtain the preprocessed spectra.
[0031] The regression block may include: a feature extraction block
configured to extract one or more features from the preprocessed
spectra; a separation block configured to obtain a training data
set and a validation data set from the one or more features; and a
regression model identifier block configured to obtain the blood
glucose concentration based on performing regression on the
training data set and the validation data set.
BRIEF DESCRIPTION OF THE DRAWINGS
[0032] The above and/or other aspects will become apparent and more
readily appreciated by describing certain exemplary embodiments
with reference to the accompanying drawings in which:
[0033] FIG. 1 illustrates a schematic flow diagram illustrating a
method for obtaining blood glucose concentration using temporal
independent component analysis (ICA), according to an exemplary
embodiment;
[0034] FIG. 2 is a schematic block diagram illustrating units for
obtaining blood glucose concentration using temporal independent
component analysis (ICA), according to an exemplary embodiment;
and
[0035] FIG. 3 is a schematic diagram illustrating single processing
unit for extracting spectra, according to an exemplary
embodiment.
DETAILED DESCRIPTION
[0036] In the following detailed description, exemplary embodiments
will be described with reference to the accompanying drawings.
Descriptions of well-known components and processing techniques are
omitted so as to not unnecessarily obscure the embodiments herein.
The examples used herein are intended merely to facilitate an
understanding of ways in which the embodiments herein can be
practiced and to further enable those of skill in the art to
practice the embodiments herein. Accordingly, the examples should
not be construed as limiting the scope of the embodiments herein.
It should be understood that other embodiments may be utilized and
that changes may be made without departing from the scope of the
disclosure. The following detailed description is, therefore, not
to be taken in a limiting sense, and the scope of the disclosure is
defined only by the appended claims.
[0037] The specification may refer to "an", "one" or "some"
embodiment(s) in several locations. This does not necessarily imply
that each such reference is to the same embodiment(s), or that the
feature only applies to a single embodiment. Single features of
different embodiments may also be combined to provide other
embodiments.
[0038] As used herein, the singular forms "a", "an" and "the" are
intended to include the plural forms as well, unless expressly
stated otherwise. It will be further understood that the terms
"includes", "comprises", "including" and/or "comprising" when used
in this specification, specify the presence of stated features,
integers, s, operations, elements and/or components, but do not
preclude the presence or addition of one or more other features,
integers, steps, operations, elements, components, and/or groups
thereof. As used herein, the term "and/or" includes any and all
combinations and arrangements of one or more of the associated
listed items.
[0039] Unless otherwise defined, all terms (including technical and
scientific terms) used herein have the same meaning as commonly
understood by one of ordinary skill in the art to which this
disclosure pertains. It will be further understood that terms, such
as those defined in commonly used dictionaries, should be
interpreted as having a meaning that is consistent with their
meaning in the context of the relevant art and will not be
interpreted in an idealized or overly formal sense unless expressly
so defined herein.
[0040] The disclosure describes a method for obtaining blood
glucose concentration using temporal independent component analysis
(ICA). According to an exemplary embodiment, the method comprises
an Independent Component Analysis (ICA) temporal module to obtain
an orthogonal pure spectrum from human Near Infrared Spectroscopy
(NIR) spectra. The human NIR spectra are received and provided to
the ICA temporal module to be used to obtain the orthogonal pure
spectra with respect to the human NIR spectra.
[0041] In an exemplary embodiment, obtaining the orthogonal pure
spectrum comprises receiving, by a pre-data whitening unit, human
NIR spectra. The pre-data whitening unit provides data whitened NIR
spectra after applying a transformation on the human NIR spectra.
In another exemplary embodiment, obtaining the data whitened NIR
spectra comprises calculating Eigen vectors of the input NIR
spectra using singular value decomposition method. Further, a
whitening transformation is applied using the Eigen vectors on the
input NIR spectra to obtain the data whitened NIR spectra.
[0042] Further, obtaining the orthogonal pure spectra comprises
calculating, by an iterative processing unit, an orthogonal pure
spectrum from the data whitened NIR spectra. In another exemplary
embodiment, the method for obtaining blood glucose concentration
comprises calculating the orthogonal pure spectra, which comprises
computing an estimate spectrum by a single processing unit, based
on the data whitened NIR spectra and a residual error, while
computing the estimate spectrum. The computing an estimate spectrum
by the single processing unit comprises randomly initializing the
learning parameters, which comprises a weight vector and a bias
vector, obtaining an estimate spectrum based on the weight vector
and the bias vector, and computing source statistics for the
estimate spectrum, the source statistics comprising a Cross
correlation and Covariance matrix. Further, the method for
obtaining blood glucose concentration comprises combining all
computed orthogonal pure spectrum to obtain an orthogonal pure
spectra.
