U.S. patent application number 16/274082 was filed with the patent office on 2020-08-13 for miniaturized noninvasive glucose sensor and continuous glucose monitoring system.
The applicant listed for this patent is MEDTRONIC MINIMED, INC.. Invention is credited to Raymond M. Russell, Peter Schultz, Li Zhou.
Application Number | 20200253513 16/274082 |
Document ID | 20200253513 / US20200253513 |
Family ID | 1000003914470 |
Filed Date | 2020-08-13 |
Patent Application | download [pdf] |
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United States Patent
Application |
20200253513 |
Kind Code |
A1 |
Zhou; Li ; et al. |
August 13, 2020 |
MINIATURIZED NONINVASIVE GLUCOSE SENSOR AND CONTINUOUS GLUCOSE
MONITORING SYSTEM
Abstract
Systems and methods are described herein for utilizing a
photoacoustic sensor for estimating analyte concentration levels.
Also described here are training methods for training an analyte
sensor to more accurately estimate an analyte concentration level
on the basis of a received acoustic signal.
Inventors: |
Zhou; Li; (Pasadena, CA)
; Russell; Raymond M.; (Arcadia, CA) ; Schultz;
Peter; (Chatsworth, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
MEDTRONIC MINIMED, INC. |
Northridge |
CA |
US |
|
|
Family ID: |
1000003914470 |
Appl. No.: |
16/274082 |
Filed: |
February 12, 2019 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/746 20130101;
A61B 2562/0204 20130101; A61B 5/7267 20130101; A61B 5/14532
20130101; A61B 5/0095 20130101 |
International
Class: |
A61B 5/145 20060101
A61B005/145; A61B 5/00 20060101 A61B005/00 |
Claims
1. An analyte monitor, the analyte monitor comprising: a light
emitter configured to emit light toward a target, a sensor
configured to sense acoustic waves generated by analyte molecules
in the target in response to the light emitted by the light
emitter, a resonance chamber sized to form a standing wave from
generated acoustic waves, and a signal processing module configured
to estimate an analyte concentration level on the basis of acoustic
waves sensed by the sensor.
2. The analyte monitor of claim 1, wherein the light emitter is
configured to emit light having a wavelength in the mid-infrared
region.
3. The analyte monitor of claim 1, wherein the sensor is positioned
proximate to an anti-node of the standing wave to be formed in the
resonance chamber.
4. The analyte monitor of claim 1, wherein the signal processing
module is configured to determine whether the estimated analyte
concentration level falls within one of two or more pre-determined
ranges.
5. The analyte monitor of claim 4, further comprising a transmitter
configured to transmit a signal when the estimated analyte
concentration level falls within one of the two or more
pre-determined ranges.
6. The analyte monitor of claim 1, wherein the sensor comprises a
microphone.
7. The analyte monitor of claim 1, wherein the sensor comprises a
transducer.
8. The analyte monitor of claim 1, wherein the resonance chamber
comprises a resonance branch for formation of the standing wave and
a measurement branch connecting the resonance branch to the sensor,
the measurement branch being positioned proximate to an anti-node
of the standing wave to be formed in the resonance branch.
9. A method for estimating analyte concentration levels of a
specific target from acoustic signals generated by volumetric
expansion of the target due to thermal vibration of analyte
molecules in the target in response to irradiation of the target
with light, the method comprising: obtaining acoustic signals with
a sensor of a first analyte monitor and simultaneously obtaining
analyte concentration levels using a reference analyte monitor to
form a training set, the reference analyte monitor and the first
analyte monitor being different monitors; training an algorithm of
a signal processing module of the first analyte monitor using
features of the obtained acoustic signals and the obtained analyte
concentration levels of the training set; and after training of the
algorithm, using the first analyte monitor to estimate analyte
concentration levels from obtained acoustic signals.
10. The method of claim 9, wherein the reference analyte monitor is
a continuous glucose monitor having an invasive component and
wherein the first analyte monitor is non-invasive.
11. The method of claim 9, wherein the features of the obtained
acoustic signals are selected from the group comprising: a
timestamp of the recording acoustic signal; an amplitude of the
acoustic signals, an in-phase component of the acoustic signals;
and out-of-phase component of the acoustic signals; and a frequency
of the acoustic signals.
12. The method of claim 9, wherein the step of using the first
analyte monitor to estimate analyte concentration levels from
obtained acoustic signals comprises determining whether an
estimated value of the analyte concentration level falls within two
or more pre-determined ranges.
13. The method of claim 12, further comprising determining a
confidence level that the analyte concentration level falls within
one of the two or more pre-determined ranges.
14. The method of claim 12, wherein the method further comprises
transmitting, using a transmitter, a signal in response to a
determination that the estimated value of the analyte concentration
level falls within one or more of the two or more pre-determined
ranges.
15. The method of claim 14, wherein the signal is a blood glucose
concentration pre-determined range value or an alert signal.
16. The method of claim 9, wherein the analyte molecules are
glucose molecules.
17. The method of claim 9, wherein the step of using the first
analyte monitor to estimate concentration levels from obtained
acoustic signals comprises amplifying and filtering of the obtained
acoustic signal.
18. The method of claim 17, wherein at least part of the amplifying
and filtering of the obtained acoustic signal is performed using a
resonance chamber.
