U.S. patent application number 10/911112 was filed with the patent office on 2005-11-10 for method and apparatus for facilitating the provision of healthcare services.
This patent application is currently assigned to MedPond, LLC. Invention is credited to Gribkov, Evgueni N., Pougatchev, Vadim I., Sanders, John, Zhirnov, Yevgeniy N..
Application Number | 20050251057 10/911112 |
Document ID | / |
Family ID | 35240330 |
Filed Date | 2005-11-10 |
United States Patent
Application |
20050251057 |
Kind Code |
A1 |
Sanders, John ; et
al. |
November 10, 2005 |
Method and apparatus for facilitating the provision of healthcare
services
Abstract
The invention concerns a method and apparatus for facilitating
the provision of health care services using, for example, an
application service provider. In one embodiment of the invention, a
server computer, physiological testing unit and one or more
computer program products coordinate with one another to perform
the following tasks. First, receiving a request to utilize the
physiological testing unit to process physiological data of a
patient. Second, if the request is granted, enabling the
physiological testing unit to process the physiological data of the
patient. Third, if the request is granted, processing the account
of a user.
Inventors: |
Sanders, John; (Granbury,
TX) ; Pougatchev, Vadim I.; (Poulsbo, WA) ;
Zhirnov, Yevgeniy N.; (Poulsbo, WA) ; Gribkov,
Evgueni N.; (Kingston, WA) |
Correspondence
Address: |
Winstead Sechrest & Minick P.C.
P.O. Box 50784
Dallas
TX
75201
US
|
Assignee: |
MedPond, LLC
Austin
TX
|
Family ID: |
35240330 |
Appl. No.: |
10/911112 |
Filed: |
August 4, 2004 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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10911112 |
Aug 4, 2004 |
|
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10842294 |
May 10, 2004 |
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Current U.S.
Class: |
600/509 |
Current CPC
Class: |
Y10S 128/92 20130101;
G16H 50/70 20180101; A61B 5/349 20210101; A61B 5/4035 20130101 |
Class at
Publication: |
600/509 |
International
Class: |
A61B 005/04 |
Claims
What is claimed is:
1. A method for facilitating the provision of health care services
using a physiological testing unit, one or more memories operable
for storing one or more computer program products, and a processor
operable for executing the one or more computer program products,
the method comprising the steps of: receiving a prompt, through the
one or more computer program products, to utilize the physiological
testing unit to process first physiological data of a patient;
determining, through the one or more computer program products, if
the physiological testing unit is to be enabled to process the
first physiological data of the patient; and if the physiological
testing unit is to be enabled, enabling, through the one or more
computer program products, the physiological testing unit to
process the first physiological data of the patient.
2. The method of claim 1, wherein the step of determining if the
physiological testing unit is to be enabled to process the first
physiological data of the patient comprises the step of determining
if utilization of the physiological testing unit is authorized.
3. The method of claim 1, wherein the step of determining if the
physiological testing unit is to be enabled to process the first
physiological data of the patient comprises the step of determining
if a second data is available, wherein the second data is needed to
utilize the physiological testing unit to process the first
physiological data of the patient.
4. The method of claim 1, wherein the one or more memories are
coupled to the physiological testing unit.
5. The method of claim 4, wherein the one or more memories are
further coupled to a computer server.
6. The method of claim 1, wherein the processor is coupled to the
physiological testing unit.
7. The method of claim 1, wherein the processor is comprised of a
first processing subunit and a second processing subunit, and
further wherein the first processing subunit is coupled to the
physiological testing unit and the second processing subunit is
coupled to a computer server.
8. The method of claim 1, wherein the prompt to utilize the
physiological testing unit to process the first physiological data
of the patient is communicated, using a communications network,
from the physiological testing unit to the one or more computer
program products.
9. The method of claim 1, wherein the prompt to utilize the
physiological testing unit to process the first physiological data
of the patient is communicated, using a communications network,
from a communications device to the one or more computer program
products.
10. The method of claim 9, wherein the communications device
comprises a client computer.
11. The method of claim 1, wherein the step of enabling the
physiological testing unit to process the first physiological data
of the patient comprises the step of enabling the physiological
testing unit to obtain the first physiological of the patient.
12. The method of claim 1, wherein the step of enabling the
physiological testing unit to process the first physiological data
of the patient comprises the step of enabling the physiological
testing unit to display the first physiological data of the
patient.
13. The method of claim 1 further comprising the steps of storing,
through the one or more computer program products, the first
physiological data of the patient in the one or more memories
wherein the first physiological data of the patient is obtained
utilizing the physiological testing unit.
14. The method of claim 1 further comprising the steps of storing,
through the one or more computer program products, the first
physiological data of the patient in a second one or more memories
wherein the first physiological data of the patient is obtained
utilizing the physiological testing unit.
15. The method of claim 13 further comprising the step of
displaying, through the one or more computer program products, the
first physiological data of the patient in proximity to a second
physiological data of the patient.
16. The method of claim 1, wherein the prompt to utilize the
physiological testing unit to process the first physiological data
of the patient comprises a request for performance of a specific
physiological test, and wherein the method further comprises the
step of selecting a display, through the one or more computer
program products, in response to the request for performance of the
specific physiological test.
17. The method of claim 16, wherein the display comprises a
graphical user interface.
18. The method of claim 1 further comprising the steps of:
receiving, through the one or more computer program products, the
first physiological data of the patient; wherein the first
physiological data of the patient is obtained utilizing the
physiological testing unit; and processing, through the one or more
computer program products, the first physiological data of the
patient to produce a second processed physiological data of the
patient.
19. The method of claim 18, wherein the second processed
physiological data comprises one or more HRV measurements.
20. The method of claim 19, wherein the one or more HRV
measurements are selected from the group consisting of: 30:15
Ratio, NNmin Standing, NNmax Standing, Tmax, Trec, E/I Ratio, SD,
NNmin SB, NNmax SB, VARmax, VARmean, RMS-SD, NN, SDNN, TP, LFnorm,
HFnorm, LF/HF, VLF, LF, HF, VR and combinations thereof.
21. The method of claim 18, wherein the step of receiving the first
physiological data of the patient, the step of processing the first
physiological data of the patient to produce the second processed
physiological data of the patient, and a step of sending the second
processed physiological data, through the one or more computer
program products, to a first location are performed in a
substantially real-time manner, wherein the first location is
selected from the group comprising: the physiological testing unit,
and a communications device operatively coupled to the one or more
memories using a communications network.
22. A system comprising: one or more memory units operable for
storing one or more computer program products for facilitating the
provision of health care services; a processor coupled to the one
or more memory units, wherein the processor executes the one or
more computer program products for performing the steps of:
receiving a prompt to utilize a physiological testing unit to
process first physiological data of a patient; determining if the
physiological testing unit is to be enabled to process the first
physiological data of the patient; and if the physiological testing
unit is to be enabled, enabling the physiological testing unit to
process the first physiological data of the patient.
23. The system of claim 22, wherein the step of determining if the
physiological testing unit is to be enabled to process the first
physiological data of the patient comprises the step of determining
if utilization of the physiological testing unit is authorized.
24. The system of claim 22, wherein the step of determining if the
physiological testing unit is to be enabled to process the first
physiological data of the patient comprises the step of determining
if a second data is available, wherein the second data is needed to
utilize the physiological testing unit to process the first
physiological data of the patient.
25. The system of claim 22, wherein the one or more memories are
coupled to the physiological testing unit.
26. The system of claim 25, wherein the one or more memories are
further coupled to a computer server.
27. The system of claim 22, wherein the processor is coupled to the
physiological testing unit.
28. The system of claim 22, wherein the processor is comprised of a
first processing subunit and a second processing subunit, and
further wherein the first processing subunit is coupled to the
physiological testing unit and the second processing subunit is
coupled to a computer server.
29. The system of claim 22, wherein the prompt to utilize the
physiological testing unit to process the first physiological data
of the patient is communicated, using a communications network,
from the physiological testing unit to the one or more computer
program products.
30. The system of claim 22, wherein the prompt to utilize the
physiological testing unit to process the first physiological data
of the patient is communicated, using a communications network,
from a communications device to the one or more computer program
products.
31. The system of claim 30, wherein the communications device
comprises a client computer.
