Method and system for non-invasive optical blood glucose detection utilizing spectral data analysis

Information

  • Patent Grant
  • 8340738
  • Patent Number
    8,340,738
  • Date Filed
    Friday, April 17, 2009
    15 years ago
  • Date Issued
    Tuesday, December 25, 2012
    11 years ago
Abstract
Systems and methods are disclosed for non-invasively measuring blood glucose levels in a biological sample based on spectral data. A variety of techniques are disclosed for improving signal-to-noise ratio in the acquisition of spectral data and calculating attenuance of light attributable to blood in a sample. Disclosed techniques include (1) using a standard deviation operation in conjunction with the logarithm function, (2) using a normalization factor, (3) using a ratio factor, (4) accounting for the effect of temperature on various system components such as resistors, and (5) accounting for dark current in a light detector by performing a calibration.
Description
BACKGROUND OF THE INVENTION

Diabetes is a chronic disease that, when not controlled, over time leads to serious damage to many of the body's systems, including the nerves, blood vessels, eyes, kidneys and heart. The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) estimates that 23.6 million people or 7.8 percent of the population in the United States had diabetes in 2007. Globally, the World Health Organization (WHO) estimates that more than 180 million people have diabetes, a number they expect to increase to 366 million by 2030, with 30.3 million in the United States. According to the WHO, an estimated 1.1 million people died from diabetes in 2005. They project that diabetes deaths will increase by more than 50% between 2006 and 2015 overall and by more than 80% in upper-middle income countries.


The economic burden from diabetes for individuals and society as a whole is substantial. According to the American Diabetes Association, the total annual economic cost of diabetes was estimated to be $174 billion in the United States in 2007. This is an increase of $42 billion since 2002. This 32% increase means the dollar amount has risen over $8 billion more each year.


A vital element of diabetes management is the self-monitoring of blood glucose (SMBG) concentration by diabetics in the home environment. By testing blood glucose levels often, diabetics can better manage medication, diet, and exercise to maintain control and prevent the long-term negative health outcomes. In fact, the Diabetes Control and Complications Trial (DCCT), which followed 1,441 diabetics for several years, showed that those following an intensive-control program with multiple blood sugar tests each day as compared with the standard-treatment group had only one-fourth as many people develop diabetic eye disease, half as many develop kidney disease, one-third as many develop nerve disease, and far fewer people who already had early forms of these three complications got worse.


However, current monitoring techniques discourage regular use due to the inconvenient and painful nature of drawing blood through the skin prior to analysis, which causes many diabetics to not be as diligent as they should be for good blood glucose control. As a result, non-invasive measurement of glucose concentration is a desirable and beneficial development for the management of diabetes. A non-invasive monitor will make testing multiple times each day pain-free and more palatable for children with diabetes. According to a study published in 2005 (J, Wagner, C. Malchoff, and G. Abbott, Diabetes Technology & Therapeutics, 7(4) 2005, 612-619), people with diabetes would perform SMBG more frequently and have improved quality of life with a non-invasive blood glucose monitoring device.


There exist a number of non-invasive approaches for blood glucose determination. One technique of non-invasive blood chemicals detection involves collecting and analyzing light spectra data.


Extracting information about blood characteristics such as glucose concentration from spectral or other data obtained from spectroscopy is a complex problem due to the presence of components (e.g., skin, fat, muscle, bone, interstitial fluid) other than blood in the area that is being sensed. Such other components can influence these signals in such a way as to alter the reading. In particular, the resulting signal may be much larger in magnitude than the portion of the signal that corresponds to blood, and therefore limits the ability to accurately extract blood characteristics information.


The present invention is directed to overcoming one or more of the problems set forth above.


SUMMARY OF INVENTION

In an aspect of the present invention, a system for detecting glucose in a biological sample is disclosed. This system includes at least one light source configured to strike a target area of a sample, at least one light detector positioned to receive light from the at least one light source and to generate an output signal, having a time dependent current, which is indicative of the power of light detected, and a processor that receives the output signal from the at least one light detector and based on the received output signal, calculates the attenuance attributable to blood in a sample present in the target area with a signal-to-noise ratio of at least 20-to-1, and based on the calculated attenuance, determines a blood glucose level associated with a sample present in the target area.


In another aspect of the present invention, a system for detecting glucose in a biological sample is disclosed. The system includes at least one light source configured to strike a target area of a sample, at least one light detector positioned to receive light from the at least one light source and to generate an output signal, having a time dependent current, which is indicative of the power of light detected, and a processor that receives the output signal from the at least one light detector and based on the received output signal, calculates the attenuance attributable to blood in a sample present in the target area with a normalization factor, and based on the calculated attenuance, determines a blood glucose level associated with a sample present in the target area.


In yet another aspect of the present invention, a system for detecting glucose in a biological sample is disclosed. The system includes at least one light source configured to strike a target area of a sample, at least one light detector positioned to receive light from the at least one light source and to generate an output signal, having a time dependent current, which is indicative of the power of light detected, and a processor that receives the output signal from the at least one light detector and based on the received output signal, calculates the attenuance attributable to blood in a sample present in the target area with a ratio factor, and based on the calculated attenuance, determines a blood glucose level associated with a sample present in the target area.


In still another aspect of the present invention, a system for detecting glucose in a biological sample is disclosed. The system includes at least one light source configured to strike a target area of a sample, at least one light detector positioned to receive light from the at least one light source and to generate an output signal, having a time dependent current, which is indicative of the power of light detected, and a processor that receives the output signal from the at least one light detector and based on the received output signal, calculates the attenuance attributable to blood in a sample present in the target area and eliminates effect of uncertainty caused by temperature dependent detector response of the at least one light detector, and based on the calculated attenuance, determines a blood glucose level associated with a sample present in the target area.