[0043] Further, calculating individual orthogonal pure spectra
comprises reiterating over the estimate spectrum until convergence
of learning parameters is achieved to obtain an orthogonal pure
spectrum, wherein the reiterating over the estimate spectrum to
obtain the orthogonal pure spectrum comprises calculating updated
values of the weight vector and the bias vector based on the source
statistics of the estimate spectrum, calculating the updated
estimate spectrum based on the updated values of the weight vector
and the bias vector, assigning the updated estimate spectrum values
as the orthogonal pure spectrum in response to convergence being
achieved for the weight vector, and reiterating the above
operations in response to convergence not being reached.
[0044] Further, the method of obtaining a pure spectrum comprises
calculating new deflated NIR spectra to be transmitted back to the
iterative processing unit, to compute a new orthogonal pure
spectrum, after removing the effect of the orthogonal pure spectrum
previously calculated. In an exemplary embodiment, calculating the
new deflated NIR spectra to be transmitted back to the iterative
processing unit comprises deflating the data whitened NIR spectra
based on the orthogonal pure spectrum obtained from the iterative
processing unit. Further, the method for obtaining blood glucose
concentration comprises checking if a certain number of the
orthogonal pure spectrums are obtained. Further, the method for
obtaining blood glucose concentration comprises sending back the
deflated NIR spectra to the iterative processing unit to obtain a
new orthogonal pure spectrum if the certain number of the
orthogonal pure spectrums are not obtained.
[0045] Further, the method for obtaining blood glucose
concentration is based on a processing module applying one or more
preproces sings and drift removal techniques on the human NIR
spectra and the orthogonal pure spectra to obtain a preprocessed
spectra. The ICA temporal module filters the human NIR spectra and
obtains the orthogonal pure spectra. The orthogonal pure spectra
along with the human NIR spectra are transmitted to the processing
module that receives the orthogonal pure spectra and human NIR
spectra, and applies one or more processing and drift removal
techniques to the human NIR spectra and the orthogonal pure spectra
to obtain preprocessed spectra. Upon applying one or more
preprocessings and drift removal techniques to the human NIR
spectra and the orthogonal pure spectra components, the processing
module obtains the preprocessed spectra.
[0046] In an exemplary embodiment, obtaining the preprocessed
spectra comprises applying an extended multiplicative scatter
correction (EMSC) method on the human NIR spectra and the
orthogonal pure spectra. Further, the method for obtaining blood
glucose concentration comprises applying, by means of a Fast
Fourier Transform (FFT) block, filtering methods to obtain filtered
spectra, wherein obtaining the filtered spectra comprises applying
a Fourier domain filtering on the human NIR spectra after
performing the EMSC method to reduce the impact of noise on the
human NIR spectra by using a Hanning Window, and differentiating,
with respect to a wavelength, the Fourier domain filtered spectra
to remove the impact of drift, which is constant with respect to
the wavelength to obtain the filtered spectra. Further, the method
for obtaining blood glucose concentration comprises applying, on
the filtered spectra, drift removal techniques to obtain the
preprocessed spectra.
[0047] Further, the method for obtaining blood glucose
concentration comprises a regression block obtaining a glucose
concentration from the preprocessed spectra. The processing module
transmits the preprocessed spectra to the regression block. The
regression block receives the preprocessed spectra and provides the
data to the feature extraction block, wherein the feature
extraction block extracts one or more features from the obtained
preprocessed spectra. Further, the method for obtaining blood
glucose concentration comprises obtaining a training data set and a
validation data set from the feature. Further, the method for
obtaining blood glucose concentration comprises obtaining the
glucose concentration upon performing regression on the training
data set and the validation data set.
[0048] FIG. 1 illustrates a schematic flow diagram 100 illustrating
a method for obtaining blood glucose concentration using temporal
independent component analysis (ICA), according to an exemplary
embodiment.
[0049] According to the flow diagram 100, at operation 102, an
Independent Component Analysis (ICA) temporal module obtains
orthogonal pure spectra from human NIR spectra. Further, at
operation 104, a processing module applies one or more
preprocessings and drift removal techniques on the human NIR
spectra and the orthogonal pure spectra to obtain preprocessed
spectra. Further, at operation 106, a regression block obtains a
glucose concentration from the preprocessed spectra.