32. The system of claim 22, wherein the step of enabling the
physiological testing unit to process the first physiological data
of the patient comprises the step of enabling the physiological
testing unit to obtain the first physiological data of the
patient.
33. The system of claim 22, wherein the step of enabling the
physiological testing unit to process the first physiological data
of the patient comprises the step of enabling the physiological
testing unit to display the first physiological data of the
patient.
34. The system of claim 22 further comprising the steps of storing,
through the one or more computer program products, the first
physiological data of the patient in the one or more memories
wherein the first physiological data of the patient is obtained
utilizing the physiological testing unit.
35. The system of claim 22 further comprising the steps of storing,
through the one or more computer program products, the first
physiological data of the patient in a second one or more memories
wherein the first physiological data of the patient is obtained
utilizing the physiological testing unit.
36. The system of claim 34 further comprising the step of
displaying, through the one or more computer program products, the
first physiological data of the patient in proximity to a second
physiological data of the patient.
37. The system of claim 22, wherein the prompt to utilize the
physiological testing unit to process the first physiological data
of the patient comprises a request for performance of a specific
physiological test, and wherein the method further comprises the
step of selecting a display, through the one or more computer
program products, in response to the request for performance of the
specific physiological test.
38. The system of claim 37, wherein the display comprises a
graphical user interface.
39. The system of claim 22 further comprising the steps of:
receiving, through the one or more computer program products, the
first physiological data of the patient; wherein the first
physiological data of the patient is obtained utilizing the
physiological testing unit; and processing, through the one or more
computer program products, the first physiological data of the
patient to produce a second processed physiological data of the
patient.
40. The system of claim 39, wherein the second processed
physiological data comprises one or more HRV measurements.
41. The system of claim 40, wherein the one or more HRV
measurements are selected from the group consisting of: 30:15
Ratio, NNmin Standing, NNmax Standing, Tmax, Trec, E/I Ratio, SD,
NNmin SB, NNmax SB, VARmax, VARmean, RMS-SD, NN, SDNN, TP, LFnorm,
HFnorm, LF/HF, VLF, LF, HF, VR and combinations thereof.
42. The system of claim 39, wherein the step of receiving the first
physiological data of the patient, the step of processing the first
physiological data of the patient to produce the second processed
physiological data of the patient, and a step of sending the second
processed physiological data, through the one or more computer
program products, to a first location are performed in a
substantially real-time manner, wherein the first location is
selected from the group comprising: the physiological testing unit,
and a communications device operatively coupled to the one or more
memories using a communications network.
43. A method for facilitating the provision of health care services
using a physiological testing unit, one or more memories operable
for storing one or more computer program products, and a processor
for executing the one or more computer program products, the method
comprising the steps of: tracking, through the one or more computer
program products, utilization of the physiological testing unit to
process physiological data of a patient; and indicating in a user
record, through the one or more computer program products,
utilization of the physiological testing unit to process the
physiological data of the patient.
44. The method of claim 43 further comprising the step of
generating an invoice in response to the step of indicating in a
user record utilization of the physiological testing unit to
process the physiological data of the patient.
45. The method of claim 44, wherein the invoice includes charges
for use of consumable medical products.
46. The method of claim 43, wherein the step of tracking
utilization of the physiological testing unit to process
physiological data of a patient comprises the step of tracking,
through the one or more computer program products, generation of a
patient report.
47. A method for facilitating the provision of health care services
using a physiological testing unit, one or more memories operable
for storing one or more computer program products, and a processor
for executing the one or more computer program products, the method
comprising the steps of: accessing the one or more computer program
products; requesting, through the one or more computer program
products, use of the physiological testing unit to process
physiological data of a patient; and if the request is granted,
utilizing the physiological testing unit to process the
physiological data of the patient.
48. The method of claim 47 further comprising the steps of:
requesting, through the one or more computer program products,
performance of a specific physiological test; and receiving a
display, through the one or more computer program products, wherein
the display is selected in response to the step of requesting
performance of the specific physiological test.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of the U.S. patent
application Ser. No. 10/842,294, entitled "Method and apparatus for
measurement of autonomic nervous system function", filed on May 10,
2004, which is hereby incorporated by reference.
BACKGROUND INFORMATION
[0002] 1. Technical Field
[0003] The present invention relates to apparatuses and methods for
facilitating the provision of health care services.
[0004] 2. Description of the Related Art
[0005] The autonomic nervous system (ANS) is primarily responsible
for the fine-tuned regulation of many human organs and systems. An
individual whose autonomic nervous system correctly regulates such
organs and systems is said to have good autonomic function.
Improper autonomic function may be referred to as autonomic
dysfunction, which can be the result of autonomic neuropathy (AN).
AN can result in improper regulation of organs and systems, which
in turn may lead to the malfunction of those organs and systems. AN
is often associated with a number of disorders such as diabetes and
coronary artery disease. In fact, the last two decades have
witnessed the recognition of a significant relationship between AN
and cardiovascular mortality, including sudden cardiac death. Thus,
testing for AN may be a useful health monitoring tool.
[0006] One way to test for AN is by evaluating how well the ANS
regulates the heart through a "heart rate variability" (HRV) study.
In such a study, a patient or subject (hereinafter "patient")
performs certain breathing tests, which, in a person with a
properly functioning ANS, will cause fluctuations in the patient's
heart rate (HR). As AN increases, HRV decreases. HRV is a
measurement of the fluctuation of R-R intervals in a patient's
electrocardiogram (ECG). The R-R interval is the distance between R
peaks in a QRS complex. Detection of R-R intervals may be achieved
by various methods such as a simple threshold technique or
statistical method, both of which are known to those of ordinary
skill in the art.
[0007] HRV testing is useful for more than determining whether a
patient has AN. For example, HRV testing may be used to monitor
disease progression as a function of changes in autonomic function.
HRV testing may also be used to evaluate a patient's response to a
prescribed treatment for an autonomic disorder. Other applications
for HRV testing include: general health screening, diabetic
neuropathy assessment, pre-condition cardiac health screening,
post-myocardial infarction risk assessment and evaluation, drug
studies including the relationship between certain drug dosages and
AN function, and stress measurement of, for example, ADHD
children.
[0008] Several clinical tests, known to those of ordinary skill in
the art, help physicians or clinicians measure HRV. Examples of
such tests are the Slow Metronomic Breathing test, Valsalva test
and Orthostatic test. Each test measures certain HRV parameters,
subsets of which may indicate whether a patient is predisposed for,
or afflicted with, AN and one or more of its related maladies such
as diabetes. These three tests will now be addressed.
[0009] 1. Slow Metronomic Breathing Test
[0010] The Slow Metronomic Breathing test is designed to assess the
parasympathetic branch of the ANS. As those of ordinary skill in
the art will appreciate, during the test the patient breathes
deeply and evenly, in a supine position, at six breaths per minute
while ECG recordings are made. Any events that could alter
spontaneous breathing, such as speech or coughing, should be
limited. To foster patient compliance with the prescribed breathing
regimen, the patient should breathe for one minute following pacer
movements, similar to a metronome, which may be displayed on a
computer screen.
[0011] The breathing regimen described above helps assess ANS
function because parasympathetic regulation of the heart rhythm
relies on different types of receptors located in the lungs. These
receptors are taxed by the deep breathing performed during the
Metronomic test. More specifically, chemoreceptors detect
concentrations of CO.sub.2 and H+ ions in the arterial blood, which
change as one breathes. Chemoreceptors send signals to the brain
that are representative of the concentration of these elements. The
brain may then regulate the heart, by adjusting the heart rate, to
achieve these reported concentration levels. Mechanoreceptors,
unlike chemoreceptors, react to changes of air pressure within a
patient's airways. Breathing, and especially heavy breathing,
creates changes in intrathoracic pressure which are then sensed by
mechanoreceptors. This results in a change in blood pressure. The
baroreflex mechanism then causes changes in heart rate. These
changes in pressure produce signals that are sent along afferent
fibers from the mechanoreceptors to the brain stem. In summary,
changes in breathing can affect both chemoreceptors and
mechanoreceptors, both located in the lungs, which in turn
communicate with the brain to potentially illicit a change in HR
for a person with "good HRV."
[0012] The HRV parameters or measurements derived from the
Metronomic test may include one or more of the measurements found
in FIG. 16B. The parameters are calculated on "normal-to-normal"
inter-beat intervals (NN intervals), which are R-R intervals
calculated on beats caused by normal heart contractions paced by
sinus node depolarization.