In still yet another aspect of the present invention, a system for detecting glucose in a biological sample is disclosed. The system includes at least one light source configured to strike a target area of a sample, at least one light detector, which includes a preamplifier having a feedback resistor, positioned to receive light from the at least one light source and to generate an output signal, having a time dependent current, which is indicative of the power of light detected, and a processor that receives the output signal from the at least one light detector and based on the received output signal, calculates the attenuance attributable to blood in a sample present in the target area and eliminates effect of uncertainty caused by temperature dependent detector response of the at least one light detector, and based on the calculated attenuance, determines a blood glucose level associated with a sample present in the target area.


In another aspect of the present invention, a system for detecting glucose in a biological sample is disclosed. The system includes at least one light detector having a preamplifier and a feedback resistor.


In yet another aspect of the present invention, a method for detecting glucose in a biological sample is disclosed. The method includes utilizing at least one light source configured to strike a target area of a sample, utilizing at least one light detector positioned to receive light from the at least one light source and to generate an output signal, having a time dependent current, which is indicative of the power of light detected, receiving the output signal from the at least one light detector with a processor and based on the received output signal, calculating the attenuance attributable to blood in a sample present in the target area with a signal-to-noise ratio of at least 20-to-1, and determining a blood glucose level associated with a sample present in the target area based on the calculated attenuance with the processor.


In still another aspect of the present invention, a method for detecting glucose in a biological sample is disclosed. The method includes utilizing at least one light source configured to strike a target area of a sample, utilizing at least one light detector positioned to receive light from the at least one light source and to generate an output signal, having a time dependent current, which is indicative of the power of light detected, receiving the output signal from the at least one light detector with a processor and based on the received output signal, calculating the attenuance attributable to blood in a sample present in the target area with a normalization factor with the processor, and determining a blood glucose level associated with a sample present in the target area based on the calculated attenuance with the processor.


In yet another aspect of the present invention, a method for detecting glucose in a biological sample is disclosed. The method includes utilizing at least one light source configured to strike a target area of a sample, utilizing at least one light detector positioned to receive light from the at least one light source and to generate an output signal, having a time dependent current, which is indicative of the power of light detected, receiving the output signal from the at least one light detector with a processor, calculating the attenuance attributable to blood in a sample present in the target area with a ratio factor based on the received output signal with the processor, and determining a blood glucose level associated with a sample present in the target area based on the calculated attenuance with the processor.


In another aspect of the present invention, a method for detecting glucose in a biological sample is disclosed. The method includes utilizing at least one light source configured to strike a target area of a sample, utilizing at least one light detector positioned to receive light from the at least one light source and to generate an output signal, having a time dependent current, which is indicative of the power of light detected, receiving the output signal from the at least one light detector with a processor and based on the received output signal, calculating the attenuance attributable to blood in a sample present in the target area with a ratio factor with the processor, eliminating effect of uncertainty caused by temperature dependent detector response of the at least one light detector with the processor, and determining a blood glucose level associated with a sample present in the target area with the processor based on the calculated attenuance with the processor.


These are merely some of the innumerable aspects of the present invention and should not be deemed an all-inclusive listing of the innumerable aspects associated with the present invention.





BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the present invention, reference may be made to accompanying drawings, in which:



FIG. 1 illustrates a plot of a pulse wave corresponding to light absorption of arterial blood, according to exemplary embodiments;



FIG. 2 illustrates an exemplary system for obtaining spectral data;



FIG. 3 illustrates a plot of A(t), calculated according to Equation (9) using data in FIG. 1; and



FIG. 4 is a basic illustrative schematic of a preamplifier circuit that coverts photocurrent into voltage prior to digitization.





DETAILED DESCRIPTION OF THE INVENTION

In the following detailed description, numerous exemplary specific details are set forth in order to provide a thorough understanding of the invention. However, it will be understood by those skilled in the art that the present invention may be practiced without these specific details, or with various modifications of the details. In other instances, well known methods, procedures, and components have not been described in detail so as not to obscure the present invention.


Optical spectroscopy can be used to determine the amount of light absorbed and scattered, i.e., attenuated, by a biological sample such as a human finger. By measuring the amount of light absorbed by the sample, it is possible to determine glucose, cholesterol, and hemoglobin levels of a subject non-invasively. Fingertip measurements are usually preferred because of the large concentration of capillaries in the fingertip and because of the conversion of arterial blood into venous blood that occurs in the fingertip. However, the techniques of the present invention are not limited to use with a fingertip. For example, the biological sample could be a human earlobe.


When light is transmitted through a biological sample, such as a human finger, the light is attenuated by various components of the finger including skin, muscle, bone, fat, interstitial fluid and blood. It has been observed, however, that light attenuation by a human finger exhibits a small cyclic pattern that corresponds to a heartbeat. It is believed that this cyclic pattern will be present in measurements of many other human body parts, the earlobe being one of many examples.



