U.S. patent application number 16/505195 was filed with the patent office on 2019-10-31 for methods and devices for displaying trend and variability in a physiological dataset.
This patent application is currently assigned to Respiratory Motion, Inc.. The applicant listed for this patent is Respiratory Motion, Inc.. Invention is credited to Malcolm Bock, Jordan Brayanov, Jenny Freeman, Michael Lalli, Colin M. MacNabb.
Application Number | 20190333257 16/505195 |
Document ID | / |
Family ID | 54017875 |
Filed Date | 2019-10-31 |
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United States Patent
Application |
20190333257 |
Kind Code |
A1 |
Brayanov; Jordan ; et
al. |
October 31, 2019 |
METHODS AND DEVICES FOR DISPLAYING TREND AND VARIABILITY IN A
PHYSIOLOGICAL DATASET
Abstract
Embodiments of the invention are directed to methods and devices
for displaying trends and variability in a physiological dataset.
The method comprises obtaining the physiological dataset, applying
a smoothing algorithm to the physiological dataset to obtain a
trend of the physiological dataset, applying a variability
algorithm to the physiological dataset to obtain the variability of
the physiological dataset, outputting a graph of the trend of the
physiological dataset, and outputting a graph of the variability of
the physiological dataset.
Inventors: |
Brayanov; Jordan; (Medford,
MA) ; Bock; Malcolm; (Medfield, MA) ; Lalli;
Michael; (Somerville, MA) ; Freeman; Jenny;
(Weston, MA) ; MacNabb; Colin M.; (Allston,
MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Respiratory Motion, Inc. |
Watertown |
MA |
US |
|
|
Assignee: |
Respiratory Motion, Inc.
Watertown
MA
|
Family ID: |
54017875 |
Appl. No.: |
16/505195 |
Filed: |
July 8, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15454500 |
Mar 9, 2017 |
10347020 |
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16505195 |
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14640648 |
Mar 6, 2015 |
9595123 |
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15454500 |
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61948964 |
Mar 6, 2014 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16H 10/60 20180101;
G06T 11/60 20130101; G16H 40/63 20180101; G06T 11/206 20130101;
G16H 50/20 20180101 |
International
Class: |
G06T 11/20 20060101
G06T011/20; G16H 10/60 20060101 G16H010/60; G16H 40/63 20060101
G16H040/63 |
Claims
1. A method of displaying trends and variability in a physiological
dataset, comprising, on a processor: obtaining the physiological
dataset; applying a smoothing algorithm to the physiological
dataset to obtain a trend of the physiological dataset; applying a
variability algorithm to the physiological dataset to obtain the
variability of the physiological dataset; outputting a graph of the
trend of the physiological dataset; and outputting a graph of the
variability of the physiological dataset.
2. The method of claim 1, wherein the physiological dataset is
based on data obtained from a patient's respiratory system.
3. The method of claim 1, wherein the smoothing algorithm is one of
a moving average algorithm and a digital filter algorithm.
4. The method of claim 1, wherein the graph of the trend of the
physiological dataset and the graph of the variability of the
physiological dataset are one of overlaid and graphed
adjacently.
5. The method of claim 1, wherein the graph of the variability of
the physiological dataset comprises an envelope bounded on the top
by a plot of the maximums identified by the variability algorithm
and bounded on the bottom by a plot of the minimums identified by
the variability algorithm.
6. The method of claim 5, wherein the space between the bounds is
shaded.
7. The method of claim 6, wherein the graph of the variability of
the physiological dataset is used to assess and diagnose apnea.
8. The method of claim 1, wherein the physiological dataset is
interbreath interval data.
9. The method of claim 1, wherein the graph of variability of the
physiological dataset is a function of fractal scaling coefficients
calculated at various time points and over various time windows of
the dataset.
10. The method of claim 1, wherein the graph of variability of the
physiological dataset comprises one or more of, error bars, line
graphs, momentum bars, shaded areas under a curve, and a stochastic
plot.
11. The method of claim 1, wherein the magnitude of the variability
which is displayed by the graph of variability of the physiological
dataset is calculated as a function of at least one of, the raw
dataset, the smoothed dataset, multiple smoothed datasets, the
fractal scaling coefficients of the dataset, or the stochastic
coefficients of the dataset.
12. A device comprising: a transthoracic impedance measurement
device to obtain a physiological dataset; a processor receiving the
physiological dataset from the measurement device, and adapted to:
apply a smoothing algorithm to the physiological dataset to obtain
a trend of the physiological dataset; and apply a variability
algorithm to the physiological dataset to obtain the variability of
the physiological dataset ; and an output device coupled to the
processor and adapted to: output a graph of the trend of the
physiological dataset; and output a graph of the variability of the
physiological dataset.