[0013] 2. The Orthostatic Test
[0014] Like the Metronomic test, the Orthostatic test is used to
evaluate the effect of parasympathetic regulation on HR. Therefore,
the test provides a good indication of autonomic function and HRV.
More specifically, the Orthostatic test evaluates how a change in
body position affects heart rate. The patient is instructed to lie
down in an idle, relaxed, supine position. After a minute of
recording ECG signals, the patient stands up while avoiding any
rapid movements. The patient remains standing for another minute.
The patient's heart rhythm is monitored continuously while the
patient lies down and stands up. HR monitoring should continue
until a stationary state in HR is detected.
[0015] The Orthostatic test helps evaluate autonomic function
because it taxes a set of regulatory mechanisms that support
parasympathetic regulation of the heart rhythm. More specifically,
blood mass redistribution takes place when a patient changes from a
supine position to a standing position. The baroreceptors situated
in the aortic arch and carotid nodes perceive this change in blood
distribution and communicate the change to the brain via afferent
fibers. These communications cause an increase in the activation of
sympathetic efferent fibers and a decrease in activation of
parasympathetic efferent fibers. These efferent fibers then
transmit regulatory instructions from the brain down the
sympathetic and parasympathetic nerves pathways. The tonus of the
arteries in the carotid sinus is consequently decreased causing
activation of the adrenergic receptors of blood vessel walls and
perivascular tissues. Thus, the body shift causes a sympathetic
positive chronotropic effect. Concurrently, when the patient
changes positions, an increase of muscular activity takes place
thereby causing an increase in blood delivery from the extremities.
The sympathetic effects are increased and sustained during the
post-stimuli period to support the vertical posture. So, blood
pressure gradually increases due to activation of the sympathetic
NS. The increase in blood pressure causes stimulation of the
parasympathetic NS. This stimulation occurs via the baroreflex
mechanism and is followed by a decrease in HR. In summary, changing
positions taxes the ANS, which should result in a change in heart
rate for those patients with good HRV.
[0016] The HRV parameters or measurements derived from the
Metronomic test may include one or more of the measurements found
in FIG. 16A. The parameters are calculated on "normal-to-normal"
inter-beat intervals (NN intervals), which are R-R intervals
calculated on beats caused by normal heart contractions paced by
sinus node depolarization.
[0017] 3. The Valsalva Test
[0018] The Valsalva test also helps assess autonomic function. The
Valsalva test commences with the patient in the supine position
with his head slightly elevated. The patient then strains by
blowing into a mouthpiece until a 40 mm Hg pressure is obtained for
15 seconds. Following cessation of the Valsalva strain, the patient
relaxes and breathes at a normal rate. The ECG is monitored during
the strain and at 30-45 seconds afterwards. Maximum and minimum
heart rates are obtained respectively at about one second after
cessation of strain and then 15-20 seconds later. This process is
repeated three times and the largest heart rate ratio is considered
the best reflection of autonomic function. The end result of the
test is the derivation of a measurement called the Valsalva ratio.
The Valsalva ratio ("VR"), which constitutes a HRV parameter, is
the ratio of the longest R-R interval to the shortest R-R interval
at one second and 15-20 seconds after the Valsalva maneuver is
completed. Again, the methods for performing the Metronomic,
Orthostatic and Valsalva tests are known to those of ordinary skill
in the art.
[0019] While the methods for performing the Metronomic, Orthostatic
and Valsalva tests produce valuable information regarding autonomic
function, prior art methods and equipment fail to take full
advantage of the available information. For instance, in the prior
art, normative databases for HRV values are not created and
maintained. As an illustration, the prior art does not attempt to
determine normal VARmax values for patients according to such
diverse factors as race, age, smoking history and gender.
Consequently, the VARmax value of a Asian Indian, 30-year old,
non-smoking man is often compared with that of a 30-year old,
Caucasian woman who has smoked for 10 years. Doing so may lead to
an inaccurate assessment of the male patient's autonomic function.
Furthermore, the prior art does not attempt to link certain factors
such as race, age and VARmax value with a risk factor for
contracting, for example, hypertension. An additional limitation in
the prior art is the inability to provide normative databases that
expand, and whose accuracy is refined as HRV studies continue to be
performed. Finally, the prior art requires expensive, complicated
and burdensome HRV testing equipment that many non-specialists are
unlikely to use. As a result, AN associated maladies, such as heart
disease and diabetes, are not assessed as well as possible because
the vast majority of clinicians do not possess these complex
tools.
[0020] Therefore, a method and apparatus for measuring autonomic
nervous system function is needed that can help patients gain early
notice when they are at risk for developing an illness forecasted
or indicated by poor autonomic function. In addition, a need exists
for specific normative databases that provide targeted HRV
information that focuses on both demographic and health factors.
Such a normative database should help discern HRV patterns to allow
clinicians to better assess potential health issues for patients.
The normative database should continue to expand and provide more
valuable forecasting and assessment tools as HRV studies are
conducted over time. Finally, a need exists for HRV testing which
is available through an Application Service Provider model so
practitioners need not invest heavily in sophisticated equipment
that must be updated regularly. Such testing capabilities would
become a powerful tool in the clinician's hands for early detection
of various medical problems before those maladies show any clinical
manifestation. Furthermore, such capabilities would better allow
health care providers to assess progress or deterioration in a
patient's previously assessed autonomic dysfunction.
SUMMARY DESCRIPTION
[0021] In one embodiment of the invention, background data from a
population of patients is obtained. The population of patients may
be comprised of patients with both normal and abnormal autonomic
function. Then, the invention may receive ECG data from the same
population of patients. HRV parameters such as NNmin SB and SD may
be measured from the ECG data. Afterwards, discriminant analysis
may be performed on the HRV parameters and background data to
determine discriminant equations, wherein each discriminant
equation discriminates between patients with normal and abnormal
autonomic function. For instance, patterns may be identified
whereby certain HRV parameter measurements, when combined with
certain background information, such as race and gender, may
distinguish between individuals with early signs of diabetes and
those without such signs. After these equations are developed, new
patients may be tested. Each new patient provides background data
and HRV data. Then, the invention may select, from among the
discriminant equations it has previously developed from the data
from the population of patients, only those equations that pertain
to the particular patient being tested. Consequently, data from a
20 year old Asian Indian woman may be compared to other 20 year old
Asian Indian women, each afflicted with a different malady. The new
patient's HRV data could then be input into the selected equations
to provide autonomic rankings that are indicative of the new
patient's autonomic function. In one embodiment of the invention,
the background and HRV data from each new patient may be added to
the same information that exists for the population of patients
thereby creating increasingly larger normative data sets from which
future patients' autonomic function can be more accurately
assessed.
[0022] In an alternative embodiment of the invention, a method for
assessing autonomic performance concerns an application for storing
a population data set on a server. The population data set may be
comprised of physiologic data and background data received from a
population of patients wherein the population of patients is
comprised of patients with abnormal autonomic function and patients
with normal autonomic function. The application is operated on the
server by an application service provider ("ASP"). The application
determines a first discriminant equation that discriminates between
the patients with abnormal autonomic function and the patients with
normal autonomic function. A user may access the application with a
browser over a communications network such as the Internet. The
application may receive background data from a new patient and
select one or more appropriate discriminant equations. The
application may send the selected discriminant equations to the
user's client terminal. The client terminal may then enter
physiologic data from the new patient into the selected
discriminant equations to produce autonomic rankings. The autonomic
rankings are indicative of the new patient's autonomic function.
The client terminal may then send the autonomic ranking and the
physiologic data to the application. The application may use this
information to determine additional discriminant equations.
[0023] Yet another embodiment of the invention entails a method of
identifying an R-wave of an ECG signal. The method comprises
receiving an ECG signal from a patient and sampling the ECG signal
at a predetermined sampling rate to obtain a first sample, a second
sample, a third sample and a fourth sample. The samples are then
filtered and the slopes between the different samples are
calculated. The different slopes are then compared until a maximum
slope is located that exceeds a minimum threshold value and is less
than a maximum threshold value.