19. The method of claim 9, further comprising the steps of:
converting the obtained acoustic signals into an analog electrical
signal using a sensor; and converting the analog electrical signal
into a digital electrical signal using an analog-to-digital
converter.
20. A computer readable storage medium comprising instructions
which, when executed by a processor, perform a method for
estimating analyte concentration levels of a specific target from
acoustic signals generated by thermal vibration of analyte
molecules in the target in response to irradiation of the target
with light, the method comprising: obtaining acoustic signals with
a sensor of a first analyte monitor and simultaneously obtaining
analyte concentration levels using a reference analyte monitor to
form a training set, the reference analyte monitor and the first
analyte monitor being different; training an algorithm of a signal
processing module of the first analyte monitor using features of
the obtained acoustic signals and the obtained analyte
concentration levels of the training set; and after training of the
algorithm, using the first analyte monitor to estimate analyte
concentration levels from obtained acoustic signals.
Description
TECHNICAL FIELD
[0001] Embodiments of the subject matter described herein relate
generally to photoacoustic techniques for monitoring analyte
concentration levels. More particularly, embodiments of the subject
matter described herein relate to photoacoustic techniques for
monitoring blood glucose concentration levels of a user.
BACKGROUND
[0002] There are approximately 450 million people suffering from
diabetes worldwide. As is known, diabetes is a result of the body's
inefficient production or use of insulin, which leads to medical
complications of hyper- or hypo-glycemia in the short term, and
micro- or macro-vascular problems in the long term if left
untreated. The control of blood glucose concentration levels to
within a desired range, for example through the administration of
insulin, is therefore necessary to prevent the development of such
complications.
[0003] In order to determine when blood glucose concentration
levels need to be controlled, it is necessary to measure the
blood-glucose concentration levels of a diabetic person.
[0004] Photoacoustic techniques for monitoring glucose
concentration levels are desirable for several reasons. In
particular, photoacoustic techniques do not require an invasive
component, such as a transdermal sensor probe or a "finger prick"
puncture, in order to monitor the glucose concentration levels of a
user. Due to the non-invasive nature of photoacoustic techniques,
it is possible to increase user comfort whilst wearing the device
and also to improve the ease and comfort of installation of the
device. Furthermore, it is possible to continuously measure the
blood glucose concentration level with photoacoustic methods, in
contrast to the less-useful intermittent monitoring realized by
"finger-prick" monitoring techniques.
[0005] Photoacoustic techniques rely upon the irradiation of a
target with light, such as light provided by a laser beam. The
light produces thermal effects, such as a volumetric expansion, in
the target and the thin layer of air contacting the target due to
thermal diffusion, which causes a pressure oscillation that
generates an acoustic wave. The characteristics of this acoustic
wave depend upon several factors, such as the target's absorption
co-efficient to the wavelength of light used, the density of the
medium through which the acoustic wave propagates, the thermal
expansion co-efficient of the target, the velocity of the acoustic
wave, and so on.
[0006] If skin is used as a target, the light may penetrate a
distance into the skin and excite molecules, such as glucose
molecules, beneath the skin. The acoustic wave generated by the
thermal excitation (and subsequent volumetric expansion) of these
glucose molecules can be used to estimate the concentration of the
glucose molecules.
[0007] However, there are disadvantages associated with the use of
photoacoustic techniques for monitoring blood glucose concentration
levels. In particular, the acoustic wave generated through the
excitation of glucose molecules may not be of sufficient magnitude
to obtain a strong enough signal for the accurate measurement of
the blood glucose concentration. Furthermore, the characteristics
of the acoustic wave may vary from person to person, dependent upon
(for example) the user's skin light transmittivity characteristics,
skin sweat gland activity, skin composition and structure, and so
on.
[0008] Accordingly, it is desirable to overcome the disadvantages
associated with photoacoustic techniques for measuring analyte
concentrations, such as blood glucose concentration levels.
Furthermore, other desirable features and characteristics will
become apparent from the subsequent detailed description and the
appended claims, taken in conjunction with the accompanying
drawings and the foregoing technical field and background.
BRIEF SUMMARY
[0009] According to a first aspect of the invention, there is
provided an analyte monitor. The analyte monitor includes a light
emitter configured to emit light at a target. The analyte monitor
includes a sensor configured to sense acoustic waves generated by
analyte molecules in the target in response to the emitted light.
The analyte monitor also includes a resonance chamber sized to form
a standing wave with the generated acoustic waves. The analyte
monitor includes a signal processing module configured to estimate
an analyte concentration level on the basis of the sensed acoustic
waves.
[0010] According to a second aspect of the invention, there is
provided a method for training an algorithm for estimating analyte
concentration levels of a specific target from acoustic signals
generated via thermal vibration of analyte molecules in the target
in response to irradiation of the target with light. The method
includes the step of obtaining acoustic signals with a sensor of a
first analyte monitor and simultaneously obtaining analyte
concentration levels using a reference analyte monitor to form a
training set, the reference analyte monitor and the first analyte
monitor being different. The method includes the step of training
an algorithm of a signal processing module of the first analyte
monitor using features of the obtained acoustic signals and the
obtained analyte concentration levels of the training setThe method
also includes the step of, after training of the algorithm, using
the first analyte monitor to estimate analyte concentration levels
from obtained acoustic signals.