13. A system for displaying trends and variability in a
physiological dataset, comprising: a patient monitoring device; at
least one sensor coupled to the patient monitoring device; a
processor contained within the patient monitoring device and
receiving patient data from the at least on sensor; a screen
contained within the patient monitoring device and receiving
display information from the processor; wherein the processor:
obtains the physiological dataset from the at least one sensor;
applies a smoothing algorithm to the physiological dataset to
obtain a trend of the physiological dataset; applies a variability
algorithm to the physiological dataset to obtain the variability of
the physiological dataset; outputs a graph of the trend of the
physiological dataset to the screen; and outputs a graph of the
variability of the physiological dataset to the screen.
14. The system of claim 13, wherein the physiological dataset is
based on data obtained from a patient's respiratory system.
15. The system of claim 13, wherein the smoothing algorithm is one
of a moving average algorithm and a digital filter algorithm.
16. The system of claim 13, wherein the graph of the trend of the
physiological dataset and the graph of the variability of the
physiological dataset are one of overlaid and graphed
adjacently.
17. The system of claim 13, wherein the graph of the variability of
the physiological dataset comprises an envelope bounded on the top
by a plot of the maximums identified by the variability algorithm
and bounded on the bottom by a plot of the minimums identified by
the variability algorithm.
18. The system of claim 17, wherein the space between the bounds is
shaded.
19. The system of claim 18, wherein the graph of the variability of
the physiological dataset is used to assess and diagnose apnea.
20. The system of claim 13, wherein the physiological dataset is
interbreath interval data.
Description
REFERENCE TO RELATED APPLICATIONS
[0001] The present application is a continuation application of
U.S. Non-Provisional patent application Ser. No. 15/454,500, filed
Mar. 9, 2017, with is a continuation application of U.S.
Non-Provisional patent application Ser. No. 14/640,648, filed Mar.
6, 2015, which claims priority to Provisional U.S. Application No.
61/948,964, filed Mar. 6, 2014, all entitled "METHODS AND DEVICES
FOR DISPLAYING TREND AND VARIABILITY IN A PHYSIOLOGICAL DATASET,"
and all are incorporated herein in their entirety.
BACKGROUND
1. Field of the Invention
[0002] The invention is directed to devices and methods for
displaying a physiological dataset in graphical form. Specifically,
the invention is directed toward devices and methods for displaying
trend and variability of a physiological dataset in graphical
form.
2. Background of the Invention
[0003] Medical professionals use charts of physiological data on a
regular basis to come to decisions critical to patient care.
Patient information charts have historically been written or
printed on paper, however with the advent of electronic displays,
charts of patient's data are increasingly found in electronic
forms. Everything from patient health information to real-time
physiological data is transitioning from paper to electronic form.
The transition to electronic form, linked to computers or other
programmable equipment, enables new and improved visualizations to
be applied to patient data, especially physiological data.
[0004] Physiological data is typically acquired from the patient by
means of a variety of sensors. Data can be acquired over the course
of a patient's life at regularly scheduled exams, or over a series
of hours, minutes, or in real-time in the case of continuous
monitoring.
[0005] Patients in a hospital may be connected to a variety of
sensors, monitors and devices which produce real-time traces of
physiological signals, real-time and near-real-time calculations of
physiological parameters. For example, an ICU patient could be
simultaneously connected to devices which record ECG, EMG, EEG,
capnography, pulse oximetry, pneumography, blood pressure, etc.,
yielding a plethora of physiological parameters including heart
rate, end-tidal CO2 or end-expiratory CO2, O2 saturation,
respiratory rate, tidal volume, and minute ventilation. The sheer
number of physiological datasets measured from a patient in the
hospital can easily lead to information overload.
[0006] The information overload can cause healthcare providers to
overlook aspects of the data that could indicate important aspects
of the patient's condition or the patient's state. Therefore, there
is a need to reduce information overload.
SUMMARY OF THE INVENTION
[0007] The present invention overcomes the problems and
disadvantages associated with current strategies and designs and
provides new tools and methods of displaying a physiological
dataset in graphical form.
[0008] One embodiment of the invention is directed to a method of
displaying trends and variability in a physiological dataset. The
method comprises the steps of obtaining the physiological dataset,
applying a smoothing algorithm to the physiological dataset to
obtain a trend of the physiological dataset, applying a variability
algorithm to the physiological dataset to obtain the variability of
the physiological dataset, outputting a graph of the trend of the
physiological dataset, and outputting a graph of the variability of
the physiological dataset.
[0009] In a preferred embodiment, the physiological dataset is
based on data obtained from a patient's respiratory system.