[0024] In still another embodiment of the invention, a method for
assessing autonomic function is concerned whereby a first set of
ECG data is received from a patient. The first set of ECG data may
have been recorded or derived while the patient was in a
substantially reclined position. The first set of ECG data is then
measured to obtain or derive a first set of HRV parameters
comprised of one or more of the following HRV parameters: RMS-SD,
TP, LFnorm, HFnorm, LF/HF, NN, SDNN, VLF, LF and HF. A second set
of ECG data is received from the patient wherein the second set of
ECG data was recorded or derived pursuant to one or more of the
following HRV tests: Orthostatic test, Metronomic test and Valsalva
test. A second set of ECG data is then measured to obtain a second
set of HRV parameters that are related to or derived from the
Orthostatic test, Metronomic test and/or Valsalva test. Finally,
the embodiment evaluates or utilizes the first set of HRV
parameters in conjunction with the second set of HRV parameters to
evaluate the patient's autonomic function.
[0025] The foregoing has outlined rather broadly the features of
the present invention in order that the detailed description of the
invention that follows may be better understood. Additional
features and advantages of the invention will be described
hereinafter, which form the subject of the claims of the
invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0026] For a more complete understanding of the present invention,
and the advantages thereof, reference is now made to the following
description taken in conjunction with the accompanying drawings, in
which:
[0027] FIG. 1 is a flow diagram illustrating a method for
measurement of autonomic nervous system function in one embodiment
of the invention.
[0028] FIG. 2 is an example of a questionnaire concerning
background information from a patient.
[0029] FIG. 3 is an example of a questionnaire concerning patient
health information.
[0030] FIG. 4 is a block diagram illustrating a computer network
for performing the processes of an embodiment of the invention.
[0031] FIG. 5 is a block diagram illustrating an exemplar data
acquisition device in an embodiment of the invention.
[0032] FIGS. 6A-8 are examples of a normative database in an
embodiment of the invention.
[0033] FIG. 9 is an example of a graphic display in an embodiment
of the invention.
[0034] FIG. 10 is a block diagram that illustrates the modules of
an embodiment of the invention.
[0035] FIG. 11 is a flow diagram illustrating the sequence of
operations that may be performed in accordance with an embodiment
of the present invention that uses an ASP model.
[0036] FIG. 12 is a data processing system that may be used for
implementing various embodiments of the present invention.
[0037] FIGS. 13A-13B are flow diagrams illustrating the sequence of
operations that may be performed in accordance with an embodiment
of the present invention that concerns ECG analysis.
[0038] FIGS. 14A-C are examples of a normative database in an
embodiment of the invention.
[0039] FIGS. 15A-B comprise a flow diagram, and accompanying table,
illustrating a sequence of operations concerning ECG analysis that
may be performed in accordance with an embodiment of the present
invention.
[0040] FIGS. 16A-D are tables illustrating examples of HRV
parameters in one embodiment of the invention.
DETAILED DESCRIPTION
[0041] 1. Acquire Background Data from Patient
[0042] FIG. 1 illustrates a method for measurement of autonomic
nervous system function. The method begins in step 100. In step
105, background data is obtained from a patient. Such data may
include, for example, age, height, weight, gender, race, smoker
status and health status. FIG. 2 illustrates exemplar questions
regarding the patient's background information. FIG. 3 illustrates
exemplar questions regarding the patient's health history. FIGS. 2
and 3 are merely exemplar questionnaires and those of ordinary
skill in the art will appreciate that more or less detailed
questions or other questions can be asked. For example, the patient
may be asked whether he has cancer, and if so, specifically what
type of cancer. If any "medical conditions" are indicated, such as
cancer, then the patient may be deemed, in a general sense,
"unhealthy." If no "medical condition" is noted, the patient may be
generally deemed "healthy." In addition, the clinician may make
clinical observations regarding the patient and include those
observations along with the data supplied by the patient. For
example, the clinician may note whether the patient presents with
clinical symptoms of abnormal autonomic function such as tingling
sensations in the patients arms or legs. If such symptoms are
present, the clinician may note the patient has abnormal autonomic
function. Clinician is used to generally encompass any healthcare
provider such as a technician, technologist, nurse, therapist or
doctor.
[0043] 2. Conduct HRV Tests
[0044] Returning to FIG. 1, step 110 entails autonomic testing of
the patient to obtain or derive ECG data. Such testing may occur
after background information has been received from the patient in
step 105. The patient may undergo provocative HRV tests or studies
such as the Metronomic Breathing test, Valsalva test and the
Orthostatic test which were described in detail above. These tests
are called provocative tests because a patient must provoke his
nervous system, by standing up or breathing in a certain way, to
produce results indicative of his HRV.
[0045] In addition to the provocative tests, a Short-Term Resting
HRV test may also be used to derive ECG data and HRV parameters.
The test is conducted over a five minute period while ECG data is
derived or recorded from a patient resting in a substantially
reclined position. For example, the patient may be resting in a
lying or sitting position. The patient breathes normally and in a
non-provoked manner. For example, he does not time his breathing as
is the case in the Metronomic test. Furthermore, he does not exhale
forcefully in an effort to reach a certain air pressure as is the
case in the Valsalva test. Nor does the patient recline and then
stand up in order to test his ANS as is the case with Orthostatis
test. Therefore, specialized spirometric equipment is not needed.
Also, patients who cannot tolerate stressful provocative measures,
for health reasons, can still undergo this HRV test.
[0046] The Short-Term Resting HRV test assesses the balance between
the sympathetic and parasympathetic branches of the ANS. These
aspects of the nervous system have an effect on autonomic function.
Historically, this test was used in limited capacities in assessing
autonomic function and HRV. The limited use was due, at least in
part, to the complexity associated with deriving HRV parameters
from the data produced by the test. Consequently, the Metronomic,
Valsalva and Orthostatic tests were favored over using the
Short-Term Resting HRV test. Furthermore, the prior art often
taught that just a few parameters from the provocative tests were
sufficient to assess autonomic function.
[0047] In one embodiment of the invention, the Short-Term Resting
HRV test is used in a novel way to assess autonomic function. The
Short-Term Resting HRV test results are combined with results from
one or more of the provocative tests to assess autonomic function.
By so combining the results from Short-Term Resting HRV test and
one or more provocative tests, autonomic function may be assessed
in a more accurate way than is possible with the cursory prior art
methods of testing autonomic function.
[0048] At least ten HRV parameters, existing in both the time and
frequency domains, can be monitored in the Short-Term Resting HRV
test. All of parameters are calculated on "normal-to-normal"
inter-beat intervals (NN intervals), which are R-R intervals
calculated on beats caused by normal heart contractions paced by
sinus node depolarization. All time-domain HRV parameters are
derived directly from NN intervals recorded during the test. The
frequency-domain HRV parameters are derived from the power spectral
density (PSD) calculated by means of a Fast Fourier Transform
(FFT).
[0049] As seen in FIG. 16C, the following is a list of definitions
for the time-domain HRV parameters or measurements. First, Mean NN
interval ("NN") is a mean inter-beat interval value averaged over
the entire ECG recording and is measured in milliseconds. Second,
SDNN ("SDNN") is a standard deviation of the NN intervals that is
calculated from the square root of the variance of those intervals.
Variance is the mathematical equivalent to the total power of the
spectral analysis. Consequently, variance reflects all cyclic
components of variability in a recorded series of NN intervals. The
actual values of SDNN depend on the length of recording whereby the
longer the recording is, the higher the SDNN values are. Thus, one
should not compare SDNN values derived from ECG recordings of
different lengths. SDNN is measured in milliseconds. Third,
"RMS-SD" is the root mean square of the differences in successive
NN intervals. This measure is an estimate of high-frequency
variations of heart rate, derived from short term NN recordings,
that reflects an estimate of parasympathetic regulation of the
heart. RMS-SD is measured in milliseconds.
[0050] As seen in FIG. 16D, the following is a list of definitions
for the frequency-domain HRV parameters or measurements. First,
Total Power ("TP") is a short-term estimate of the total power of
the power spectral density in the range of frequencies between 0
and 0.4 Hz. This measure reflects overall autonomic activity where
sympathetic activity is a primary contributor. Total Power is
calculated in milliseconds squared (ms.sup.2) or (ms.sup.2/HZ).