FIG. 1 depicts a plot 102 of a detector photocurrent, ID(t), that corresponds to the power of light received by a detector after the light has passed through a subject's finger. As can be seen, the detector photocurrent exhibits a cyclic pattern. This cyclic pattern is due to the subject's heartbeat, which cyclically increases and decreases the quantity of blood in the subject's capillaries (or other structures). Although the magnitude of the cyclic pattern is small in comparison to the total photocurrent generated by the detector, considerable information can be extracted from the cyclic pattern of the plot 102. For example, assuming that the person's heart rate is sixty beats per minute, the time between the start of any pulse beat and the end of that pulse beat is one second. During this one-second period, the photocurrent will have a maximum or peak reading 104 and minimum or valley reading 106. The peak reading 104 of the plot corresponds to when there is a minimum amount of blood in the capillaries, and the valley reading 106 corresponds to when there is a maximum amount of blood in the capillaries. By using information provided by the peak and valley of the cyclic plot, the optical absorption and scattering by major finger constituents that are not in the capillaries such as skin, fat, bones, muscle and interstitial fluids are excluded. These major constituents that are not in the capillaries are excluded because they are not likely to change during the time interval of one heartbeat. In other words, the light that is absorbed and scattered, i.e., attenuated, by the blood can be detected based on the peaks and valleys of the plot 102.


Assuming that the peak of the cyclic photocurrent generated by the light-sensing device is IP, the adjacent valley of the cyclic photocurrent is IV, and the photocurrent generated by the light-sensing device without a human finger is I0, the transmittances corresponding to the peak and valley photocurrents can be defined as:











T
V

=


I
V


I
0








and




(
1
)







T
P

=


I
P


I
0






(
2
)







The corresponding peak and valley absorbance are:

AV=−log(TV)  (3)
and
AP=−log(TP)  (4)

The difference between AV and AP represents the light absorption and scattering by the blood in the finger, excluding non-blood constituents:










Δ





A

=



A
V

-

A
P


=

log


(


I
P


I
V


)







(
5
)







As can be seen in the algorithm shown in Equation (5), ΔA does not depend on I0. Thus, calculating ΔA does not require a determination of the current generated by the light-sensing device without a sample. Monitoring the photocurrent corresponding to light power transmitted through a sample is sufficient to calculate ΔA.



FIG. 2 depicts a simplified block diagram of an exemplary apparatus for use in an exemplary embodiment. Optical measurement system, which is generally indicated by numeral 200, uses the “pulsatile” concept for determining an amount of light absorbed and scattered solely by the blood in a sample (a human finger in this exemplary embodiment). A power source 201, such as a battery, provides power to a light source 202 that generates a plurality of light beams 204, 206, 208, 210 that are directed toward the top of the finger of a subject. In an exemplary embodiment, each of the light beams 204, 206, 208, 210 have the same wavelength or a different wavelength range, typically within 800 nm to 1600 nm. Although the optical measurement system 200 is described herein as generating four (4) light beams, it is contemplated that the light source 202 can be altered to generate fewer light beams or additional light beams in other embodiments.


A first aperture 212 ensures that the light beams 204, 206, 208, 210 strike a target area of the finger. A second aperture 214 ensures that the portion of the light beams that are transmitted through the finger strike a lens 216. Light beams 204, 206, 208, 210 are attenuated by the finger and components of the optical measurement system 200, and, thus, attenuated light beams 218, 220, 222, 224 are emitted from the finger. The attenuated light beams 218, 220, 222, 224 strike the lens 216, and the lens 216 collects the attenuated light beams 218, 220, 222, 224 so that they impinge more efficiently on a detector block 226.


The detector block 226 is positioned directly under the lens 216 and comprises a plurality of light-sensing devices (LSD) 228, 230, 232, 234 such as an array of photodiodes. According to one aspect of the optical measurement system 200, each of the light-sensing devices 228, 230, 232, 234 detects a specific wavelength of light as defined by corresponding interference filters (IF) 236, 238, 240, 242, respectively. The interference filter transmits one or more spectral bands or lines of light, and blocks others.


Each of the light-sensing devices 228, 230, 232, 234 generates a corresponding photocurrent signal that is proportional to the power of the light received by the particular light sensing device. The photocurrent signal generated by the photodiode can be converted to another form of signal, such as an analog voltage signal or a digital signal. A processor 243 is coupled to the detector block 226 and is configured to calculate the change of photocurrent signals 244, 246, 248, 250.


According to one aspect, the processor 243 executes an algorithm such as shown in the Equation (5) to calculate the change in the light absorption (ΔA) solely caused by the blood in the finger. Thereafter, this quantitative calculation of light absorption of the blood can be used to determine a characteristic of the blood. For example, by comparing the calculated light absorption value to predetermined values corresponding to different glucose levels stored in a memory (not shown), a blood-glucose level of the subject can be determined.


A difficulty associated with the finger based pulsatile detection methodology is low signal-to-noise (S/N) ratio, because the amplitude of cyclic pattern (i.e., the difference between peak and valley) is typically 1%-2% of the total photocurrent generated by the light power transmitted through the finger. To obtain a S/N ratio of 100:1 in the determination of ΔA, the baseline noise of the device being used to measure the light absorption by the finger should not be larger than 3.0×10−5 in absorbance (peak to peak), within a 10 Hz bandwidth.


However, a 3.0×10−5 absorbance (peak to peak) baseline noise level within a 10 Hz bandwidth is difficult to obtain with the low light power levels that are used by some battery-powered hand held non-invasive blood chemicals measurement devices. One solution involves data averaging. To increase the S/N ratio, the averaged value of ΔA, as defined by the Equation below, is used in further calculation to extract blood glucose concentration:











Δ





A

_

=




j
=
1

M







Δ






A
j







(
6
)







In Equation (6), M is the number of heartbeats during the time interval of the pulsatile measurement. However, this approach requires long data acquisition time, due to the fact that the rate of heartbeat is in the order of one per second. For example, 25 seconds would be needed for increasing the S/N ratio by a factor of five, and 100 seconds would be needed for increasing the S/N ratio by a factor of ten. In comparison, current commercial blood drawing glucose meters can determine blood glucose level within 5 seconds. Furthermore, long detection time will significantly increase measurement errors due to finger movement, light power drift, device temperature change, etc. Thus, there is a need for new techniques to measure blood glucose levels quickly and accurately.