Preferably, the smoothing algorithm is one of a moving average
algorithm and a digital filter algorithm. The graph of the trend of
the physiological dataset and the graph of the variability of the
physiological dataset are preferably one of overlaid and graphed
adjacently. Preferably, the graph of the variability of the
physiological dataset comprises an envelope bounded on the top by a
plot of the maximums identified by the variability algorithm and
bounded on the bottom by a plot of the minimums identified by the
variability algorithm. The space between the bounds is preferably
shaded and the graph of the variability of the physiological
dataset is preferably used to assess and diagnose apnea.
[0010] In a preferred embodiment, the physiological dataset is
interbreath interval data. Preferably, the graph of variability of
the physiological dataset is a function of fractal scaling
coefficients calculated at various time points and over various
time windows of the dataset. Preferably, the graph of variability
of the physiological dataset comprises one or more of, error bars,
line graphs, momentum bars, shaded areas under a curve, and a
stochastic plot. In a preferred embodiment, the magnitude of the
variability which is displayed by the graph of variability of the
physiological dataset is calculated as a function of at least one
of, the raw dataset, the smoothed dataset, multiple smoothed
datasets, the fractal scaling coefficients of the dataset, or the
stochastic coefficients of the dataset.
[0011] Another embodiment of the invention is directed toward a
device comprising a transthoracic impedance measurement device to
obtain a physiological dataset, a processor receiving the
physiological dataset from the measurement device, and an output
device coupled to the processor. The processor is adapted to: apply
a smoothing algorithm to the physiological dataset to obtain a
trend of the physiological dataset, apply a variability algorithm
to the physiological dataset to obtain the variability of the
physiological dataset. The output device is adapted to: output a
graph of the trend of the physiological dataset and output a graph
of the variability of the physiological dataset.
[0012] Another embodiment of the invention is directed toward a
system for displaying trends and variability in a physiological
dataset. The system comprises a patient monitoring device, at least
one sensor coupled to the patient monitoring device, a processor
contained within the patient monitoring device and receiving
patient data from the at least on sensor, a screen contained within
the patient monitoring device and receiving display information
from the processor. The processor: obtains the physiological
dataset from the at least one sensor, applies a smoothing algorithm
to the physiological dataset to obtain a trend of the physiological
dataset, applies a variability algorithm to the physiological
dataset to obtain the variability of the physiological dataset,
outputs a graph of the trend of the physiological dataset to the
screen, and outputs a graph of the variability of the physiological
dataset to the screen.
[0013] In a preferred embodiment, the physiological dataset is
based on data obtained from a patient's respiratory system.
Preferably, the smoothing algorithm is one of a moving average
algorithm and a digital filter algorithm. The graph of the trend of
the physiological dataset and the graph of the variability of the
physiological dataset are preferably one of overlaid and graphed
adjacently. Preferably, the graph of the variability of the
physiological dataset comprises an envelope bounded on the top by a
plot of the maximums identified by the variability algorithm and
bounded on the bottom by a plot of the minimums identified by the
variability algorithm. The space between the bounds is preferably
shaded and the graph of the variability of the physiological
dataset is preferably used to assess and diagnose apnea.
[0014] In a preferred embodiment, the physiological dataset is
interbreath interval data. Preferably, the graph of variability of
the physiological dataset is a function of fractal scaling
coefficients calculated at various time points and over various
time windows of the dataset. Preferably, the graph of variability
of the physiological dataset comprises one or more of, error bars,
line graphs, momentum bars, shaded areas under a curve, and a
stochastic plot. In a preferred embodiment, the magnitude of the
variability which is displayed by the graph of variability of the
physiological dataset is calculated as a function of at least one
of, the raw dataset, the smoothed dataset, multiple smoothed
datasets, the fractal scaling coefficients of the dataset, or the
stochastic coefficients of the dataset.
[0015] Other embodiments and advantages of the invention are set
forth in part in the description, which follows, and in part, may
be obvious from this description, or may be learned from the
practice of the invention.
DESCRIPTION OF THE DRAWING
[0016] The invention is described in greater detail by way of
example only and with reference to the attached drawing, in
which:
[0017] FIG. 1: Example MV trend. (A) Raw data. Note the highly
varying signal making it difficult to determine the overall
respiratory status. (B) Visualizing a trend in the data. The
average trend helps identify general drifts in the measurements.
(C) Visualizing the variability in the data. The variability
envelope when applied in conjunction with the trend in the data
contains all relevant information from the raw signal, yet presents
it in an easier-to-comprehend fashion.
[0018] FIG. 2: Examples of average trends and variance envelopes
applied to a variety of respiratory signals (MV, TV, RR)
[0019] FIG. 3: Example of adequate ventilation (MV) over time, as
visualized by a stable trend and a stable envelope.