Second, Very Low Frequency ("VLF") is a band of power spectrum
range between 0.0033 and 0.04 Hz. This measure is not well defined
in terms of physiologic mechanisms that cause the VLF component of
the power spectrum. Generally, this parameter indicates overall
activity of various slow mechanisms of sympathetic function. VLF is
calculated in milliseconds squared (ms.sup.2). Third, Low Frequency
("LF") is a band of the power spectrum range between 0.04 and 0.15
Hz. This measure reflects both sympathetic and parasympathetic
activity. Generally, the parameter is a strong indicator of
sympathetic activity in long-term recordings. Parasympathetic
influence is represented by LF when the respiration rate is lower
than nine breaths per minute or while taking a deep breath. Thus,
when the patient is in a state of relaxation with slow and even
breathing, the LF values can be very high, indicating increased
parasympathetic activity rather than an increase of sympathetic
regulation. LF is calculated in milliseconds squared (ms.sup.2).
Fourth, High Frequency ("HF") is a band of the power spectrum range
between 0.15 and 0.4 Hz. This measurement reflects parasympathetic
(vagal) activity. HF is also known as a "respiratory" band because
it corresponds to the NN variations caused by respiration. This
phenomenon is known as respiratory sinus arrhythmia (RSA). Heart
rate increases during inhalation and drops during exhalation. Slow,
even breathing causes an increase in the amplitude of the HF peak
on the power spectrum. High Frequency is calculated in milliseconds
squared (ms.sup.2). Fifth, LF/HF Ratio ("LF/HF") is the ratio
between the power of Low Frequency and High Frequency bands. This
measure indicates overall balance between sympathetic and
parasympathetic systems. Higher values reflect domination of the
sympathetic system while lower ones reflect domination of the
parasympathetic system. When deep and even breathing occurs,
however, the elevation of this parameter reflects an increase of
parasympathetic regulation due to the effect of RSA. LF/HF Ratio is
calculated in normalized units. Sixth, Normalized Low Frequency
("LFnorm") is the ratio between the absolute value of the Low
Frequency and the difference between Total Power and Very Low
Frequency. This measure minimizes any effect of changes in Very Low
Frequency power and emphasizes changes in sympathetic regulation.
Normalized LF is calculated in percentile units. Seventh,
Normalized High Frequency ("HFnorm") is the ratio between the
absolute value of the High Frequency and the difference between
Total Power and Very Low Frequency. This measure minimizes any
effect of changes in Very Low Frequency power and emphasizes
changes in parasympathetic regulation. Normalized HF is calculated
in percentile units.
[0051] Those of ordinary skill in the art will appreciate that
there are a number of alternative embodiments available which allow
for patients to undergo HRV testing using other methodologies and
parameters not specifically mentioned above, and that such
embodiments are within the scope of the present invention.
[0052] 3. Recording Equipment
[0053] FIG. 4 illustrates equipment that may be used in one
embodiment of the invention. The exemplar embodiment of the
invention may comprise one or more testing units 405, doctor's
workstations 410 and an internet-based server 415. The testing unit
405 is used for conducting autonomic assessment tests. As
illustrated in FIG. 5, the testing unit 501 may consist of a PDA
545 (personal digital assistant or handheld computer), utilizing
Windows Mobile 2003 OS, or a Tablet PC, using, for example, Windows
XP or Windows CE, and an ECG/Pressure acquisition device (EPAD)
550. The EPAD 550 provides, for example, functionality to measure a
single-channel ECG and airflow pressure. The EPAD 550 may have
three input connectors to attach standard ECG lead wires 510, in an
isolated fashion, with disposable pre-gelled snap electrodes. The
three ECG electrodes are typically applied approximately one inch
below the middle of both collarbones and mid anterior on the medial
line at ribs six and eight. The EPAD 550 may utilize individual,
replaceable, 0.060 Pin connector, AHA color-coded patient lead
wires. The EPAD 550 may incorporate 10 bit resolution, or more, and
a frequency response of 0.05 to at least 45 Hz. The ECG signal from
ECG lead wires 510 is amplified by amplifier 525 and digitized via
the analog-to-digital (A/D) converter 520 using, for example, a
sample rate of 300 samples/second using methods known to those of
ordinary skill in the art. Hypoallergenic hydrogel electrodes
combined with Ag/AgCl sensors provide reliable tracings. An
exemplar electrode is the Easytrode.TM., available from sEMG, 202
Providence Mine Rd., Ste. 202, Nevada City, Calif. 95959.
[0054] The EPAD 550 also has an input tip to connect to a
spirometric mouthpiece 505, via flexible plastic tubing, for
measuring airflow and pressure when breathing through the
mouthpiece. The pressure signal from mouthpiece 505 is converted
into electronic form via the pressure transducer 515 and is then
digitized via the A/D converter 520 using methods known to those of
ordinary skill in the art. The spirometric circuitry may provide
for a flow range of +/-14 liters/second with a volume between 0 and
8 liters expressed in body temperature and pressure saturated with
water vapor conditions (BTPS). The flow specifications may allow
for the greater of +/-5% or 200 ml/sec for FEF 25%-75% (forced
expiratory flow) and the greater of +/-10% or 400 ml/sec for PEF
(peak expiratory flow). The same circuitry may, in one embodiment
of the invention, provide volume specifications that allow for the
greater of +/-3% or 50 ml for forced vital capacity (FVC) and
forced expiratory volume in one second (FEV1). Elevation correction
should allow for elevations of 0 to 15,000 feet. Accuracy and BTPS
conditions may comply with Am. Thoracic Society Standards from
1994.
[0055] The digitized ECG and pressure signals are coupled to the
processor 530. Processor 530 may execute programming instructions
by which a patient's heart rate variability is analyzed in response
to the measured physiological data and may take various forms, such
as conventional microprocessors of a standard personal computer,
workstation or other microprocessor-driven device. In one
embodiment of the invention, the processor 530 is an
INTEL-compatible microprocessor of IBM-compatible personal
computers. The EPAD 550 may be implemented using a standard
personal computer chassis with certain components (e.g., amplifier
525 and analog-to-digital (A/D) converter 520) provided in the form
of circuit modules adapted for insertion into I/O ports of the
computer. The memory 535 is coupled to the processor 530 and may
include a Random Access Memory (RAM) for temporary data storage
and/or a device with read/write access for permanent data storage,
such as a hard drive. The memory 535 may be available to store
physiological data until the data is transmitted to the PDA 545. A
person of ordinary skill in the art will appreciate that this
transmission may occur in numerous ways including wireless means
observing the Bluetooth protocol, WiFi, satellite or other wireless
transmission means. It will be further appreciated by those of
ordinary skill in the art that the techniques of the present
invention may be implemented with various apparatuses, including
both hardware and software. For example, the PDA 545 may receive
previously recorded data from holter recordings. Doing so may allow
HRV studies, such as the Short Term Resting HRV test, to be
performed on previously recorded data. Consequently, ECG studies,
taken for reasons completely unrelated to HRV studies, may still be
analyzed for HRV purposes. The PDA 545 may also receive data from
implantable devices such as pacemakers or AICD's. The devices may
communicate with the PDA 545 in real time or may deliver ECG data
upon interrogation by the PDA 545. Other examples of alternative
HRV testing equipment include the Qmed Monitor nDx.TM., from Qmed
Inc., and the ANScore System.TM., from Boston Medical Technologies,
Inc.
[0056] The data may be transmitted from the EPAD 550 to the PDA
545, or directly to the doctor's workstation 410. The doctor's
workstation 410, as shown in FIG. 4, utilizes software executing
on, in one example, the Windows Me/2000/XP operating system. The
software may be installed on any desktop or laptop computer that
has, for example, USB connection capability and access to the
Internet. The doctor's workstation 410 software can be programmed
to automatically acquire test data from the PDA 545 every time the
PDA 545 is placed on its cradle, which is connected to the PC via
the USB port. The workstation 410 allows for management of test
data by facilitating the following: obtaining test data from the
PDA 545, viewing and verifying test data, sending test data and/or
patient data to the server 415, accessing normative databases and
discriminant equations for HRV assessment from the server 415, as
will be discussed below, viewing and printing pre-formatted test
reports, deleting test data and exporting test data to other
locations. In one embodiment of the invention, the workstation 410
has at least a Pentium-II 350 MHz processor, 32 MB of RAM, video
card with at least 800.times.600 High-Color resolution, 50 MB of
free hard disk space and CD ROM drive. In other embodiments, the
workstation 410 may be omitted whereby, for example, the testing
unit 405 may communicate directly with the server 415 via wireless
means such as WiFi, satellite transmission or, for example, other
forms of wireless transmission. In still other embodiments, the
testing unit 405 may be combined with the doctor's workstation 410
into one portable unit, such as a tablet PC. The tablet PC may
communicate with the server 415.