[0011] According to a third aspect of the invention, there is
provided a computer-readable medium containing instructions which,
when executed by a processor, performs a method for training an
algorithm for estimating analyte concentration levels of a specific
target from acoustic signals generated via thermal vibration of
analyte molecules in the target in response to irradiation of the
target with light. The method includes the step of obtaining
acoustic signals with a sensor of a first analyte monitor and
simultaneously obtaining analyte concentration levels using a
reference analyte monitor to form a training set, the reference
analyte monitor and the first analyte monitor being different. The
method includes the step of training an algorithm of a signal
processing module of the first analyte monitor using features of
the obtained acoustic signals and the obtained analyte
concentration levels of the training set; and after training of the
algorithm, using the first analyte monitor to estimate analyte
concentration levels from obtained acoustic signals.
[0012] This summary is provided to introduce a selection of
concepts in a simplified form that are further described below in
the detailed description. This summary is not intended to identify
key features or essential features of the claimed subject matter,
nor is it intended to be used as an aid in determining the scope of
the claimed subject matter.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] A more complete understanding of the subject matter may be
derived by referring to the detailed description and claims when
considered in conjunction with the following figures, wherein like
reference numbers refer to similar elements throughout the
figures.
[0014] FIG. 1 shows a schematic of a photoacoustic technique for
measuring an analyte concentration of a target;
[0015] FIG. 2 shows a graph of wavelengths of light that strongly
interact with glucose molecules;
[0016] FIG. 3 shows a schematic of an analyte monitor in accordance
with exemplary embodiments;
[0017] FIG. 4 shows an exploded view of an analyte monitor in
accordance with exemplary embodiments;
[0018] FIG. 5 shows an angled partially transparent perspective
view of an analyte monitor in accordance with exemplary
embodiments;
[0019] FIG. 6 shows computer simulations of a resonance chamber in
accordance with exemplary embodiments;
[0020] FIG. 7 shows a flowchart of a method in accordance with
exemplary embodiments;
[0021] FIG. 8 shows a flowchart of another method in accordance
with exemplary embodiments;
[0022] FIG. 9 shows a flowchart of another method in accordance
with exemplary embodiments;
[0023] FIG. 10 shows a flowchart of another method in accordance
with exemplary embodiments;
[0024] FIG. 11 shows a flowchart of another method in accordance
with exemplary embodiments;
[0025] FIG. 12 shows a graph comparing results obtained from an
analyte monitor in accordance with exemplary embodiments to a
conventional analyte monitor; and
[0026] FIG. 13 shows another graph comparing results obtained from
an analyte monitor in accordance with exemplary embodiments to a
conventional analyte monitor.
DETAILED DESCRIPTION
[0027] The following detailed description is merely illustrative in
nature and is not intended to limit the embodiments of the subject
matter or the application and uses of such embodiments. As used
herein, the word "exemplary" means "serving as an example,
instance, or illustration." Any implementation described herein as
exemplary is not necessarily to be construed as preferred or
advantageous over other implementations. Furthermore, there is no
intention to be bound by any expressed or implied theory presented
in the preceding technical field, background, brief summary or the
following detailed description.
[0028] Techniques and technologies may be described herein in terms
of functional and/or logical block components, and with reference
to symbolic representations of operations, processing tasks, and
functions that may be performed by various computing components or
devices. Such operations, tasks, and functions are sometimes
referred to as being computer-executed, computerized,
software-implemented, or computer-implemented. It should be
appreciated that the various block components shown in the figures
may be realized by any number of hardware, software, and/or
firmware components configured to perform the specified functions.
For example, an embodiment of a system or a component may employ
various integrated circuit components, e.g., memory elements,
digital signal processing elements, logic elements, look-up tables,
or the like, which may carry out a variety of functions under the
control of one or more microprocessors or other control
devices.
[0029] When implemented in software or firmware, various elements
of the systems described herein are essentially the code segments
or instructions that perform the various tasks. In certain
embodiments, the program or code segments are stored in a tangible
processor-readable medium, which may include any medium that can
store or transfer information. Examples of a non-transitory and
processor-readable medium include an electronic circuit, a
semiconductor memory device, a ROM, a flash memory, an erasable ROM
(EROM), a floppy diskette, a CD-ROM, an optical disk, a hard disk,
or the like.
[0030] "Connected/Coupled"--The following description refers to
elements or features being "connected" or "coupled" together. As
used herein, unless expressly stated otherwise, "coupled" or
"connected" means that one element/feature is directly or
indirectly joined to (or directly or indirectly communicates with)
another element/feature, and not necessarily mechanically.
[0031] In addition, certain terminology may also be used in the
following description for the purpose of reference only, and thus
are not intended to be limiting. For example, terms such as
"upper", "lower", "above", and "below" refer to directions in the
drawings to which reference is made. Terms such as "front", "back",
"rear", "side", "outboard", and "inboard" describe the orientation
and/or location of portions of the component within a consistent
but arbitrary frame of reference which is made clear by reference
to the text and the associated drawings describing the component
under discussion. Such terminology may include the words
specifically mentioned above, derivatives thereof, and words of
similar import. Similarly, the terms "first", "second", and other
such numerical terms referring to structures do not imply a
sequence or order unless clearly indicated by the context.
[0032] For the sake of brevity, conventional techniques related to
signal processing, data transmission, signaling, network control,
and other functional aspects of the systems (and the individual
operating components of the systems) may not be described in detail
herein. Furthermore, the connecting lines shown in the various
figures contained herein are intended to represent exemplary
functional relationships and/or physical couplings between the
various elements. It should be noted that many alternative or
additional functional relationships or physical connections may be
present in an embodiment of the subject matter.