[0020] FIG. 4: Example of an agitated patient who may be
undermedicated. Note that the trend in the data increases slightly,
whereas the envelope increases substantially with time, indicative
of increased respiratory variability, likely caused by increase in
pain and discomfort.
[0021] FIG. 5: Example of a patient who is headed towards
respiratory compromise. The average MV trend is systematically
decreasing and so is the variability in the MV data.
[0022] FIG. 6: Example of a patient with apneic breathing pattern.
Note the increase in variability (with envelope encroaching on the
MV=0 line) coupled with a decrease in the overall trend. This is
indicative of a repetitive breathing pattern with significant
respiratory pauses and interspersed large "rescue" breaths.
[0023] FIG. 7: Example of a patient with apneic breathing pattern
as a result of opioid administration. Note the increase in
variability (with envelope encroaching on the MV=0 line) coupled
with a decrease in the overall trend. This is indicative of a
repetitive breathing pattern with significant respiratory pauses
and interspersed "rescue" breaths.
[0024] FIG. 8: Example of a patient who is headed towards
respiratory compromise following opioid administration. The average
MV trend is systematically decreasing and so is the variability in
the MV data.
[0025] FIG. 9: Example of a patient who may be undermedicated. Note
that, despite receiving a dose of opioids, the trend in the data
remains practically unchanged, whereas the envelope increases with
time, indicative if increased respiratory variability, likely
caused by increase in pain and discomfort.
[0026] FIG. 10: Example of a patient displaying hypopneic breathing
following opioid administration. The decrease in both the trend and
variability in the data suggest a regular breathing pattern at
lower volumes and rates.
[0027] FIG. 11: Example of adequate ventilation (MV) over time, as
visualized by a small change in the trend (expected result of
opioid administration) and a stable envelope.
[0028] FIG. 12: Example of an embodiment of the structure of the
device disclosed herein.
[0029] FIG. 13: Example of an embodiment of a patient monitoring
device.
DESCRIPTION OF THE INVENTION
[0030] As embodied and broadly described herein, the disclosures
herein provide detailed embodiments of the invention. However, the
disclosed embodiments are merely exemplary of the invention that
may be embodied in various and alternative forms. Therefore, there
is no intent that specific structural and functional details should
be limiting, but rather the intention is that they provide a basis
for the claims and as a representative basis for teaching one
skilled in the art to variously employ the present invention
[0031] It has surprisingly been discovered that a visualization of
physiological data aids healthcare providers in quickly assessing
important features of a monitored physiological parameter by
reducing the perceived complexity of a recorded dataset. The
invention achieves this by simultaneously displaying a
physiological parameter's trend and variability as well as their
evolution over time. This is in contrast to existing methods for
displaying physiological datasets, which generally include applying
various filtering (smoothing) algorithms. Filters generally reduce
the perceived complexity of a dataset, enabling a better assessment
of trends in the data, but in the process they reduce variability,
impairing the ability to assessment changes in variability in the
data. Variability has proven to be an important feature of
physiological signals. For example, reduced heart rate variability
can predict mortality following a heart attack.
[0032] A caregiver would not be able to assess heart rate
variability from a chart of heart rate where the dataset is
filtered. A solution to this problem is to overlay the filtered
signal with an indication of variability.
[0033] The method described herein is a means of displaying a
physiological dataset within a graphical user interface. The
dataset is calculated and/or monitored with respect to an
independent variable, e.g. time. The dataset is a measurement,
calculation or derivation related to a tissue, organ, organ system
or physiological system. Features of the time-series analysis
including the value, trend of the value and variability of the
dataset correlate with specific disease stated related to the
monitored tissue, organ or organs system. The features of the time
series analysis may also correlate with overall patient health. The
method of displaying the dataset enables medical caregivers to
quickly assess important time-series features of the dataset.
[0034] The method specifically aids in identifying the trend and
variability of the dataset with respect to an independent variable,
e.g. time. The assessment of variability combined with the trend
aids in assessing patient health or diagnosing or predicting
disease states.
[0035] The dataset may be acquired from the patient by a means of
an analog or digital sensor. The dataset may represent a
physiological signal or a calculated, estimated or derived
physiological parameter or health index. A health index is a
numerical representation based on one or more physiological
parameters, or features of their signals. The health index
correlates with patient health, disease state or overall patient
status. In one embodiment of the invention the dataset is a
respiratory parameter derived from a transthoracic impedance
measurement. In one embodiment the dataset is a calculation of
minute ventilation, calculated based on a measurement of
transthoracic impedance. In one embodiment the dataset is a
respiratory health index based on the combination of variability in
tidal volume, the trend in minute ventilation and the duty cycle of
the respiratory rate. In another embodiment of the invention, the
dataset is the rapid shallow breathing index derived from the
patient's respiratory parameters over time.