[0057] The test evaluation server 415 may be an internet server
that provides multi-user connection capability. The normative
databases and discriminant functions, to be addressed more
thoroughly below, may be stored on the server 415 or alternatively
on the doctor's workstation 410. Many users may simultaneously
connect to the server 415. The server software can provide for
highly secure communication between any user and the server itself.
For example, the software can have a digital certificate that
encrypts data using Secure Sockets Layer (SSL) technology. The SSL
security protocol provides data encryption, server authentication,
message integrity, and optional client authentication for a TCP/IP
connection. SSL technology is available in 128-bit encryption key
strength. A person of ordinary skill in the art will appreciate
that SSL is an example of applicable encryption technology and that
numerous other forms of encryption technology are feasible.
[0058] Any of the four aforementioned HRV tests may be conducted
using standard HRV testing equipment and methods known in the art
(e.g., using the Task Force Report for Heart Rate Variability:
Standards of Measurement, Physiologic Interpretation, and Clinical
Use, Circulation Vol. 93. No 5, 1996, which is incorporated herein
by reference).
[0059] 4. Detecting R-Waves
[0060] The above equipment should be able to record ECG signals
because, as previously noted, HRV studies concern changes in heart
rate over time. Examining the change in R-R cycle length monitors
these changes. The R-R cycle length is determined by measuring the
amount of time in between the R waves of two consecutive QRS
complexes. FIGS. 13A-13B illustrate one embodiment of a method,
which may be implemented by the above equipment, for identifying
the R-wave of an ECG signal. After beginning the process in step
1300, step 1305 commences whereby an ECG signal is received and
sampled. X[i] represents an exemplar sample data point. The
sampling rate may be, for example, 256 samples/second, although
other sampling rates may suffice. In step 1310, sample X[i] is
processed by a band pass filter (BPF). The band pass filter may
have, for example, a pass band of 5-40 Hz, thereby removing DC,
baseline drift, high frequency noise, artifact and muscle activity,
which normally occupies, approximately, the 100 Hz frequency range.
The band pass filter may be elliptical in nature to promote better
signal quality and diminish distortion.
[0061] In step 1315, the previously filtered data is filtered once
more in a moving average filter (MAF). In one embodiment of the
invention, the MAF is a sixth order filter, although other orders
may suffice. The MAF output is represented by the following
equation:
Y[i-n/2]=1/n*(X1[i-n]+X1[i-n+1]+ . . . +X1[i]).
[0062] Using a MAF helps ameliorate the effects of noise by
examining multiple samples at once. Doing so helps diminish the
effect of outlier points that may be present due to noise. In one
embodiment of the invention, the MAF may average seven samples at a
time, but fewer or more samples may be filtered. This moving window
smoothes out the effects of noise yet avoids becoming a burden on
processing bandwidth. Thus, the calculations may be done in real
time. The MAF plays an important role because noise is a constant
problem in many HRV testing situations, especially since clinical
settings may have other noise-emitting equipment in the room. In
addition, the HRV equipment is often used by individuals not
accustomed to proper skin preparation and electrode placement which
are important for high quality ECG recordings. While the MAF is
described in one embodiment of the invention, various filtering and
signal averaging techniques may be used in various embodiments of
the invention. Those of ordinary skill in the art will realize the
aforementioned filtering techniques may be carried out in hardware
or software.
[0063] In step 1320 of FIG. 13A, the amplitude difference in
consecutive samples is calculated to obtain slope. Such a
calculation is represented by the following equation:
D[i]=Y[i-n/2]-Y[i-n/2-1], wherein D[i] represents slope. To magnify
the slope, D[i] may be squared or raised to an exponential power,
such as 4, although other powers may suffice. Choosing an even
power converts negative amplitude readings into positive values,
thereby accounting for negative R waves or positive R waves that
read as negative R waves due to improper electrode placement.
Again, electrode placement may be improper due to administration of
HRV tests by individuals that lack specialized, cardiac-related
experience. For example, this may occur if a patient at home uses
the present invention.
[0064] In step 1325, D[i] slope is compared to the preceding
(D[i-1]) and succeeding (D[i+1]) slopes. If D[i] is not greater
than the other slopes, the process returns to start 1300 and D[i]
is not deemed to be an R wave. If D[i] is greater than the other
slopes, the process continues with D[i] serving as a prospective R
wave.
[0065] A peak slope is sought because R waves typically possess a
frequency of approximately 20 Hz, a frequency higher than other
waves found in the ECG. Therefore, finding the peak slope in an ECG
complex leads to locating the R wave. The prior art typically
searches for peak amplitude, instead of peak slope, in an effort to
identify an R wave. Doing so leads to high amplitude artifacts and
noise being incorrectly labeled as an R wave. Because an embodiment
of the present invention focuses on slope in pursuit of the 20 Hz R
wave, noise with high amplitude and high frequency can be filtered
out as discussed above. A maximum amplitude, which may be
indicative of noise, may not be so filtered. Also, setting a
maximum amplitude threshold might accidentally remove valuable R
waves with high amplitudes. In addition, HRV studies are often of
major benefit to older patients in predicting various maladies, and
such patients often have low amplitude R waves brought on by
diminished cardiac strength. The frequency of their R waves
changes, however, less drastically and is therefore preferable to
amplitude. In summary, the present invention's focus on maximum
frequency or slope is preferable to maximum amplitude.
[0066] The peak slope, representative of what might prove to be a R
wave, may next be validated to ensure it truly represents the
maximum slope of a R wave. To do so requires a pool of R waves that
can be compared to the prospective R wave. In step 1330, after the
prospective R wave associated with D[i] has been determined, the
number of previously determined R waves is questioned. In step
1335, if less than a predetermined number of such R waves have been
found, step 1340 is engaged. An example of such a predetermined
number of R waves is ten, although other values may be used to
provide a proper pool of waves. In step 1340, D[i] is compared
against a threshold slope value. The minimum threshold (minTHR) may
be indicative of a minimum slope commonly attributed by those of
ordinary skill in the art to R waves. If D[i] is less than the
threshold slope, D[i] is determined to not be representative of a R
wave and the entire ECG detection sequence begins anew at START
1300. If D[i] does exceed the threshold, further validation of the
prospective R wave continues. In addition, the slope threshold is
set to D[i], in step 1345, for future comparisons. At the beginning
of the ECG detection sequence, the threshold may be set to
zero.
[0067] In step 1355, an R-R interval is calculated using the
prospective R wave, which is associated with a time at which D[i]
occurs, and the immediately preceding, previously confirmed R wave.
If no previous R wave exists, the newly confirmed R wave is stored
and the ECG detection process begins anew.
[0068] In step 1360, a confirmation period begins by verifying that
the R-R interval, calculated in step 1355, which is associated with
D[i], is greater than a minimum cycle length (minCP) and shorter
than a maximum cycle length (maxCP). At the beginning of the ECG
detection sequence, minCP may be set to 333 ms and maxCP may be set
to 2000 ms. A typical R-R cycle length fits within these bounds.
Those cycle lengths that are not within these bounds are more
commonly associated with noise or other non-sinus cardiac rhythms.
For example, signal artifacts, which are normally filtered out from
genuine ECG data using previously described methodologies, often
contain many high frequency signals, with short cycle lengths, in
rapid succession. The lower bound (min CP) would help ensure these
values are not labeled as R waves. The minCP and maxCP values
identified above are examples only, and those of ordinary skill in
the art may use other values. If the cycle length meets the
requirements of step 1360, the prospective R wave is confirmed as
an R wave and is no longer considered to be a prospective R wave.
The R-R interval may now be used in the evaluation of many HRV
parameters as previously described.
[0069] In step 1365, the number of previously determined R waves is
questioned again in light of the newly determined R wave. In step
1365, if less than a predetermined number of such R waves have been
found, step 1375 is engaged. An example of such a predetermined
number of R waves is 10, although other values may be used to
provide a proper pool of waves. In step 1375, if no such number of
waves exists, the minimum threshold is set to, for example, zero.