Improving S/N Ratio by Standard Deviation


The time dependent detector photocurrent output, ID(t), shown in FIG. 1 can be expressed as the sum of a small time dependent cyclic photocurrent ΔI(t), corresponding to the heartbeat, a noise current n(t), and a constant baseline photocurrent IB:

ID(t)=IB+ΔI(t)+n(t)  (7)


The above Equation can be re-arranged as:












I
D



(
t
)



I
B


=

1
+



Δ






I


(
t
)



+

n


(
t
)




I
B







(
8
)







Applying common logarithm to both side of the Equation (8), one obtains:










A


(
t
)


=


log


[



I
D



(
t
)



I
B


]


=

log


(

1
+



Δ






I


(
t
)



+

n


(
t
)




I
B



)







(
9
)








FIG. 3, which is generally indicated by numeral 300, shows a typical A(t) plot 302, calculated according Equation (9) using data in FIG. 1. For a pulse function A(t) shown in FIG. 3, the following key relationship exists during the time interval of one heartbeat:

σ[A(t)]=kΔA  (10)

in which σ[A(t)] is the Standard Deviation of A(t), and k is a proportional constant.


Considering the fact that IB is a constant and σ2 (log IB)=0, one obtains:

σ[A(t)]=σ[log ID(t)]  (11)


Therefore, the peak-to-valley height of the A(t) plot during the time interval of one heartbeat can be obtained directly from the standard deviation of the logarithm of ID(t):










Δ





A

=



σ


[

A


(
t
)


]


k

=


σ


[

log







I
D



(
t
)



]


k






(
12
)








A major advantage of Equation (12) is that high S/N ratio can be achieved within short data acquisition time (approximately one second), as explained below.


In a finger based pulsatile measurement depicted by FIG. 2, the value of the sum, ΔI(t)+n(t) is typically less than 2% of the large constant baseline photocurrent IB. Therefore, Equation (9) can be approximated as:










A


(
t
)


=


log


[



I
D



(
t
)



I
B


]





1

ln





10






Δ






I


(
t
)



+

n


(
t
)




I
B








(
13
)







Similarly, the standard deviation of A(t) can be approximated as:










σ


[

A


(
t
)


]





1

ln





10








σ
2



[

Δ






I


(
t
)



]


+


σ
2



[

n


(
t
)


]





I
B







(
14
)







Equation (14) demonstrates great noise reduction power of Equation (12). For example, for a relatively high baseline noise with the ratio







ρ
=



σ


[

n


(
t
)


]



σ


[

Δ






I


(
t
)



]



=

0.1






(


or  






10

%

)




,





the contribution to σ[A(t)] from the baseline noise n(t) is estimated to be less than 0.005 (or 0.5%), corresponding to an increase in S/N ratio by a factor of 20 without increasing detection time. As such, dramatic noise reduction can be obtained without increasing the data acquisition time, and a finger based pulsatile measurement can be completed within the time interval of one heartbeat (which is approximately one second), and the requirement for the S/N ratio of 100 to 1 in determination of ΔA can be satisfied using an optical system with a baseline noise of about 6.0×10−4 absorbance (peak to peak) within a 10 Hz bandwidth. It should be pointed out that when the baseline noise of an optical system is dominated by shot noise due to low light illumination power, a noise reduction by a factor of 20 equals an increasing in light illumination power by a factor of 202=400.


This ability of obtaining higher S/N ratio within the very short data acquisition time, e.g., less than one second, will significantly reduce detection error caused by factors such as finger movement, temperature change, and light power drift during the measurement, and therefore dramatically improve the accuracy and reproducibility of the pulsatile detection methodology.


Furthermore, the value of k does not change with wavelength, because transmitted lights at all wavelengths have identical pulse shape due to the heartbeat. As a result, the constant k will be cancelled in data normalization discussed in next section, and σ[log ID(t)] will be used in further regression analysis to establish correlation between the optical measurement and blood glucose level. This will greatly simplify the data analysis process since σ[log ID(t)] involves only two standard math functions available in most popular spreadsheet programs such as Microsoft EXCEL®. EXCEL® is a federally registered trademark of Microsoft Corporation, having a place of business at One Microsoft Way, Redmond, Wash. 98052-6399.


Normalization


At each wavelength λi, the absorption ΔA(λi) is linked to the increase of amount of blood (ΔB) in the optical sensing area of the fingertip due to the heartbeat by the following Equation:

ΔAi)=ε(C,λi,TB  (15)

in which ε(C,λi,T) is the absorption/scattering coefficient of blood at wavelength λi, finger temperature T, and blood glucose concentration C. It is well understood that the variable ΔB differs from person to person, and may even change from day to day for the same person.


The uncertainty from the variable ΔB can be cancelled by introducing the normalization factor Qi(C,T) at each wavelength λi, as defined by the Equation below:












Q
i



(

C
,
T

)


=



Δ






A


(

λ
i

)







i
=
1

N







Δ






A


(

λ
i

)





=


ɛ


(

C
,

λ
i

,
T

)






i
=
1

N



ɛ


(

C
,

λ
i

,
T

)






,




(
16
)








in which N is total number of wavelength employed. Preferably, N typically ranges from twenty to thirty.