[0036] In one embodiment of the invention, the physiological
parameter is Minute Ventilation (MV). The trends in MV combined
with an assessment of the variability of MV can assist medical
caregivers to identify periods of apnea, hypopnea,
hyperventilation, impending respiratory failure/arrest, response to
narcotics, pain level, and/or depth of anesthesia.
[0037] The method described herein is preferably applied to the
dataset first by implementing a filter to reduce the perceived
complexity of the dataset. The filter enables the caregiver to
quickly assess trends in the data without suffering from
information overload of the entire dataset. The filter applied to
the dataset may be applied in software or electrical hardware. The
filter applied to the dataset may be a time-domain filter or
frequency domain filter. The filter may be moving average, a
weighted moving average, a smoothing algorithm, a Chebyshev filter,
a Butterworth filter, a Bessel filter, an elliptic filter, constant
k filter, m-derived filter, special filter, top-hat filter, or
other Fourier-transform-based filter. The window of the filter may
be 2 minutes, 5 minutes, 10 minutes, 1 hour, a custom time frame,
or another time frame and preferably corresponds to the rate at
which trends are likely to appear in the data.
[0038] An embodiment of the invention implements a smoothing
average over a two-minute window. This smoothed data is then
displayed as the trend over time. The middle panel in FIG. 1 shows
an example of the smoothed trend line overlaid on the dataset.
[0039] After the filter highlights the trend in the data, the
method preferably adds a visual indication of variability to the
graph. The visual indication of variability preferably consists of
an envelope which overlays the smoothed trend. The visualization
preferably updates in real-time for monitored parameters, but may
be applied retroactively on historical data.
[0040] In one embodiment of the invention, the minimum and maximum
points within each window are determined and stored in an array of
peaks. Preferably once the minimum and maximum points are
determined in each window position, all the peaks are plotted on
the graph. The maximum peaks are preferably then connected by line
segments, with points between the peaks being interpolated. The
minimum points are also preferably connected by line segments with
points between the minimum peaks being interpolated. The bottom
panel in FIG. 1 is an example of this envelope. In this embodiment,
the area within the maximum envelope and the minimum envelope may
be shaded.
[0041] A quantitative coefficient of variability is preferably
calculated for each point on the chart and displayed. The
coefficient of variability is preferably calculated from a window
of data points which is smaller than the total number of points on
the graph. The coefficient of variability is preferably based on
the statistics of the dataset calculated within the window. The
coefficient of variability is preferably a function of statistical
variance, standard deviation, or entropy.
[0042] In one embodiment, error bars are applied behind the
smoothed dataset. The error bars are preferably a function of the
standard deviation of the dataset within a window of, for example,
2 minutes. The error bar is preferably overlaid on the graph at the
last point in the window, the center point in the window, or the
first point in the window.
[0043] In one embodiment, a function of one or more fractal scaling
coefficients, or a function of a ratio of at least two fractal
scaling coefficients is utilized and overlaid on the graph. In one
embodiment, a set of fractal scaling coefficients is calculated for
the entire dataset (FC1), then again for the window (FC2). The
coefficient of variability is preferably calculated as a function
of one or more coefficients from the set of FC1 as compared to FC2.
One embodiment of the visualization is to display variability as a
function of the difference or absolute value of the difference of
two or more smoothing algorithms applied to the dataset. In one
embodiment of the invention, two moving average algorithms are
applied to the dataset, one with a window of ten (10) minutes and
one with a window of two (2) minutes. The visualization preferably
consists of a graph of the two moving averages overlaid on each
other, or both overlaid on the dataset, smoothed or un-smoothed.
This may enable the caregiver to see the trend from the smoothed
data as well as discern the absolute difference between the
smoothed data trends. It is understood that when the two averages
cross, i.e. the absolute difference between the two averages
reaches zero, the trend in the data has changed direction. This can
predict a rapid change in state and trigger an alarm signal.
[0044] In another embodiment, the difference between the results of
the two smoothing algorithms is calculated and displayed on a
graph. The graph is preferably overlaid on the graph of the
smoothed dataset, or appears in its own space. This visualization
preferably provides an indicator of the momentum behind a trend,
where a large difference between the results indicates a strong
trend, and a small difference between the results indicates a
stable trend. However, a change in sign indicates a reversal of the
previous trend.
[0045] Another visualization that can be applied to the data is a
stochastic plot. The stochastic plot may be overlaid on the raw
dataset or a smoothed dataset. The stochastic plot can be
interpreted by a care provider to predict a patient's future
status.