This value is set in step 1345. The ECG detection sequence then
begins again in step 1300.
[0070] If there is such a predetermined amount of R waves, as
illustrated in step 1380, a median value of a certain number of
immediately preceding, previously detected peak slopes, each
associated with a previously determined R wave, may be calculated.
The selected peak slopes do not have to immediately precede the
most recently confirmed R wave. There may be maximum number of
preceding R waves that may be entered into the median calculation.
The maximum number is thirty in one embodiment of the invention.
The median peak slope value is calculated and then multiplied by a
first predetermined value to obtain a new minimum threshold
(minTHR). The median may also be multiplied by a second
predetermined value to obtain a new maximum threshold (maxTHR). In
one embodiment of the invention, the first predetermined value is
0.0625 and the second predetermined value is 1.6. Both values were
arrived at empirically and are only exemplar values. Other values
may be used. In addition, mean, average or mode values, or similar
methods related thereto, may be substituted for median values.
[0071] In step 1385, the minCP and maxCP are reevaluated in light
of the newly confirmed R wave. These values may be obtained by
finding the minimum R-R cycle length (minRR) and maximum R-R cycle
length (maxRR) from a certain number of immediately preceding,
previously detected peak slopes, each associated with a previously
determined R wave. The selected peak slopes do not have to
immediately precede the most recently confirmed R wave. There may
be maximum number of preceding R waves that may be analyzed. The
maximum number is thirty in one embodiment of the invention. Once
minRR and maxRR have been found, maxCP and minCP are calculated as
follows:
maxCP=maxRR+maxRR/2
minCP=minRR-minRR/2
[0072] These formulae simply set CP thresholds equal to maximum and
minimum R-R intervals, found within a set of R waves, with 50%
tolerance. The level of tolerance is an empirical value and may be
adjusted in other embodiments of the invention. If maxCP>2000,
the newly calculated maxCP is reset to 2000. If minCP<333, the
newly calculated minCP is reset to 333. These values, as previously
described, are known to those of ordinary skill in the art as
reasonable bounds for R-R intervals. In step 1390, the ECG
detection process ends or loops back to step 1300.
[0073] In subsequent iterations of the ECG detection scheme, the
predetermined number of previously determined R waves, as set out
in step 1330, will eventually be met. Then, step 1350 may be
performed. A newly determined slope may then be compared to the new
minimum and maximum thresholds determined in step 1380. These
values help to verify if a prospective R wave bears a resemblance
to the median value of previously determined R waves. If the
prospective R wave is random noise or an artifact, it would likely
not pass this test. In addition, waves with smaller slopes, such as
the P wave, would not exceed the minimum threshold. Because the
median values may be calculated on the thirty most recently
determined R waves, for example, the threshold values are adaptive
to true changes in heart rate which may have been brought on by any
number of factors, including provocative measures undertaken in HRV
testing. After step 1350, confirmation of the prospective R wave
continues as previously described and as indicated in FIG. 13A. One
of ordinary skill in the art will appreciate that there are a
number of alternative embodiments available which allow for R wave
detection and that such embodiments are within the scope of the
present invention.
[0074] The ECG detection sequence, in its many embodiments, has
several advantages over the prior art. The sequence helps combat
noise and thereby identifies R waves more accurately. The method
also provides flexibility in contrast to the rigid systems
represented by the prior art. Such flexibility exists in, for
example, the method's ability to adjust boundaries (e.g., minTHR)
according to patient data that is received by the system. In
addition, the resultant ability to accurately measure R-R cycle
lengths, in real time, helps a clinician terminate a lengthy study,
such as the 5 Minute Resting HRV study, if poor signals are being
generated. Then, for example, electrode patches can be re-applied
and the test can begin again.
[0075] 5. Further Verifying R Waves
[0076] FIG. 15A illustrates an embodiment of a method for further
verifying that the above process has accurately detected R waves.
The method helps distinguish abnormal waves from normal R waves.
The abnormal waves may be, for example, artifact signals produced
by sources other than the heart. Such artifact may be due to a
clinician contacting a loose electrode. Other examples of abnormal
waves are ectopic heart beats that produce R waves. These R waves
represent heart activity other than normal waves originating from
sinus node activity.
[0077] An embodiment of the verification method begins at 1500. In
1505, a sample array of "N" RR intervals is collected. DC bias is
removed from the intervals in 1510. The mean ("M") and standard
deviation (".sigma.") of the intervals is calculated in 1515. In
1520, the interval ("i") to be examined is set to "0". Using
statistical methods known to those of ordinary skill in the art, in
1525 a "T" value is calculated as follows: T=absolute value of
(RR[i]-M)/.sigma.. In addition, a "t" value is ascertained using
the degree of freedom table illustrated in FIG. 15B. For example,
for an array of 15 RR intervals, the "N-1" degree of freedom value
is 14 and the corresponding t value is 2.24. In 1530, T is compared
to t. If T is not larger than t, RR[i] is likely not an abnormal
beat. Consequently, in 1545 the next interval, RR[i+1], is set to
be examined. If 1550 indicates RR[i] was not the last interval in
the array, the process begins anew as 1555 returns to 1525 to begin
analysis of RR[i+1]. If RR[i] is the last interval in the array,
1555 returns the process to 1500 to begin analysis of a new array
of RR intervals.
[0078] In 1530, if T is larger than t, RR[i] may be an abnormal
beat and must be analyzed further. In 1535, if RR[i] is less than
70% of RR[i-1] or more than 130% of RR[i-1], RR[i] is deemed an
abnormal beat which may be indicative of artifact or, for example,
an ectopic beat. In 1540, RR[i] may be set to a point that is
interpolated between preceding and proceeding valid RR intervals.
The interpolated point may be defined as follows:
RR[i]=(RR[i-1]+RR[i+1])/2. Consequently, the abnormal wave, in its
original form, is removed from further analysis. More specifically,
time domain analysis of the array will not consider those RR
intervals preceding and proceeding the abnormal signal. However, in
frequency domain analysis, the artifact is adjusted so that the R
wave is still analyzed but only at its interpolated position and
not its original position. In 1545, the next interval, RR[i+1], is
set to be examined. One result from this verification process is
that abnormal waves that were previously identified as R waves are
no longer so identified. The method is known to those of ordinary
skill in the art and is further described in the following article:
D. Sepetliev, Statistical methods in medical scientific research
(Medicine, Moscow, 1968), which is hereby incorporated within.
[0079] The clinician may still wish to further verify that the R
waves were accurately detected. In one embodiment of the invention,
the clinician may view a display that illustrates a 5 second window
of ECG data. Within the window, each normal and abnormal R wave is
identified with, for example, a marker that may be in the form of a
cross-hair. FIGS. 13A-13B addresses detection of normal R waves. So
that the clinician has a general idea of where the 5 second window
is taken from, a graph that tracks HR throughout the ECG recording
is displayed. The section of the HR graph that pertains to the
selected 5 second ECG window is highlighted. If the clinician
locates a normal R wave not identified by the invention, he may
mark the missed wave, through use of a graphical user interface
(GUI), so that the system will now recognize the missed wave as a
normal R wave. If there are waves, such as artifacts, that have
been incorrectly identified by the invention as an R wave, the
clinician may remove the marker using the GUI. If an ectopic beat
has been marked as a normal R wave, the clinician may use the GUI
to toggle the identification to one representing an abnormal R
wave. The clinician may repeat this process, by moving from one-5
second window to another 5-second window, until the entire ECG
recording has been analyzed.
[0080] 6. Generate HRV Parameters
[0081] Again referring to FIG. 1, once the autonomic tests have
been administered in step 110, the resultant physiologic data, such
as ECG data that was derived from the tests, is measured and other
physiologic data, such as HRV parameters may be obtained or derived
in step 115. The exact HRV parameters generated are a function of
which autonomic tests are administered. For example, the HRV
parameters measured or derived in the Metronomic test may include
one or more of the following parameters listed in FIG. 16B. The HRV
parameters measured or derived in the Orthostatic test may include
one or more of the parameters listed in FIG. 16A. The Valsalva
ratio is measured or derived from the Valsalva test. Finally, for
the Short-Term Resting HRV test, one of more of the HRV parameters
listed in FIGS. 16C-D may be derived. While the Metronomic
Breathing test, Valsalva test, Orthostatic test and Short-Term
Resting HRV test may result in the twenty-two exemplar parameters
just listed, other HRV parameters may be derived. Those of ordinary
skill in the art will appreciate that various forms of physiologic
tests may be used in conjunction with the various embodiments of
the invention. These physiologic tests entail the tests
specifically listed above, as well as variations and combinations
of those tests, and other tests no specifically addressed herein.