[0033] It should be appreciated that the later-described digital
signal processor, and any corresponding logical elements,
individually or in combination, are exemplary means for performing
a claimed function.
[0034] FIG. 1 shows a schematic illustrating how photoacoustic
measurement techniques may be used to obtain a sensor signal
representative of an analyte concentration, for example a blood
glucose concentration level of a user. As can be seen in FIG. 1, a
light emitter 10 is configured to emit light towards a target 12,
which may be tissue of a user, for example skin. The light incident
on the target 12 penetrates a distance into the target 12 and
interacts with analyte molecules, for example glucose molecules,
present in the target 12. The analyte molecules are thermally
excited and vibrate, and the medium surrounding these molecules
undergoes a volumetric expansion, thereby generating an acoustic
wave. This acoustic wave propagates out of the target 12 and into
the medium surrounding the target, for example air. The propagation
of the acoustic wave is illustrated in FIG. 1 through the use of
bold arrows. The acoustic wave is then detected by a sensor 13
configured to convert the pressure of the acoustic wave into an
electrical signal. In an exemplary embodiment, the sensor 13
comprises a microphone. In an alternative exemplary embodiment, the
sensor 13 comprises a transducer, for example a piezoelectric
transducer.
[0035] In an exemplary embodiment, the wavelength of the light
emitted by the light emitter 10 is selected so as to strongly
interact with the analyte of interest. As can be seen in the graph
20 shown in FIG. 2, if the analyte of interest is glucose, the
wavelength of the light emitted by the light emitter can be
selected to correspond to wavelengths which interact strongly with
glucose molecules for thermal excitation of the glucose molecules.
The graph 20 of FIG. 2 shows that wavenumbers (the reciprocal of
wavelength) of between about 1150 and 1000 interact strongly with
glucose molecules. This range of wavenumbers corresponds to
wavelengths of light in the mid-infrared region.
[0036] As can also be seen in FIG. 2, the absorbance of light by
glucose molecules in this wavenumber region generally increases as
the glucose concentration level increases. The three spectra 21, 22
and 23 shown in the graph 20 relate to the absorbance of light at
blood glucose concentration levels of 300 mg/dl, 200 mg/dl and 100
mg/dl, respectively.
[0037] In general, the higher the level of absorbance of light by
the analyte, the greater the acoustic response signal that will be
generated by that analyte. As such, the magnitude of the acoustic
response signal after irradiation of the target with light can be
correlated to the analyte concentration level.
[0038] The present inventors recognized that at certain analyte
concentration levels, the magnitude of the acoustic response is not
large enough to accurately distinguish between differences in
analyte concentrations without the use of highly-sensitive,
expensive sensors. These types of highly-sensitive sensor may be
prohibitively expensive when attempting to commercialize an analyte
monitor based on photoacoustic techniques.
[0039] In order to circumvent the need for these kinds of expensive
sensors to accurately detect the magnitude of the acoustic response
from the target, the inventors recognized that the acoustic signal
response may be improved through the use of a resonance chamber. A
resonance chamber uses the physical principle of resonance to
enhance the acoustic response. More specifically, when an acoustic
wave enters the resonance chamber, the acoustic wave reflects back
and forth within the chamber with minimal energy loss so as to form
a standing wave. As additional acoustic waves enter the resonance
chamber, the intensity of the standing wave increases.
[0040] As such, by pulsing the light emitted from the light emitter
10 and then measuring the intensity of the acoustic standing wave
formed in reaction to these light pulses, it is possible to more
accurately correlate the acoustic response to an analyte
concentration value for a given quality of sensor.
[0041] The inventors additionally found that, through the use of a
resonance chamber, a certain amount of noise-filtering of the
acoustic response was achievable. More specifically, since the
resonance chamber is sized so as to form a standing wave with
acoustic waves having a specific wavelength of interest, "noise"
(acoustic waves of different wavelengths/frequencies) are not
amplified by the standing wave in the same manner as the acoustic
waves having the wavelength of interest. As such, the resonance
chamber not only amplifies the wavelengths of the acoustic waves of
interest, but also advantageously acts as a mechanical
bandwidth-filter for the acoustic waves of the acoustic
response.
[0042] A schematic analyte monitor 300 in accordance with an
exemplary embodiment is shown in FIG. 3. As can be seen in FIG. 3,
the analyte monitor 300 includes a light emitter 310 for emitting
light shown with thin dashed lines) towards a target 312. In an
exemplary embodiment, the light emitter 310 comprises a
light-emitting diode (LED). In an alternative embodiment, the light
emitter 310 comprises a laser chip. A light emitter controller
module 311 includes circuitry associated with the lighter emitter
310. In exemplary embodiments, the light emitter controller module
311 is configured to control the light emitter 10 such that the
pulses of light emitted by the light emitter 10 have a
pre-determined or a variable pulse repetition frequency (PRF). In
other words, the light emitter controller module 311 is suitable
for modulating the frequency of the light pulses emitted by the
light emitter 310. Preferably, the light emitter controller module
311 is configured to control the light emitter 310 so as to emit
light pulses having a duration of about 500 ns per pulse at a
frequency of about 50 kHz or more. This pulse duration and
frequency has been found to achieve a good acoustic response for
certain analytes of interest, such as glucose.