[0046] In one embodiment of the invention, the visualization
including a smoothing component and an indication of variability is
applied to one or more datasets relating to the respiratory system.
The user can interpret the visualization in order to assess or
predict patient state, health state, respiratory status, disease
state or response to a medical intervention. The user may also use
the visualization of variability to diagnose a disease. The user
may draw conclusions from the visualization including, an
assessment of the patient's response to an opioid, a diagnosis or
prediction of respiratory arrest, respiratory failure, apnea or
cardiac arrest. The user may assess the patient's respiratory
sufficiency, likelihood of successful extubation or the necessity
of intubation.
[0047] FIG. 3 illustrates an example of the display of the
visualization algorithm on a minute ventilation dataset. The
patient in the example maintains a similar minute ventilation and
minute ventilation variability over time. A caregiver could draw
the conclusion that the patient has a good status, free of various
disease states. FIG. 11 shows an example of a healthy response to
an opioid dose, with only a slightly downward trend on the MV
dataset, and little change in the signal variability. This type of
response would lead a caregiver to conclude that the patient is
correctly dosed.
[0048] FIG. 4 indicates an example of an agitated patient. In this
instance, the increase in MV variability and MV trend as shown in
the visualization could lead a caregiver to conclude that the
patient is undermedicated and could adjust the patient's dose of
pain medication accordingly. FIG. 9 is an example of a patient who
responds idiosyncratically to an opioid dose. The variability
increases, which could indicate restlessness and discomfort and
general inefficacy of the pain medication.
[0049] It is often critical for caregivers to respond to
indications of respiratory compromise as quickly as possible. The
example in FIG. 5 is a case in which a caregiver could use the
visualization to diagnose respiratory compromise and undertake a
medical intervention to prevent patient state from worsening.
Interventions could include waking the patient, administering an
opoid antagonist such as Naloxone, or intubating and ventilating
the patient. FIG. 8 is an example of the visualization applied to
an MV dataset in a patient suffering respiratory compromise as a
result of a dose of an opioid.
[0050] Apnea is a state in which the breathing is interrupted. It
may result from a variety of causes, including opioid toxicity. The
sooner opiate toxicity can be identified, the sooner a caregiver
can undertake measures to prevent the patient's condition from
worsening. Periods of apnea are generally followed by a period of
rescue breathing which may include larger than normal or faster
than normal breaths, which normalize over time. The difference
between the breaths during these periods translates to a high index
of variability in datasets related to the respiratory system. Apnea
can be identified by a downward trend in minute volume, a high
variability in respiratory rate, or interbreath interval, and a
high variability in tidal volume and minute ventilation. FIG. 6
shows an example of the increased variability and decrease in trend
in minute ventilation to indicate the onset of apnea. FIG. 7 shows
an example of the onset of apnea as a symptom of opioid toxicity in
response to a dose of opioid pain medication.
[0051] FIG. 10 shows an example of the visualization on the MV
dataset in a patient suffering hypopnea, or shallow breathing. In
terms of the trend, it is difficult to differentiate hypopnea from
apnea, however, the variability in each case is very different. The
variability in the hypopneic patient's dataset is much lower, which
allows a caregiver to differentiate between the two cases.
[0052] The methods disclosed herein may also be applied to
parameters associated with the circulatory system including
measurements of the heart rate, or its inverse, beat-to-beat
interval. Low variability in the heart rate can predict or,
indicate, or quantify the progression of many conditions including
myocardial infarction, congestive heart failure, diabetic
neuropathy, depression or susceptibility to SIDS. In this
embodiment, the envelope provides a visualization of heart rate
variability to assist the caregiver in identifying, or assessing
the risk of the aforementioned conditions.
[0053] FIG. 13 depicts a preferred embodiment of a patient
monitoring system 1300 adapted to calculated and display a
physiological parameter's trend and variability as well as their
evolution over time. Preferably, patient monitoring system 1300 is
a portable device that can be mounted on an IV pole, attached to a
bed, attached to a wall, placed on a surface or otherwise
positioned. Patient monitoring system 1300 may be adapted for use
during medical procedures, recovery, and/or for patient monitoring.
Preferably, patient monitoring system 1300 is battery powered
and/or has a power cable. Patient monitoring system 1300 preferably
has at least one input port 1305. Preferably, each input port 1305
is adapted to receive signals from one or more sensors remote to
patient monitoring system 1300. Additionally, patient monitoring
system 1300 may further include wireless communication technology
to receive signals from remote and wireless sensors. The sensors
may be adapted to monitor for a specific patient characteristic or
multiple characteristics. Patient monitoring system 1300 preferably
is adapted to evaluate the data received from the sensors and apply
the algorithms described herein to the data. Furthermore, the
patient monitoring system 1300 may be able to receive custom
algorithms and evaluate the data using the custom algorithm.