Based on Equations (12) and (16), Qi(C,T) is linked to the detector photocurrent at each wavelength λi, IDi,t), by the following Equation:












Q
i



(

C
,
T

)


=



Δ






A


(

λ
i

)







i
=
1

N







Δ






A


(

λ
i

)













=




σ


[

log







I
D



(


λ
i

,
t

)



]


/
k





i
=
1

N




σ


[

log







I
D



(


λ
i

,
t

)



]


/
k











=


σ


[

log







I
D



(


λ
i

,
t

)



]






i
=
1

N



σ


[

log







I
D



(


λ
i

,
t

)



]







,




(
17
)







As shown by Equation (17), the constant k is cancelled and σ[log ID(t)] will be used in further regression analysis to establish correlation between the optical measurement and blood glucose level. This is possible because data are taken simultaneously from all detection channels.


A correlation between optical measurement and blood glucose concentration can be established according to the following Equation:










C
optical

=




i
=
1

N









a
i



(
T
)





Q
i



(

C
,
T

)








(
18
)








in which Coptical is the blood glucose concentration predicted by the optical measurement, Qi(C,T) is defined by Equations (16) and (17), and ai(T) is the temperature dependent regression coefficient corresponding to wavelength λi. The values of ai(T) can be extracted using proper statistics methods such as Partial Least Squares (PLS) regression.


Equation (18) represents ideal cases when large number of calibrations can be made at different finger temperatures. In reality, frequently only a limited number of calibrations can be made (e.g., 15 to 20), and each may be taken at a different finger temperature. Under this condition, the finger temperature can be treated as an independent variable, and the above Equation can be approximated as:










C
optical

=





i
=
1

N




b
i




Q
i



(

C
,
T

)




+

η





T






(
19
)








in which bi is the temperature independent regression coefficient corresponding to wavelength λi, and η is the regression coefficient for the finger temperature. The values of bi and that of η can be extracted using proper statistics methods such as Partial Least Squares (PLS) regression.


Ratio Methodology


Alternatively, the uncertainty from the variable ΔB can be cancelled by introducing a ratio factor Yij at wavelength λi:












Y
ij



(

C
,
T

)


=



Δ






A


(

λ
i

)




Δ






A


(

λ
j

)












=



ɛ


(

C
,

λ
i

,
T

)



ɛ


(

C
,

λ
j

,
T

)











=


σ


[

log







I
D



(


λ
i

,
t

)



]



σ


[

log







I
D



(


λ
j

,
t

)



]






,




(
20
)








in which j can be any number from 1 to N, assuming that the device collects signal at all N wavelengths.


Similar to the normalization algorithm discussed before, a correlation between optical measurement and blood glucose level can be established according to the following Equation:










C
optical

=




i

j

N





f
i



(
T
)





Y
ij



(

C
,
T

)








(
21
)








in which Coptical is the blood glucose concentration predicted by the optical measurement, Yij(C,T) is defined by Equation (20), and fi(T) is the temperature dependent regression coefficient corresponding to wavelength λi. The value of fi(T) can be obtained using statistics methods such as Partial Least Squares (PLS) regression.


Equation (21) represents ideal cases when large number of calibration can be made at different finger temperatures. In reality, frequently only limited number of calibration can be made (e.g., 15 to 20), and each may be taken at a different finger temperature. Under this condition, the finger temperature can be treated as an independent variable, and the above Equation can be approximated as:










C
optical

=





i

j

N




h
i




Y
ij



(

C
,
T

)




+

β





T






(
22
)








in which hi is the temperature independent regression coefficient corresponding to wavelength λi, and β is the regression coefficient for the finger temperature. The values of hi and that of β can be extracted using proper statistics methods such as Partial Least Squares (PLS) regression.


Elimination of the Effect of Temperature Dependent Device Response


It is well understood that the detector sensitivity of a silicon photodiode detector is a function of wavelength and temperature. For the device configuration shown in FIG. 2, which is generally indicated by numeral 200, the light power received by ith silicon diode detector, corresponding to wavelength λi, is converted into a photocurrent according to the following Equation:

IDi,t)=Pi,t)S0i)[1+γ(λi)(TDi(t)−25° C.)]  (23)


In the above Equation (23), P(λi,t) is the light power received by the detector, S0i) is the photosensitivity of the detector at wavelength λi and 25° C., γ(λi) is the temperature coefficient of the photosensitivity at wavelength λi, and TDi(t) is the temperature of ith photodiode detector. The temperature coefficient γ(λi) varies with the wavelength. For example, for Hamamatsu S1337 series photodiode detectors, γ(λi) ranges from near zero at 900 nm to over 1.0%/° C. at 1100 nm. This imposes a potential problem for the device configuration show in FIG. 2, because it is very difficult to keep temperature of each individual diode detector constant in a handheld device used by a person with diabetes under a normal household/office environment.


This uncertainty due to the detector temperature TDi(t) can be eliminated using the algorithm shown by Equations (11) and (12). Applying common logarithm on both sides of the Equation (23), one obtains:

log IDi,t)=log Pi,t)+log S0i)+log [1+γ(λi)(TDi(t)−25° C.)]  (24)


Considering the fact that S0i) is a constant and that detector temperature TDi(t) remains almost constant during the very short data acquisition time interval of approximately one second, one obtains:

σ[log IDi,t)]=σ[log Pi,t)]  (25)

As such, the uncertainty caused by detector temperature TDi(t) is eliminated by the use of this standard deviation methodology.


Voltage Detection Mode


In the device configuration shown in FIG. 2, the photocurrent of ith photodiode detector IDi,t) is typically converted into a voltage using a preamplifier before digitization. FIG. 4 shows the schematic circuit diagram of a typical preamplifier, which is generally indicated by numeral 400.