[0043] In an embodiment, the light emitter 310 may include a heat
conduction element (not shown) configured to conduct heat away from
the light emitter 310 so as to reduce the likelihood that the light
emitter 310 is heated to an undesirable temperature. In an
additional or alternative embodiment, the light emitter 310 may
include an anti-reflection surface (not shown) to reduce acoustic
signal response noise caused by reflections of the emitted
light.
[0044] The analyte monitor 300 further includes a sensor 313, for
example a microphone or a transducer, such as a piezoelectric
transducer. The sensor 313 is configured to detect acoustic waves
(shown with a bold dashed line) emitted from the thermal excitation
of analyte molecules and volumetric expansion in the target 312 by
the light emitted from the light emitter 310 and generate an
electrical signal based on these acoustic waves.
[0045] The analyte monitor 300 further includes a resonance chamber
314. The resonance chamber 314 is sized and dimensioned so as to
form a standing wave for acoustic waves having a wavelength
corresponding to the wavelength of acoustic waves generated through
the volumetric expansion of the analyte-containing medium of
interest after irradiation with light, which is controlled by pulse
repetition frequency (PRF). For example, if the analyte of interest
was glucose, and the wavelength of the acoustic wave generated
through the irradiation of glucose with light at the pulse
repetition frequency around 55 KHz was on the order of about 6 mm,
then the resonance chamber would be sized and dimensioned to as to
support a standing wave having a length on the order of 6 mm. In an
embodiment, the resonance chamber has a length that is a scalar
multiple of the length of the acoustic wave generated through the
thermal excitation and subsequent volumetric expansion of a glucose
molecule, for example 6 mm, 12 mm, 18 mm, and so on. It will be
appreciated that if the standing wave were to have a different
wavelength, the dimensions of the resonance chamber may be changed
accordingly. Although the embodiment in FIG. 3 shows the light
emitter being inside the resonance chamber, in various embodiments
the light emitter is located outside of the resonance chamber.
[0046] The present inventors found that, through the use of such a
resonance chamber, acoustic wave amplification of up to about three
times can be achieved, in comparison to the base acoustic signal.
Furthermore, as explained above, the resonance chamber may perform
a noise-filtering function.
[0047] In exemplary embodiments, the sensor 313 is operably
connected to a signal processing module 320. In the embodiment
shown in FIG. 3, the signal processing module 320 includes an
operational amplifier ("op-amp") 315 configured to further amplify
the electronic signal derived from the acoustic response of the
target 312. In exemplary embodiments, the op-amp 315 is operably
connected to an analog-to-digital converter 316 configured to
convert the analog electrical signal from the sensor 313 to a
digital signal. In exemplary embodiments, the analog-to-digital
converter 316 is operably connected to a digital signal processor
("DSP") 317 for processing of the digital signal.
[0048] Converting the analog signal from the sensor 313 into a
digital signal allows for the accurate separation of different
magnitudes of acoustic response into "bins" corresponding to
different glucose concentration levels, these "bins" being
separated by pre-determined threshold values. For example, an
acoustic response of a first magnitude or less could be determined
to correlate to a glucose concentration level of about 100 mg/dl or
below, which could be determined by the signal processing module
320 as corresponding to a "hypoglycemic" glucose level. An acoustic
response having a magnitude between the first magnitude and a
second magnitude could be determined to correlate to a glucose
concentration of between about 100 mg/dl and about 200 mg/dl, which
could be determined by the signal processing module 320 as
corresponding to a "normal" glucose level. Still further, an
acoustic response having a magnitude greater than the second
magnitude could be determined to correlate to a glucose
concentration of over about 200 mg/dl, which could be determined by
the signal processing module 320 as corresponding to a
"hyperglycemic" glucose level.
[0049] As will later be explained in more detail with respect to
FIG. 11 in an embodiment, upon determination of a "hypoglycemic" or
"hyperglycemic" glucose level, the signal processing module 320 is
configured to transmit a warning signal to the user of the analyte
monitor, or to take some other form of action (such as
automatically activating an insulin pump).
[0050] Furthermore, by "binning" the acoustic responses using
pre-determined thresholds, a more computationally efficient and
accurate monitoring of glucose concentration level results. In
particular, by comparing the magnitude of the acoustic response to
pre-determined thresholds, hypoglycemic and hyperglycemic events
may be detected more quickly and more accurately by the signal
processing module 320.
[0051] Turning to FIG. 4, an exploded-view diagram of an analyte
monitor 400 in accordance with an embodiment is shown. The analyte
monitor 400 includes a light emitter 410 configured to emit light
of a pre-determined wavelength. The analyte monitor 400 further
includes a sensor 413 configured to detect acoustic waves generated
from the volumetric expansion of an analyte of interest after
irradiation of that analyte of interest with light generated by the
light emitter 410. The analyte monitor 400 further includes a
resonance chamber 414 sized and configured to form a standing wave
from the acoustic waves so as to amplify the magnitude of the
acoustic waves. The analyte monitor 400 further includes a signal
processing module 420 configured to estimate an analyte
concentration level based on the magnitude of the sensed acoustic
response.
[0052] In exemplary embodiments, the analyte monitor 400 further
includes a power source 422 configured to supply power to one or
more of the light emitter 410, the sensor 413 and the signal
processing module 420, or other components of the analyte monitor
400.