[0054] Patient monitoring system 1300 preferably further includes a
screen or display device 1310. Preferably, screen 1310 is capable
of displaying information about patient monitoring system 1300 and
the patient being monitored. Screen 1310 preferably displays at
least one graph or window of the patient's condition, as described
herein. Each graph may be a fixed size or adjustable. For example,
the graph may be customizable based on the number of data points, a
desired length and/or time of measurement, or a certain number of
features (i.e. breaths, breath pauses, or obstructed breaths).
Additionally, the scale of the graph may be adjustable.
Furthermore, the patient or caregiver (or clinician) may be able to
choose what is displayed on screen 1310. For example, screen 1310
may be able to display the mean, median, and/or standard deviation
of data being monitored; the max, min and or range of data being
monitored; an adaptive algorithm based on trend history; a adapted
algorithm based on large populations of like patients (i.e.
condition, age, weight, and events); and/or patent breathing
parameters (i.e. blood pressure, respiratory rate, CO.sub.2, and/or
O.sub.2 rates).
[0055] Patient monitoring system 1300 is preferably equipped with
an alarm. The alarm can be an audio alarm and/or a visual alarm.
The alarm may trigger based on certain conditions being met. For
example, based on trends, real-time conditions, or patient
parameter variability. The alarm may be customizable, both in
sound/visualization and in purpose. The patient and/or caregiver
may be able to navigate through multiple windows that display
different information. For example, certain windows may display the
graphs described herein, certain windows may display the patient's
biographical data, and certain windows may display the system's
status. Additionally, custom windows may be added (e.g by the
patient, caregiver, or by the system automatically). For example, a
custom window may be for clinical use, to mark events, or to
display the patient's condition.
[0056] In a preferred embodiment, patient monitoring system 1300
has a plurality of configurations. The configurations are
preferably adapted to display relevant information to a caregiver
or patient about the patient based on the patient's current
condition. For example, for a patient undergoing a surgery, the
nurse or doctor may need different information than for a patient
recovering from an illness. Preferably, at the initiation of
monitoring the patient, the patient monitoring system 1300 allows
the patient or caregiver to select a configuration. Selectable
configurations may include, but are not limited to specific
procedures, specific illnesses, specific afflictions, specific
patient statuses, specific patient conditions, general procedures,
general illnesses, general afflictions, general patient statuses,
and/or general patient conditions. Upon selection, preferably, the
patient monitoring system 1300 will automatically display data
relevant to the selection. In another embodiment, the patient
monitoring system 1300 may automatically determine an appropriate
configuration based on the data received from the patient. The
patient or caregiver may be able to customize configurations once
they are chosen.
[0057] With reference to FIG. 12, an exemplary system includes at
least computing device 1200, for example contained within the
system depicted in FIG. 13, including a processing unit (CPU) 1220
and a system bus 1210 that couples various system components
including the system memory such as read only memory (ROM) 1240 and
random access memory (RAM) 1250 to the processing unit 1220. Other
system memory 1230 may be available for use as well. It can be
appreciated that the invention may operate on a computing device
with more than one CPU 1220 or on a group or cluster of computing
devices networked together to provide greater processing
capability. The system bus 1210 may be any of several types of bus
structures including a memory bus or memory controller, a
peripheral bus, and a local bus using any of a variety of bus
architectures. A basic input/output (BIOS) stored in ROM 1240 or
the like, may provide the basic routine that helps to transfer
information between elements within the computing device 1200, such
as during start-up. The computing device 1200 further includes
storage devices such as a hard disk drive 1260, a magnetic disk
drive, an optical disk drive, tape drive or the like. The storage
device 1260 is connected to the system bus 1210 by a drive
interface. The drives and the associated computer readable media
provide nonvolatile storage of computer readable instructions, data
structures, program modules and other data for the computing device
1200. The basic components are known to those of skill in the art
and appropriate variations are contemplated depending on the type
of device, such as whether the device is a small, handheld
computing device, a desktop computer, a computer server, a handheld
scanning device, or a wireless devices, including wireless Personal
Digital Assistants ("PDAs"), tablet devices, wireless web-enabled
or "smart" phones (e.g., Research in Motion's Blackberry.TM., an
Android.TM. device, Apple's iPhone.TM.), other wireless phones, a
game console (e.g, a Playstation.TM., an Xbox.TM., or a Wii.TM.), a
Smart TV, a wearable internet connected device, etc. Preferably,
the system is technology agnostic.