The output voltage 412 of ith preamplifier 400, in coupling with ith photodiode detector 408, can be expressed as:

Vi(t)=RiIDi,t)=R0i[1+χi(TRi(t)−25° C.)]IDi,t)  (26)


In the above Equation (26), R0i is the resistance value of feedback resistor 402 for ith preamplifier at 25° C., χi is the temperature coefficient of the resistor, and TRi(t) is the temperature of the resistor. Applying common logarithm to both side of the Equation (26), one obtains:

log Vi(t)=log Ri0+log [1+χi(TRi(t)−25° C.)]+log IDi,t)  (27)


Considering the fact that R0i is a constant and that the resistor temperature TRi(t) does not change during the very short data acquisition time interval of approximately one second, one obtains:

σ[log Vi(t)]=σ([log IDi,t)]  (28)


Substituting Equation (25) into Equation (28), one obtains:

σ[log Vi(t)]=σ[log Pi,t)]  (29)

As such, the uncertainty caused by resistor temperature TR(t) is eliminated.


Under the voltage detection mode, the normalization factor in Equation (17) can be expressed as:











Q
i



(

C
,
T

)


=


σ


[

log







V
i



(
t
)



]






i
=
1

N



σ


[

log







V
i



(
t
)



]








(
30
)







The mathematic correlation between optical measurement and blood glucose concentration can then be established according to Equation (18) or Equation (19), under corresponding calibration conditions.


Similarly, the ratio factor defined by Equation (20) can be expressed as:











Y
ij



(

C
,
T

)


=


σ


[

log







V
i



(
t
)



]



σ


[

log







V
j



(
t
)



]







(
31
)







The mathematic correlation between optical measurement and blood glucose concentration can then be established according to Equation (21) or Equation (22), under corresponding calibration conditions. The schematic circuit diagram of a typical preamplifier 400 also includes a feedback capacitor 404, an operational amplifier 406, and a ground connection 410.


Digitization


The voltage output 412 from the preamplifier 400 is usually digitized using an analog-to-digital convertor (ADC). The digitized signal is then sent to a computer for data analysis. The output of ith ADC, in communication with ith preamplifier that is in coupling with ith photodiode 408 collecting light power at wavelength λi, can be expressed by the following Equation:

(ADC)i=(ADC)0i+Gi{└IDi,t)+IDark,i┘Ri+A0i}  (32)


In the above Equation (32), (ADC)0i is the offset of ith ADC, Gi is the nominal ADC Gain used during the detection, IDi,t) is the photocurrent of ith photodiode detector, IDark,i is the dark current of ith photodiode detector, Ri=R0i[1+χi(TRi(t)−25° C.)]] is the resistance of feedback resistor of ith preamplifier, and A0i is the offset of ith preamplifier.


The contribution of the three factors, (ADC)0i, IDark,i, and A0i can be removed by carrying out a dark measurement with the light source turned off right before or after the corresponding finger measurement. When the light source is turned off, the above Equation (32) becomes

(ADC)Dark,i=(ADC)0i+Gi(IDark,iRi+A01)  (33)


The difference between the two above Equations (32) and (33) reflects ADC output corresponding to the photocurrent:

Δ(ADC)i=(ADC)i−(ADC)Dark,i=GiIDi,t)Ri  (34)


Applying common logarithm to both side of the Equation (34), one obtains:

log Δ(ADC)i=log Gi+log IDi,t)+log Ri  (35)


Gi and Ri can be considered as constants as long as the time interval between the finger measurement and the dark measurement is short. As such, one obtains:

σ[log Δ(ADC)i]=σ[log IDi,t)]  (36)

Substituting Equation (25) into Equation (36), one further obtains:

σ[log Δ(ADC)i]=σ[log Pi,t)]  (37)


Based on Equation (36), the normalization factor defined by Equation (17) can be expressed as:











Q
i



(

C
,
T

)


=


σ


[

log





Δ







(
ADC
)

i


]






i
=
1

N



σ


[

log





Δ







(
ADC
)

i


]








(
38
)







The mathematic correlation between optical measurement and blood glucose concentration can then be established according to Equation (18) or (19), under corresponding calibration conditions.


Similar to normalization, the ratio factor defined by Equation (20) can be expressed as:











Y
ij



(

C
,
T

)


=


σ


[

log





Δ







(
ADC
)

i


]



σ


[

log





Δ







(
ADC
)

j


]







(
39
)







The correlation between optical measurement and blood glucose concentration can then be established according to Equations (21) or (22), under corresponding calibration conditions.


Thus, there has been shown and described several embodiments of a novel invention. As is evident from the foregoing description, certain aspects of the present invention are not limited by the particular details of the examples illustrated herein, and it is therefore contemplated that other modifications and applications, or equivalents thereof, will occur to those skilled in the art. The terms “have,” “having,” “includes” and “including” and similar terms as used in the foregoing specification are used in the sense of “optional” or “may include” and not as “required.” Many changes, modifications, variations and other uses and applications of the present construction will, however, become apparent to those skilled in the art after considering the specification and the accompanying drawings. All such changes, modifications, variations and other uses and applications, which do not depart from the spirit and scope of the invention, are deemed to be covered by the invention, which is limited only by the claims that follow. It should be understood that the embodiments disclosed herein include any and all combinations of features described in any of the dependent claims.