[0053] In exemplary embodiments, the analyte monitor 400 further
includes a case 421 configured to surround one or more of the
above-described components. In exemplary embodiments, the case 421
may comprise a case cover having a transmitter 423 incorporated
into the case cover. In other exemplary embodiments, the
transmitter 423 is located elsewhere in the analyte monitor, for
example proximate to the signal processing module 420.
Incorporation of the transmitter 423 into the case cover allows for
the transmitter 423 to have a larger size than if the transmitter
423 were to be located inside the case 421 of the analyte monitor
400 and also to reduce the overall size of the analyte monitor. For
example, in instances where the transmitter 423 comprises an
antenna (for example a RF antenna), the increased size of the
transmitter 423 allows for a more powerful signal to be generated
by the transmitter 423.
[0054] The transmitter 423 is operably connected to the signal
processing module 420. When a measured analyte concentration is
determined to be outside of a "normal" range, the transmitter 423
is configured to transmit a signal responsive to this
determination. For example, in an embodiment, the transmitter 423
is configured to transmit a signal to a remote device (not shown),
such as a smartphone or smartwatch, in order to warn the user of
the analyte device of the high or low analyte concentration level.
Additionally or alternatively, the transmitter 423 is configured to
transmit a signal to a remote device (not shown) for application of
an additional substance to the user of the analyte monitoring
device, such as an insulin pump for delivering insulin to the
patient.
[0055] FIG. 5 shows an angled phantom view of a partially-assembled
analyte monitor 400. As can be seen in FIG. 5, the assembled
analyte monitor 400 may have a length of around below about 50 mm,
for example below about 40 mm, such as below about 30 mm,
preferably below about 20 mm, most preferably about 15 mm. The
assembled analyte monitor 400 may have a width of below about 50
mm, for example below about 40 mm, such as below about 30 mm,
preferably below about 20 mm, most preferably about 18 mm. As such,
the assembled analyte monitor 400 may be worn discretely by a user,
or even incorporated into jewelry or clothing of the user, for
example incorporated into an earring, a watch or a bracelet.
[0056] Another view of a resonance chamber in accordance with
exemplary embodiments is shown in FIG. 6. In FIG. 6, two computer
models of a resonance chamber 600 are shown. The resonance chamber
600 includes a resonance branch 601 and a measurement branch 602.
The resonance branch 601 is sized and dimensioned so as to form a
standing wave, in the manner as described above. The measurement
branch 602 connects the resonance branch 601 to the sensor.
[0057] As can be seen in the two figures of FIG. 6, the length of
the resonance branch 601 and the placement of the measurement
branch 602 along the resonance branch are important for obtaining a
clear signal of the acoustic response. In particular, as can be
seen in the bottom figure of FIG. 6, the measurement branch 602 is
positioned adjacent to a node of the standing wave formed in the
resonance branch 601. As such, minimal propagation of the acoustic
wave down the measurement branch 602 occurs, and a lower magnitude
acoustic response is measured by the sensor. Further, as can be
seen in the top figure of FIG. 6, the measurement branch 602 is
positioned close to an antinode of the standing wave formed in the
resonance branch 601. As such, a good propagation of the acoustic
wave down the measurement branch 602 occurs, and an acceptable
magnitude acoustic response signal is measured by the sensor. As
such, in an exemplary embodiment, the measurement branch 602 is
positioned proximate to an anti-node of the standing wave to be
formed in the resonance chamber 601.
[0058] When developing the analyte monitor described above, the
inventors recognized that the acoustic responses of each user to
the light emitted from the light emitter are individual. In other
words, a variety of variables may affect the acoustic response of a
user, such as skin transmittivity, skin composition, sweat gland
activity and so on. As such, the inventors found that a training
procedure to personalize the analyte monitor to an individual user
increased the accuracy of the analyte monitor in detecting raised
or lowered analyte concentrations.
[0059] FIG. 7 shows an overview of a general method S700 for
training an analyte monitor for a specific user. The individual
steps of this general method will be explained in more detail with
reference to FIGS. 8, 9, 10 and 11. At step S701, training data for
training a signal processing module of the analyte monitor is
collected from a specific user. In an embodiment, the training data
is collected by obtaining analyte concentration levels of the user
from a conventional analyte monitor, such as a
commercially-available continuous glucose monitor having an
invasive component (such as a transdermal probe), in combination
with measuring acoustic responses from the analyte monitor in
accordance with various embodiments as described herein. After
sufficient training data has been collected from the user, the
method progresses to step S702.
[0060] At step S702, the algorithm used by the signal processing
module to correlate certain acoustic signal responses to the
obtained analyte concentration levels is trained using the training
data collected from the user. In an embodiment, the training of the
signal processing module is a supervised machine learning process,
where training data is first labelled and then used in training. In
an embodiment, after the signal processing module is trained using
the training data, the signal processing module may be tested
against validation data to determine the accuracy of the signal
processing module in determining analyte concentration levels.
After training of the signal processing module to an acceptable
accuracy, the method progresses to step S703.
[0061] At step S703, the analyte monitor has been adapted to the
individual characteristics of the user and is used to monitor an
analyte concentration level of the use without the concurrent use
of another analyte monitor.
[0062] Each of the steps S701, S702 and S703 of the method S700
will now be described in more detail.