[0050] FIG. 2 is a schematic block diagram of a user device (or an
apparatus for obtaining blood glucose concentration) 200
illustrating components for obtaining blood glucose concentration
using temporal independent component analysis (ICA), according to
an exemplary embodiment.
[0051] According to an exemplary embodiment shown in FIG. 2, the
user device 200 includes at least one processor 201 including an
independent component analysis (ICA) temporal block 202, a
processing block 204, and a regression block 206. Further, the ICA
temporal block 202 comprises a pre-data whitening unit 208, a
learning algorithm unit 210, and a deflation module 212. Further,
the processing block 204 comprises an extended multiplicative
scatter correction (EMSC) module 214, a Fast Fourier Transform
(FFT) block 216, and a drift removal module 218. Further, the
regression block 206 comprises a feature extraction block 220, a
separation block 222, a training block 224, and a regression model
identifier block 226. According to an exemplary embodiment,
Independent Component Analysis (ICA) is a method used to separate
multivariate signal into additive components. The ICA defines a
generative model for the observed multivariate data, which is
typically given as a large database of samples. The ICA is a case
of Blind Source Separation, wherein the ICA is a statistical and
computational technique for revealing hidden factors that underlie
sets of random variables, measurements, or signals.
[0052] Consider an example, in which a random data vector
x=(x.sub.1, x.sub.2, . . . x.sub.m).sup.T is given as a weighted
sum of independent components s.sub.p, p=1, . . . n, such that
x=.SIGMA..sub.pa.sub.p*s.sub.p (3)
[0053] where a.sub.p are mixing weights.
[0054] The ICA is used to transform observed data x, using linear
transformation W into maximally independent components y as
y=W*x (4)
[0055] Conventional ICA techniques are based on a principle of
assuming non Gaussianity and statistical independence of source
signals. The requirement of assuming non Gaussianity and
statistical independence of source signals does not permit its use
to many real life scenarios where the source signals (y) are
commonly dependent on each other. In non-invasive continuous
glucose monitoring (CGM), the pure spectra of components in skin
such as, but not limited to, glucose, water, fat, collagen,
keratin, acid, and the like have a high correlation. Further, a
mean normalized spectrum extracted for one or more components is
not capable of appropriately capturing the peaks of the pure
spectra of components. Thus, the peaks of the pure spectra of
components cannot be extracted from NIR spectra through
conventional ICA techniques.
[0056] The ICA temporal block 202 according to an exemplary
embodiment overcomes the above discussed problem, wherein the ICA
temporal block 202 works on a batch learning method for sequential
blind source extraction. Further, the ICA temporal block 202 works
on signals obtained from non-additive white (i.i.d.) temporally
correlated sources. The ICA temporal block 202 comprises the
pre-data whitening unit 208, the learning algorithm unit 210, and
the deflation module 212.
[0057] The pre-data whitening unit 208 receives human NIR spectra.
The pre-data whitening unit 208 further provides data whitened NIR
spectra after removing error from the human NIR spectra. Further,
the pre-data whitening unit 208 calculates Eigen vectors of the
human NIR spectra using singular value decomposition method.
Further, a whitening transformation is applied by the pre-data
whitening unit 208 using the Eigen vectors of the human NIR spectra
to find the data whitened NIR spectra.
[0058] Further, the pre-data whitening unit 208 provides the data
whitened NIR spectra to the learning algorithm unit 210 that
calculates, using an iterative processing unit, an orthogonal pure
spectrum from the data whitened NIR spectra. The learning algorithm
unit 210 comprises a single processing unit, wherein the learning
algorithm unit 210 computes an estimate spectrum based on the data
whitened NIR spectra and a residual error while computing the
estimate spectrum, wherein computing the estimate spectra from the
single processing unit comprises randomly initializing the learning
parameters, the learning parameters comprising a weight vector and
a bias vector. Further, the learning algorithm unit 210 obtains an
estimate spectrum based on the weight vector and the bias vector,
and computes source statistics for the estimate spectrum comprising
a Cross correlation and Covariance matrix.
[0059] Further, the learning algorithm unit 210 of the ICA temporal
block 202 reiterates, over the estimate spectrum, operations of the
single processing unit, to obtain orthogonal pure spectra, wherein
the reiterating comprises calculating updated values of the weight
vector and the bias vector based on the source statistics of the
estimate spectrum, calculating the updated estimate spectrum based
on the updated values of the weight vector and the bias vector,
assigning the updated estimate spectrum as the orthogonal pure
spectrum in response to convergence being achieved for the weight
vector, and reiterating the operations of the single processing
unit in response to convergence not being achieved.