Claims
  • 1. A system for detecting glucose in a biological sample, comprising: at least one light source configured to strike a target area of a sample;at least one light filter positioned to receive light transmitted through the target area of the sample from the at least one light source;at least one light detector positioned to receive light from the at least one light source and filtered by the at least one light filter, and to generate an output signal, having a time dependent current, which is indicative of the power of light detected; anda processor configured to receive the output signal from the at least one light detector and based on the received output signal, calculate the attenuance attributable to blood in a sample present in the target area with a normalization factor, and based on the calculated attenuance, determine a blood glucose level associated with a sample present in the target area.
  • 2. The system for detecting glucose in a biological sample according to claim 1, wherein the processor is configured to calculate the normalization factor Qi(C,T) at a plurality of wavelengths, the ith wavelength being represented by λi, based on the time dependent detector output signal
  • 3. The system for detecting glucose in a biological sample according to claim 2, wherein the processor is configured to calculate the blood glucose level Coptical, where Qi(C,T) is the normalization factor, ai(T) is the temperature dependent regression coefficient corresponding to wavelength λi, and N is number of wavelengths employed, according to the equation:
  • 4. The system for detecting glucose in a biological sample according to claim 3, wherein the processor is configured to extract values of the temperature dependent regression coefficient ai(T) using partial least squares regression with the processor.
  • 5. The system for detecting glucose in a biological sample according to claim 2, wherein the processor is configured to calculate the blood glucose level Coptical based on the temperature independent regression coefficient, where Qi(C,T) is the normalization factor, bi is a sample temperature independent regression coefficient, η is a regression coefficient for sample temperature, C is a blood glucose concentration, T is temperature of the biological sample, and N is number of wavelengths employed, according to the equation:
  • 6. The system for detecting glucose in a biological sample according to claim 5, wherein the processor is configured to extract values of the sample temperature independent regression coefficient bi and the regression coefficient for sample temperature η using partial least squares regression with the processor.
  • 7. A method for detecting glucose in a biological sample, comprising: utilizing at least one light source configured to strike a target area of a sample;utilizing at least one light filter positioned to receive light transmitted through the target area of the sample from the at least one light source;utilizing at least one light detector positioned to receive light from the at least one light source and filtered by the at least one light filter, and to generate an output signal, having a time dependent, current, which is indicative of the power of light detected;receiving the output signal from the at least one light detector with a processor and based on the received output signal;calculating the attenuance attributable to blood in a sample present in the target area with a normalization factor with the processor; anddetermining a blood glucose level associated with a sample present in the target area based on the calculated attenuance with the processor.
  • 8. The method for detecting glucose in a biological sample according to claim 7, further comprising calculating the normalization factor Qi(C,T) at a plurality of wavelengths with the processor, the ith wavelength being represented by λi, based on the time dependent detector output signal
  • 9. The method for detecting glucose in a biological sample of claim 8, further comprising calculating the blood glucose level Coptical with the processor, where Qi(C,T) is the normalization factor, ai(T) is the temperature dependent regression coefficient corresponding to wavelength λi, and N is number of wavelengths employed, according to the equation:
  • 10. The method for detecting glucose in a biological sample of claim 9, further comprising extracting values of the temperature dependent regression coefficient ai(T) utilizing partial least squares regression with the processor.
  • 11. The method for detecting glucose in a biological sample of claim 8, further comprising calculating the blood glucose level Coptical with the processor based on the temperature independent regression coefficient, where Qi(C,T) is the normalization factor, bi is a sample temperature independent regression coefficient, η is a regression coefficient for sample temperature, C is a blood glucose concentration, T is temperature of the biological sample, and N is number of wavelengths employed, according to the equation:
  • 12. The method for detecting glucose in a biological sample of claim 11, further comprising extracting values of the sample temperature independent regression coefficient bi and the regression coefficient for sample temperature η utilizing partial least squares regression with the processor.
CROSS-REFERENCE TO RELATED APPLICATION

This patent application claims priority to U.S. Provisional Patent Application Ser. No. 61/055,303 filed on May 22, 2008, the disclosure of which is incorporated herein by reference, and also claims priority to U.S. Provisional Patent Application Ser. No. 61/089,152 filed on Aug. 15, 2008, the disclosure of which is incorporated herein by reference.