[0058] Although the exemplary environment described herein employs
the hard disk, it should be appreciated by those skilled in the art
that other types of computer readable media which can store data
that are accessible by a computer, such as magnetic cassettes,
flash memory cards, digital versatile disks, cartridges, random
access memories (RAMs), read only memory (ROM), a cable or wireless
signal containing a bit stream and the like, may also be used in
the exemplary operating environment.
[0059] To enable user interaction with the computing device 1200,
an input device 1290 represents any number of input mechanisms,
such as a microphone for speech, a touch-sensitive screen for
gesture or graphical input, keyboard, mouse, motion input, speech,
game console controller, TV remote and so forth. The output device
1270 can be one or more of a number of output mechanisms known to
those of skill in the art, for example, printers, monitors,
projectors, speakers, and plotters. In some embodiments, the output
can be via a network interface, for example uploading to a website,
emailing, attached to or placed within other electronic files, and
sending an SMS or MMS message. In some instances, multimodal
systems enable a user to provide multiple types of input to
communicate with the computing device 1200. The communications
interface 1280 generally governs and manages the user input and
system output. There is no restriction on the invention operating
on any particular hardware arrangement and therefore the basic
features here may easily be substituted for improved hardware or
firmware arrangements as they are developed.
[0060] For clarity of explanation, the illustrative system
embodiment is presented as comprising individual functional blocks
(including functional blocks labeled as a "processor"). The
functions these blocks represent may be provided through the use of
either shared or dedicated hardware, including, but not limited to,
hardware capable of executing software. For example the functions
of one or more processors presented in FIG. 12 may be provided by a
single shared processor or multiple processors. (Use of the term
"processor" should not be construed to refer exclusively to
hardware capable of executing software.) Illustrative embodiments
may comprise microprocessor and/or digital signal processor (DSP)
hardware, read-only memory (ROM) for storing software performing
the operations discussed below, and random access memory (RAM) for
storing results. Very large scale integration (VLSI) hardware
embodiments, as well as custom VLSI circuitry in combination with a
general purpose DSP circuit, may also be provided.
[0061] Embodiments within the scope of the present invention may
also include computer-readable media for carrying or having
computer-executable instructions or data structures stored thereon.
Such computer-readable media can be any available media that can be
accessed by a general purpose or special purpose computer. By way
of example, and not limitation, such computer-readable media can
comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage,
magnetic disk storage or other magnetic storage devices, or any
other medium which can be used to carry or store desired program
code means in the form of computer-executable instructions or data
structures. When information is transferred or provided over a
network or another communications connection (either hardwired,
wireless, or combination thereof) to a computer, the computer
properly views the connection as a computer-readable medium. Thus,
any such connection is properly termed a computer-readable medium.
Combinations of the above should also be included within the scope
of the computer-readable media.
[0062] Computer-executable instructions include, for example,
instructions and data which cause a general purpose computer,
special purpose computer, or special purpose processing device to
perform a certain function or group of functions.
Computer-executable instructions also include program modules that
are executed by computers in stand-alone or network environments.
Generally, program modules include routines, programs, objects,
components, and data structures, etc. that perform particular tasks
or implement particular abstract data types. Computer-executable
instructions, associated data structures, and program modules
represent examples of the program code means for executing steps of
the methods disclosed herein. The particular sequence of such
executable instructions or associated data structures represents
examples of corresponding acts for implementing the functions
described in such steps.
[0063] Those of skill in the art will appreciate the preferred
embodiments of the invention may be practiced in network computing
environments with many types of computer system configurations,
including personal computers, hand-held devices, multi-processor
systems, microprocessor-based or programmable consumer electronics,
network PCs, minicomputers, mainframe computers, and the like.
Networks may include the Internet, one or more Local Area Networks
("LANs"), one or more Metropolitan Area Networks ("MANs"), one or
more Wide Area Networks ("WANs"), one or more Intranets, etc.
Embodiments may also be practiced in distributed computing
environments where tasks are performed by local and remote
processing devices that are linked (either by hardwired links,
wireless links, or by a combination thereof) through a
communications network, e.g. in the "cloud." In a distributed
computing environment, program modules may be located in both local
and remote memory storage devices.
[0064] Other embodiments and uses of the invention will be apparent
to those skilled in the art from consideration of the specification
and practice of the invention disclosed herein. All references
cited herein, including all publications, U.S. and foreign patents
and patent applications, are specifically and entirely incorporated
by reference. It is intended that the specification and examples be
considered exemplary only with the true scope and spirit of the
invention indicated by the following claims. Furthermore, the term
"comprising of" includes the terms "consisting of" and "consisting
essentially of."
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