[0060] Further, the learning algorithm unit 210 provides the
orthogonal pure spectrum value to the deflation module 212 that
calculates new deflated NIR spectra to be sent back to the
iterative processing unit after removing the effect of the
orthogonal pure spectra. The deflation module 212 deflates the data
whitened NIR spectra based on the orthogonal pure spectrum obtained
from the iterative processing unit. Further, the deflation module
212 checks whether a certain number of orthogonal pure spectrum are
obtained. Further, the deflation module 212 transmits back the
deflated NIR spectra to the iterative processing unit to obtain a
new orthogonal pure spectrum in response to the required number of
orthogonal pure spectra not being obtained.
[0061] According to an exemplary embodiment, the ICA temporal block
202 does not assume statistical independence or non-zero kurtosis
for the source signals (e.g., pure spectra), but only assumes
different temporal structures for the pure spectra, which is true
as they have different auto correlation. The ICA temporal block 202
uses a method based on second order statistics to compute
orthogonal pure spectra, thus is computationally efficient than
related art methods.
[0062] For instance, consider that the ICA temporal block 202
estimates each orthogonal pure spectrum from the human NIR spectra
one at a time. Assume the human NIR spectra to be x(k)=[x.sub.1(k),
x.sub.2(k), . . . x.sub.m(k)]T for each time instant k, wherein x
can be represented as:
x(k)=As(k)+n(k) (5)
[0063] where A is an m.times.n unknown mixing matrix or
concentrations,
[0064] s(k) is a vector of unknown pure spectra, and
[0065] n(k) is an additive white (i.i.d.) noise vector.
[0066] In the equation (5), it is required to determine maximally
independent components/orthogonal pure spectrum, y(k), which can
optimally represent s(k).
<Operation of Pre Data Whitening>
[0067] Using the ICA temporal block method according to an
exemplary embodiment, the human NIR spectra X is mean and standard
deviation normalized to give X, wherein X is transmitted to a
pre-data whitening block to calculate eigen vectors of the human
NIR spectra and apply the whitening transformation, to obtain data
whitened NIR spectra. The eigen vectors E and D can be calculated
using:
[E, D]=eig(X'*X)
[0068] The whitening transformation to obtain the data whitened NIR
spectra {tilde over (X)} is expressed in Equation (6).
{tilde over (X)}=ED1/2EX (6)
[0069] Further, data whitening renders the covariance matrix of
data whitened NIR spectra R.sub.xx to be equal to I.sub.n:
R.sub.xx=E(*{tilde over (X)})=I.sub.n (7)
[0070] This helps to ensure that data whitened NIR spectra {tilde
over (X)} is orthogonal and their projections over each other are
zero.
<Operation of Learning Block>
[0071] In the learning block, each spectrum is calculated through
Blind extraction technique. Assume the Data whitened NIR spectra to
be x(k) and the first spectra y.sub.1(k) need to be extracted.
Then, a single processing unit is described as:
y.sub.1(k)=w.sub.1.sup.T*x(k)=.SIGMA..sub.j=1.sup.mw.sub.1jx.sub.j(k)
(8)
.epsilon..sub.1(k)=y.sub.1(k)-.SIGMA..sub.p-.sup.Lb.sub.1py.sub.1(k-p)=w-
.sub.1.sup.T*x(k)-b.sub.1.sup.T (9)
where w.sub.1=[w.sub.11, w.sub.12, . . . , w.sub.1m].sup.T
=[y.sub.1(k-1), y.sub.1(k-2), y.sub.1(k-L)].sup.T
b.sub.1=[b.sub.11, b.sub.12, b.sub.1L].sup.T
[0072] The outputs of the single processing unit y.sub.1(k) and
.epsilon..sub.1(k) represent the extracted spectra, and the error
after passing y.sub.1(k) by an FIR filter b.sub.1 respectively.
[0073] FIG. 3 is a schematic diagram illustrating single processing
unit 300 for extracting spectra, according to an exemplary
embodiment.
[0074] According to an exemplary embodiment, the single processing
unit 300 extracts one orthogonal pure spectrum from a plurality of
orthogonal pure spectrums in the received data whitened NIR
spectra. The single processing unit 300 estimates the optimal
values of vectors w.sub.1 and b.sub.1 so as to extract spectra.