[0063] FIG. 8 shows a more detailed flowchart S800 illustrating
exemplary steps involved in the collection of a training data set
of step S701. At Step S801, data is collected from a specific user
via two or more analyte monitors. One of these analyte monitors is
an analyte monitor in accordance with exemplary embodiments as
described herein, and at least one other of these analyte monitors
comprises a conventional analyte monitor, such as a continuous
glucose monitor comprising a transdermal probe. The conventional
analyte monitor collects data related to the user's blood analyte
concentration levels to be used as reference data. The analyte
monitor in accordance with various embodiments obtains acoustic
responses in parallel with obtaining the reference data.
[0064] As the reference data is obtained in parallel with the
acoustic responses, it is possible to correlate features of the
acoustic responses to the reference data. In Step S802, features
(x1, x2, x3, x4, x5 and x6) of the acoustic response signals
detected by the sensor of the analyte monitor in accordance with
exemplary embodiments are associated with the
simultaneously-obtained analyte reference values (y) obtained by
the conventional analyte monitor.
[0065] For example, if the analyte of interest is glucose,
reference glucose concentration values (y) obtained from the
conventional analyte sensor can be associated with the time stamp
(x1), amplitude (x2), phase difference (x3), in-phase component
(x4), out-of-phase component (x5) and frequency (x6) of the
acoustic signal response obtained via the analyte monitor in
accordance with exemplary embodiments. It is to be noted that the
above list of features is not an exhaustive list, and other
features of the acoustic signal response may additionally or
alternatively be associated with the reference glucose
concentration values (y) obtained from the conventional analyte
monitor.
[0066] After association of the features (x1, x2, x3, x4, x5 and
x6) of the acoustic response to the reference data (y), the method
progresses to step S803, where a labelled training data set is
constructed, using conventional techniques, with this data.
[0067] In FIG. 9, a further flowchart 5900 shows how training data
is collected by the analyte monitor in accordance with exemplary
embodiments as described herein. At Step S901, tissue of a user is
excited via an optical pulse from a light emitter. The method then
progresses to step S902.
[0068] At Step S902, an acoustic signal is generated by analytes in
the tissue in response to the optical pulse. At Step S903, this
acoustic signal is detected by a sensor and converted into an
electrical signal, in the manner described previously.
[0069] At Step S904, the electrical signal is filtered to reduce
the amount of noise in the signal. In an embodiment, this filtering
is achieved through the use of a bandwidth filter. In an
embodiment, this filtering is achieved via a time-based filtering
technique, where a pulse reference signal generated at the same
time as the emission of the optical pulse in Step S901, and wherein
only acoustic signals received a pre-determined time after the
pulse reference signal has been generated are allowed through the
signal filter. Additionally or alternatively, this signal filtering
may utilize timestamps associated with the pulse frequency of light
to filter out noise through a comparison of the timestamps of the
emitted light and the timestamps of acoustic signal responses to
the light. Also at Step S904, the filtered electrical signal is
amplified, for example through the use of an operational amplifier.
The method then progresses to Step S905.
[0070] At Step S905, a time series stationarity test is applied to
the filtered, amplified electrical signal in order to test for
noise in the signal caused by, for example, contact pressure and/or
motion of the user. If the signal fails this time series
stationarity test, the method progresses to Step S906, where the
collected data is excluded from the training data set. If the
signal passes this time series stationarity test, the method
progresses to Step 5907, where the collected data is included in
the training data set for training the analyte monitor according to
various embodiments as described herein.
[0071] The method S900 is repeated until sufficient data has been
collected to form a training data set large enough for training the
algorithm used by the signal processing module.
[0072] FIG. 10 shows a detailed flowchart S1000 illustrating
exemplary steps involved in the training of the algorithm used in
the digital signal processor as per step S702 in method S700. At
Step S1001, the labelled training data set is completed. The method
then progresses to Step S1002.
[0073] At Step S1002, a supervised learning method is used to train
the algorithm used by the digital signal processor on the labelled
training data set. More specifically, the algorithm used by the
digital signal processor uses the features (x1), (x2), (x3), (x4),
(x5) and (x6) of the electric signal derived from received acoustic
responses and estimates a glucose concentration level based on
these features. The estimated glucose concentration level is then
compared to the obtained glucose concentration level (y)
corresponding to those features as obtained from the conventional
analyte monitor. Based on the accuracy of the comparison,
parameters of the algorithm are adjusted. Statistical normalization
and transformation techniques, such as linear regression
techniques, may be used in this adjusting of parameters.
[0074] After the parameters have been adjusted, the glucose
concentration level is again estimated and compared to the obtained
glucose concentration level (y) corresponding to those features as
obtained from the conventional analyte monitor. In this manner, the
algorithm is iteratively trained to more accurately estimate the
glucose concentration level of the user based on the features of
the acoustic response signal obtained from the analyte monitor in
accordance with various embodiments. When the algorithm is fully
trained, a more accurate personalized calibration for a specific
user is achieved. In particular, the analyte monitor according to
embodiments as described herein is better able to account for
variations in the skin conditions and characteristics of a specific
user.
[0075] FIG. 11 shows a more detailed flowchart S1100 of the use of
a trained analyte monitor in monitoring analyte concentration
levels of a user, as per step S703 in FIG. 7.