US Referenced Citations (116)
Number Name Date Kind
3910701 Henderson et al. Oct 1975 A
3954560 Delafosse et al. May 1976 A
4014321 March Mar 1977 A
4632559 Brunsting Dec 1986 A
4655225 Dahne et al. Apr 1987 A
4781195 Martin Nov 1988 A
4962311 Poisel et al. Oct 1990 A
4997769 Lundsgaard et al. Mar 1991 A
5009230 Hutchinson Apr 1991 A
5028787 Rosenthal et al. Jul 1991 A
5077476 Rosenthal Dec 1991 A
5086229 Rosenthal et al. Feb 1992 A
5112124 Harjunmaa et al. May 1992 A
5137023 Mendelson et al. Aug 1992 A
5183042 Harjunmaa et al. Feb 1993 A
5222496 Clarke et al. Jun 1993 A
5255171 Clark Oct 1993 A
5423983 Chiang et al. Jun 1995 A
5529065 Tsuchiya Jun 1996 A
5535743 Backhaus et al. Jul 1996 A
5553613 Parker Sep 1996 A
5576544 Rosenthal Nov 1996 A
5615672 Braig et al. Apr 1997 A
5615673 Berger et al. Apr 1997 A
5666956 Buchert Sep 1997 A
5671301 Kupershmidt Sep 1997 A
5703364 Rosenthal Dec 1997 A
5743262 Lepper, Jr. et al. Apr 1998 A
5910109 Peters et al. Jun 1999 A
6025597 Sterling et al. Feb 2000 A
6043492 Lee et al. Mar 2000 A
6064898 Aldrich May 2000 A
6067463 Jeng et al. May 2000 A
6097975 Petrovsky et al. Aug 2000 A
6134458 Rosenthal Oct 2000 A
6151517 Honigs et al. Nov 2000 A
6181958 Steuer et al. Jan 2001 B1
6205354 Gellermann et al. Mar 2001 B1
6304767 Soller et al. Oct 2001 B1
6312393 Abreu Nov 2001 B1
6337564 Manzini et al. Jan 2002 B2
6403944 MacKenzie et al. Jun 2002 B1
6421548 Berman et al. Jul 2002 B1
6424848 Berman et al. Jul 2002 B1
6424849 Berman et al. Jul 2002 B1
6424851 Berman et al. Jul 2002 B1
6430424 Berman et al. Aug 2002 B1
6445938 Berman et al. Sep 2002 B1
6522903 Berman et al. Feb 2003 B1
6574490 Abbink et al. Jun 2003 B2
6671528 Steuer et al. Dec 2003 B2
6684099 Ridder et al. Jan 2004 B2
6723048 Fuller Apr 2004 B2
6731963 Finarov et al. May 2004 B2
6775564 Peters et al. Aug 2004 B1
6804002 Fine et al. Oct 2004 B2
6833540 MacKenzie et al. Dec 2004 B2
6865408 Abbink et al. Mar 2005 B1
6873865 Steuer et al. Mar 2005 B2
6958039 Burd et al. Oct 2005 B2
6968222 Burd et al. Nov 2005 B2
6990365 Parker et al. Jan 2006 B1
6993372 Fine et al. Jan 2006 B2
7039447 Berman et al. May 2006 B2
7043289 Fine et al. May 2006 B2
7107087 Hwang et al. Sep 2006 B2
7133711 Chernoguz et al. Nov 2006 B2
7254432 Fine et al. Aug 2007 B2
7266400 Fine et al. Sep 2007 B2
7409239 Chung et al. Aug 2008 B2
7424317 Parker et al. Sep 2008 B2
7809418 Xu Oct 2010 B2
7961305 Xu et al. Jun 2011 B2
20010030742 Kramer et al. Oct 2001 A1
20010039376 Steuer et al. Nov 2001 A1
20020010563 Ratteree et al. Jan 2002 A1
20020016534 Trepagnier et al. Feb 2002 A1
20020019055 Brown et al. Feb 2002 A1
20020161289 Hopkins et al. Oct 2002 A1
20020167704 Kleinhans et al. Nov 2002 A1
20030004423 Lavie et al. Jan 2003 A1
20030023152 Abbink et al. Jan 2003 A1
20030078504 Rowe Apr 2003 A1
20040015734 Rahman Jan 2004 A1
20040087844 Yen May 2004 A1
20040106163 Workman et al. Jun 2004 A1
20040127779 Steuer et al. Jul 2004 A1
20040181132 Rosenthal Sep 2004 A1
20040225205 Fine et al. Nov 2004 A1
20040225206 Kouchnir Nov 2004 A1
20050131286 Parker et al. Jun 2005 A1
20050272987 Kiani-Azarbayjany et al. Dec 2005 A1
20060009685 Finarov et al. Jan 2006 A1
20060058622 Tearney et al. Mar 2006 A1
20060063983 Yamakoshi Mar 2006 A1
20060129040 Fine et al. Jun 2006 A1
20060152726 Larsen et al. Jul 2006 A1
20060200014 Fine et al. Sep 2006 A1
20060224057 Burd et al. Oct 2006 A1
20060226992 Al-Ali et al. Oct 2006 A1
20060250676 Hagood Nov 2006 A1
20060258918 Burd et al. Nov 2006 A1
20060264719 Schurman et al. Nov 2006 A1
20070049811 Kobayashi et al. Mar 2007 A1
20070078312 Fine et al. Apr 2007 A1
20070149869 Yen Jun 2007 A1
20080027297 Yamakoshi Jan 2008 A1
20080144004 Rosenthal Jun 2008 A1
20080194014 Young et al. Aug 2008 A1
20090059586 Livesay et al. Mar 2009 A1
20090079964 Xu Mar 2009 A1
20090105565 Xu Apr 2009 A1
20090116017 Xu et al. May 2009 A1
20090247843 Xu Oct 2009 A1
20090270700 Van Herpen et al. Oct 2009 A1
20100026995 Merritt et al. Feb 2010 A1
Foreign Referenced Citations (32)
Number Date Country
1192665 Sep 1998 CN
01094745 May 2001 EP
9013092 Nov 1990 WO
9115991 Oct 1991 WO
9115992 Oct 1991 WO
9300856 Jan 1993 WO
9306774 Apr 1993 WO
9413199 Jun 1994 WO
9416614 Aug 1994 WO
9531930 Nov 1995 WO
9604840 Feb 1996 WO
9617546 Jun 1996 WO
9639926 Dec 1996 WO
9639927 Dec 1996 WO
9803847 Jan 1998 WO
9836681 Aug 1998 WO
9939631 Aug 1999 WO
0001294 Jan 2000 WO
0016688 Mar 2000 WO
0193755 Dec 2001 WO
0196872 Dec 2001 WO
02082990 Oct 2002 WO
03010510 Feb 2003 WO
2005045377 May 2005 WO
2006086566 Aug 2006 WO
2006094109 Sep 2006 WO
2007122557 Nov 2007 WO
2008039195 Apr 2008 WO
2009035669 Mar 2009 WO
2009045492 Apr 2009 WO
2009120600 Oct 2009 WO
2010017238 Feb 2010 WO
Related Publications (1)
Number Date Country
20090292186 A1 Nov 2009 US
Provisional Applications (2)
Number Date Country
61055303 May 2008 US
61089152 Aug 2008 US