Hence, a cost function J(w.sub.1, b.sub.1) is defined as:
J(w.sub.1, b.sub.1)=E{.epsilon..sup.2} (10)
[0075] From Equations 9 and 10, the result obtained can be defined
as:
J(w.sub.1,
b.sub.1)=w.sub.1.sup.TR.sub.xxw.sub.1-2w.sub.1.sup.Tb.sub.1+b.sub.1.sup.T-
b.sub.1 (11)
[0076] where Covariance Matrix R.sub.xx=E{xx.sup.T} and
[0077] Cross correlation Matrices
= , = ##EQU00002##
[0078] Further, the learning algorithm unit of ICA temporal block
202 minimizes cost function J(w.sub.1, b.sub.1) to estimate each
orthogonal pure spectrum. By differentiating J(w.sub.1, b.sub.1)
with respect to w.sub.1 and b.sub.1 separately and equating them to
0, the result obtained would be:
w.sub.1=R.sub.xx.sup.-1b.sub.1 (12)
b.sub.1=.sup.-1w.sub.1=.sup.-1 (13)
[0079] Equations (12) and (13) together represent an iterative
method, similar to an expectation-maximization (EM) method where
the parameters of the previous iteration are used to learn new
statistics. Also, from Equation (7), R.sub.xx=I.sub.n. Therefore,
Equation (12) becomes:
w.sub.1=b.sub.1 (14)
[0080] Therefore, the operations of a single processing unit for
ICA temporal can be summarized as: [0081] 1. Randomly initializing
w.sub.1 and b.sub.1. [0082] 2. Obtaining y.sub.1 using the current
value of w.sub.1 (Equation (8)). [0083] 3. Computing the statistics
of source (R.sub.xx, and R) keeping w.sub.1 and b.sub.1 constant.
[0084] 4. Updating the learning parameters w.sub.1 and b.sub.1
using Equations (12) and (13). [0085] 5. In response to w.sub.1
convergence being achieved, extracting current y.sub.1 spectra.
[0086] Otherwise, above operations are repeated.
[0087] Further, the deflation module 212 deflates the input to
remove the effect of orthogonal pure spectrum derived in a previous
operation from data whitened NIR spectra using Equation (15):
x.sub.i+1(k)=x.sub.i(k)-{tilde over (w)}.sub.1*y.sub.i(k) (15)
[0088] where x.sub.i+1(k) is the deflated NIR spectra.
[0089] Further, {tilde over (w)}.sub.i is calculated by minimizing
mean square cost function J.sub.i({tilde over
(w)}.sub.i)=E{x.sub.i+1.sup.Tx.sub.i+1} with respect to {tilde over
(w)}.sub.i, which will give:
w ~ i = E .times. { x i .times. y i } E .times. { y i 2 } = E
.times. { x i .times. x i T } .times. w i E .times. { y i 2 } ( 16
) ##EQU00003##
[0090] where w.sub.i is the learning parameter obtained from the
last iterative operation. Since, by the pre-data whitening unit
208, the human NIR spectra were whitened to obtain data whitened
spectra, Equation (16) can be simplified to:
{tilde over (w)}.sub.i=w.sub.i (17)
[0091] The value of {tilde over (w)}.sub.i is fed back to the
learning algorithm in an iterative fashion to derive the next
orthogonal pure spectrum. The above operations are repeated till a
certain number of orthogonal pure spectrums are obtained, wherein
the orthogonal pure spectrum extracted is appropriately capable of
capturing the peaks in all range of wavenumber, adequately
representing the pure spectra.
[0092] Further, the processing block 204 of the user device 200
comprises the EMSC module 214, the Fast Fourier Transform (FFT)
block 216, and the drift removal module 218. The processing block
204 applies one or more preprocessings and drift removal techniques
on the orthogonal pure spectra components to obtain preprocessed
spectra. The processing block 204 receives the human NIR spectra
and orthogonal pure spectra from the ICA temporal block 202, and
provides the data to the extended multiplicative scatter correction
(EMSC) module 214. The EMSC module 214 applies an extended
multiplicative scatter correction (EMSC) method on the human NIR
spectra, uses the orthogonal pure spectra and regress for their
compositions in the NIR spectra. For instance, let Y be any human
NIR spectra comprising various pure spectra X.sub.1, X.sub.2, . . .
, Xk for different blood components. Then, Y can be obtained using
simple Linear Regression at any given wavelength as follows:
Y(n)=a.sub.0+.SIGMA..sub.k=1.sup.Ma.sub.kX.sub.k (18)
[0093] where a.sub.k are the strengths of blood component and
a.sub.0 is a DC component.