[0076] At step S1101, the trained analyte monitor obtains acoustic
responses that are converted into electrical signals having
features (x1, x2, x3, etc.) that can be used to estimate an analyte
concentration of a user. The method then progresses to step
S1102.
[0077] At Step S1102, the features of the electrical signal are
used, using the algorithms of the trained digital signal processor
module, to estimate an analyte concentration level. After
estimation of the analyte concentration level, the method
progresses to step S1103.
[0078] At Step S1103, the estimated analyte concentration level is
sorted into a "bin", each bin corresponding to analyte
concentration levels of a particular user state. For example, the
estimated analyte concentration level is a blood glucose
concentration level, and the estimated glucose concentration level
is sorted into one of three bins, the first bin having a range of
below about 100 mg/dl and corresponding to a hypoglycemic state,
the second bin having a range of between about 100 mg/dl and 200
mg/dl and corresponding to a "normal" state, and the third bin
having a range of above about 200 mg/dl and corresponding to a
hyperglycemic state. In an exemplary embodiment, the end-points of
the ranges of the bins are variable and can be varied on the basis
of a particular user's blood glucose characteristics.
[0079] In an exemplary embodiment, a probability of the estimated
blood glucose level falling within a certain bin is estimated by
the trained algorithm of the digital signal processor, based on the
features of the electrical signal derived from the acoustic
response. In this manner, a probabilistic confidence level of the
"binning" step can be determined. In exemplary embodiments,
multiple estimations of the blood glucose level can be performed,
and the confidence level can be iteratively re-evaluated on the
basis of each subsequent estimation. In this manner, a high
confidence level of the binning of the blood glucose concentration
estimation can be achieved.
[0080] After the analyte concentration estimation is binned, the
method progresses to Step S1104. At step S1104, the algorithm of
the digital signal processor outputs the "state" corresponding to
the estimated analyte concentration level. For example, if the
estimated blood glucose concentration level was above 200 mg/d1,
the algorithm would output a "hyperglycemic" state. After
outputting of this state, the method progresses to Step S1105.
[0081] At Step S1105, action is performed in response to the output
of the state, if required. For example, if a "hyperglycemic" state
is output by the analyte monitor, an alert may be transmitted to a
remote device so as to warn the user of this state. Additionally or
alternatively, a signal may be transmitted to an insulin pump so as
to administer an amount of insulin in response to the output of the
state. Additionally or alternatively, a signal is transmitted to a
display device to display the blood glucose concentration
state.
[0082] In order to test the accuracy of glucose concentrations
estimated with the analyte monitor of embodiments as described
herein and a conventional continuous glucose monitor having a
transdermal probe, glucose concentration measurements were tested
simultaneously in-silico using a trained analyte monitor according
to an embodiment and a transdermal continuous glucose monitor and
then compared to each other. The results of these comparisons are
shown in the graph 1200 of FIG. 12. As shown in FIG. 12, the
glucose concentrations estimated by the trained analyte monitor in
accordance with embodiments as described herein closely matched the
glucose concentrations measured using a conventional,
commercially-available continuous glucose monitor having a
transdermal probe.
[0083] Glucose concentration estimations were also obtained with an
in-silico simulation of untrained analyte monitor in accordance
with embodiments as described herein and compared to the glucose
concentrations estimations obtained with a trained algorithm
prototype analyte monitor in accordance with embodiments as
described herein. It was determined that the prototype untrained
analyte monitor had a success rate of about 60% in determining
hypoglycemic events, whereas the prototype trained analyte monitor
had a success rate in excess of about 85% in determining
hypoglycemic events, thereby demonstrating the effect of the
machine learning training algorithm in improving the accuracy of
glucose concentration estimations.
[0084] Further, in-vivo, experiments were also performed, as shown
in the graph 1300 of FIG. 13. As can be seen in FIG. 13, thee is a
strong correlation between the sensor signal obtained from the
analyte monitor in accordance with embodiments having a trained
algorithm and a known blood glucose concentration level obtained
from a reference sensor, thereby indicating the accuracy of this
method in detecting blood glucose concentration levels.
[0085] The various tasks performed in connection with the processes
described herein may be performed by software, hardware, firmware,
or any combination thereof. For illustrative purposes, the
following description of process S700 may refer to elements
mentioned in connection with FIGS. 1 to 6. In practice, portions of
process S700 may be performed by different elements of the
described system, e.g., the digital signal processor module or a
different controller module. It should be appreciated that process
S700 may include any number of additional or alternative tasks, the
tasks shown in FIG. 7 need not be performed in the illustrated
order, and process S700 may be incorporated into a more
comprehensive procedure or process having additional functionality
not described in detail herein. Moreover, one or more of the tasks
shown in FIG. 7 could be omitted from an embodiment of the process
S700 as long as the intended overall functionality remains
intact.
[0086] While at least one exemplary embodiment has been presented
in the foregoing detailed description, it should be appreciated
that a vast number of variations exist. It should also be
appreciated that the exemplary embodiment or embodiments described
herein are not intended to limit the scope, applicability, or
configuration of the claimed subject matter in any way. Rather, the
foregoing detailed description will provide those skilled in the
art with a convenient road map for implementing the described
embodiment or embodiments. It should be understood that various
changes can be made in the function and arrangement of elements
without departing from the scope defined by the claims, which
includes known equivalents and foreseeable equivalents at the time
of filing this patent application.
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