[0094] Taking X.sub.1 as the glucose spectra, the glucose spectra
can be obtained by subtracting other components in the given
spectra:
X.sub.1=Y-a.sub.0-.SIGMA..sub.k=2.sup.Ma.sub.kX.sub.k (19)
[0095] The data can be further provided to the FFT block 216,
wherein FFT filtering methods are applied to reduce impact of noise
on the spectra and obtain filtered spectra. In the FFT filtering
method, a Fourier domain filtering is performed on the human NIR
spectra after EMSC is applied to reduce the impact of noise on the
spectra by using a Hanning Window of size N.sub.win, which is
expressed in the following equations:
X(k)=FFT(x(n))
x.sub.fd(t)=IFFT(X(k)*Hanning(N.sub.win)
[0096] Further, the FFT block 216 comprises a differential block
that acts as a supplement to the drift removal block to remove the
impact of constant drift with respect to a wavelength.
Mathematically, the differential block is denoted as
d .function. ( X .function. ( k ) ) d .times. .times. .lamda. .
##EQU00004##
[0097] With respect to a wavelength, the differential block
differentiates the Fourier domain filtered spectra to remove the
impact of drift, which is constant with respect to a wavelength, to
obtain filtered spectra. Based on the filtered spectra obtained
from the FFT block and the differential block, a correlation value
for each feature index with respect to glucose without and with the
FFT filtering and the differentiation by the differential block can
be obtained. From the comparison, it can be observed that there is
an increase in the correlation for the most of the indices after
the FFT filtering and the differentiation.
[0098] Further, the data from the FFT block 216 can be provided to
the drift removal module 218 that applies a drift removal method to
obtain the preprocessed spectra by removing the effect of
experimental/instrumental drift in the NIR spectra. During drift
removal method, it is assumed that NIR spectra contain only linear
drift.
[0099] Further, the user device 200 comprises the regression block
206 that performs regression on the processed data and calculates
glucose output from the processed data. The regression block 206
comprises the feature extraction block 220, the separation block
222, the training block 224, and the regression model identifier
block 226. The feature extraction block 220 receives the spectra
obtained after data processing block as input and extracts one or
more features associated with the processed data signal. The
features are the wavelength spectrums which show consistent high
correlations with the glucose concentration. The obtained features
are provided to the separation block 222 that separates training
data set and validation data set.
[0100] The training data set is further provided to the training
block 224 that receives the training data set and trains the
regression model. In an exemplary embodiment, the training block
224 uses a principal components regression (PCR) method for
training the data set. This is merely an example and the disclosure
is not limited thereto. The person having ordinarily skill in the
art can use any of other similar known methods of regression for
training the regression model, without departing from the scope of
the invention. Further, the regression model identifier block 226
receives the trained data set from the training block 224 and
validation data set from the separation block 222, and obtains the
glucose concentration upon performing regression on the training
data set and the validation data set.
[0101] At least one of the components, elements or units
represented by a block in the drawings may be embodied as various
numbers of hardware, software and/or firmware structures that
execute respective functions described above, according to an
exemplary embodiment. For example, at least one of these
components, elements or units may use a direct circuit structure,
such as a memory, processing, logic, a look-up table, etc. that may
execute the respective functions through controls of one or more
microprocessors or other control apparatuses. Also, at least one of
these components, elements or units may be specifically embodied by
a module, a program, or a part of code, which contains one or more
executable instructions for performing specified logic functions.
Also, at least one of these components, elements or units may
further include a processor such as a central processing unit (CPU)
that performs the respective functions, a microprocessor, or the
like. Further, although a bus is not illustrated in the above block
diagrams, communication between the components, elements or units
may be performed through the bus. Functional aspects of the above
exemplary embodiments may be implemented in algorithms that execute
on one or more processors. Furthermore, the components, elements or
units represented by a block or processing steps may employ any
number of related art techniques for electronics configuration,
signal processing and/or control, data processing and the
like..
[0102] The "unit" or "module" used herein may be a hardware
component, such as a processor or a circuit, and/or a software
component that is executed by a hardware component such as a
processor.
[0103] Although a few embodiments have been shown and described, it
would be appreciated by those skilled in the art that changes may
be made in the exemplary embodiments without departing from the
principles and spirit of the disclosure, the scope of which is
defined in the claims and their equivalents.
* * * * *