Various forms of physiologic data may be derived from these tests.
The derived physiologic data may constitute a raw form of data or
may be a processed form of data. The processed data may be derived
directly or indirectly from the original form of the data. For
example, the data may constitute raw ECG data, artifact free ECG
data or HRV parameters derived from the ECG. The physiologic data
need not be limited to ECG data. For example, in one embodiment of
the invention the physiologic data may relate, for example, to
blood pressure. These physiological tests and various forms of
physiological data, as well as related embodiments, are within the
scope of the present invention.
[0082] 7. Determine Whether Normative Database Exists
[0083] Initially, no database of test results may exist from which
normative values may be derived. Consequently, in step 110, a
statistically significant number or population of individuals must
be tested in order to generate data that can be gathered and
compiled into a database. Such a population may be tested according
to any number of HRV tests including the Slow Metronomic Breathing
test, Valsalva test, Orthostatic test or Short-Term Resting HRV
test. Assuming, in step 120, that no such database exists
initially, step 125 calls for the addition of the patient data
obtained in steps 105 and 110 to be added to the database, which
may reside on the server 415. Patient data should continue to be
collected at least until a statistically significant data set from
a population of patients is achieved. What may constitute such a
statistically significant data set will be discussed in more detail
in conjunction with step 135.
[0084] 8. Perform Discriminant Analysis
[0085] As test results and patient information are entered into the
database, discriminant analysis of the data may begin in step 130.
The data set can be classified according to any number of variables
such as, for example, type of test administered (e.g., Metronomic
and/or Orthostatic), parameters monitored (e.g., E/I ratio and/or
SDNN), age, gender, race, smoking history and health condition
(e.g., whether a patient has pancreatic cancer or simply whether a
patient is healthy or ill). Healthy individuals may be included in
addition to those with conditions such as diabetes or heart
disease. Subsets of these variables may indicate the severity of AN
related to maladies such as diabetes. On a more general note, the
patients in the data set may be given a preliminary classification
that helps measure the severity of various health conditions. For
example, each patient in the data set may have a health
classification such as "no autonomic dysfunction", "borderline
dysfunction" or "clinically evident autonomic dysfunction."
[0086] In the following example of a data set, a group of 128
patients took the 5-min resting HRV test, Slow Metronomic Breathing
test and Orthostatic test. All patients were 30-35 year old
Caucasian, non-smoking men. The group consisted of two subgroups:
64 "healthy" patients and 64 patients with clinically evident
diabetic autonomic dysfunction. The data set, comprised of
background data and HRV parameters from the population of patients,
was then subjected to statistical discriminatory analysis.
Statistical discriminatory analysis is used to determine one or
more discriminant equations wherein each such equation
discriminates between, for example, patients with abnormal
autonomic function and patients with normal autonomic function.
Doing so indicates whether a pattern indicative of autonomic
dysfunction could be found for similarly situated individuals.
[0087] Discriminant function analysis is a statistical tool used to
determine which variables discriminate between two or more
naturally occurring groups. For example, the analysis can be used
to investigate which patient information and autonomic test
parameters discriminate between individuals with autonomic
dysfunction, individuals without autonomic dysfunction and
borderline individuals that lie between these classifications.
Discriminant analysis can then be used to determine which
variable(s) are the best predictors of autonomic dysfunction. In a
stepwise discriminant function analysis, such as the one used in
the present example, a model of discrimination is built
step-by-step. Specifically, at each step, variables are reviewed
and evaluated to determine which one will contribute most to the
discrimination between groups of patients. If such a contribution
is made, that variable will then be included in the later analysis
and the process starts again until all variables have been
examined. The statistical methods incorporated in this example are
known to those of ordinary skill in the art. In addition, the
particular statistical analysis employed in the invention need not
be the exact analysis described herein. Those of ordinary skill in
the art will readily realize that other statistical methodologies
may be employed to identify patterns within the data set.
[0088] Keeping with the present example, twenty-one HRV parameters,
derived from three HRV tests, were gathered for all 128 patients.
This data is provided in FIGS. 6A-I. These test results were
processed with a standard forward stepwise linear discriminant
analysis. The Statistica.TM. 5.0 software package was used to
provide this analysis, with the following parameters set for the
method: Tolerance=0.010, F to enter=1.00, F to remove=0.00 and
Number of steps=21 (i.e., the number of parameters to be analyzed).
F is essentially computed as the ratio of the between-groups
variance in the data over the pooled (average) within-group
variance. If the between-group variance is significantly larger,
then there must be significant differences between means. The
stepwise procedure is guided by the respective "F to enter" and "F
to remove" values. The F value for a variable indicates its
statistical significance in the discrimination between groups. In
other words, it is a measure of the extent to which a variable
makes a unique contribution to the prediction of group membership.
Statistica.TM. software is available from StatSoft, Inc., 2300 East
14th Street, Tulsa, Okla. 74104. The above-identified values are
provided as examples only and may be modified by those of ordinary
skill in the art in accordance with their statistical analysis
design choices.
[0089] The discriminant analysis derived (i) a discriminant
equation that (ii) determined 8 of the 21 parameters were
statistically significant. The data for these 8 parameters, a
subset of data presented in FIGS. 6A-I, is presented in FIGS.
14A-C. A focus on 8 of the 21 parameters demonstrated a pattern
that significantly separated patients with autonomic dysfunction
from those without autonomic dysfunction. The discriminant analysis
indicated the other 13 parameters were not statistically relevant
in discriminating between patients afflicted with autonomic
neuropathy due to diabetes and those patients with normal autonomic
function. The significant parameters for the 5-min Resting HRV test
were RMS-SD and TP. The significant parameters for the Slow
Metronomic Breathing test were E/I Ratio, SD and NNmin SB. Finally,
the significant parameters for the Orthostatic test were 30:15
Ratio, NNmin Standing and NNmax Standing. A description of these
parameters was set out above.
[0090] The newly derived discriminant equation is as follows:
Y=(21.7134*E/I
Ratio)+(0.0936*SD)-(0.0628*RMS-SD)+(0.0008*TP)+(3.7881*30:1- 5
Ratio)-(0.0020*NNmin SB)-(0.0100*NNmin Standing)+(0.0056*NNmax
Standing)-39.6343.
[0091] Essentially, this equation was derived so that, when the 8
relevant factors are input into the equation, any resultant Y value
that is positive will be indicative of a patient with normal
autonomic function. Any resultant Y value that is negative will be
indicative of a patient with autonomic neuropathy due to diabetes.
Those variables with the largest coefficients are the ones that
contribute most to the prediction of autonomic dysfunction. Thus,
in this example, the E/I ratio contributes most to the prediction
because its coefficient is larger than the other coefficients.
[0092] While one discriminant equation has been identified in this
example, an embodiment of the invention concerns finding one or
more such equations. For example, a second equation could be
derived from the same data representative of the 21 HRV parameters
recorded for the above example. The first discriminant equation
discriminated between patients of a population that had a first
autonomic state, such as diabetes and autonomic neuropathy, and
other patients in the same population that had a second autonomic
state, such as no autonomic neuropathy. A second discriminant
equation might distinguish between patients with a first autonomic
state, such as hypertension and autonomic neuropathy, and other
patients with a second autonomic state, such as normal autonomic
function and no hypertension.
[0093] Those additional equations may continue to be derived as the
normative databases receive more background information and test
results. Using the new data, the invention could determine an
equation for discriminating between those with both coronary artery
disease (CAD) and diabetes and those that have neither condition.
In addition, the invention could determine another equation for
discriminating between those individuals with CAD, and associated
autonomic dysfunction, and those without autonomic dysfunction.
Also, the invention could determine an equation for discriminating
between individuals with CAD, who would have a first state of
autonomic function indicative of CAD, and those with diabetes, who
would have second state of autonomic function indicative of
diabetes. The multiple equations, possibly derived from multiple
HRV parameters taken from multiple HRV tests, provide for more
accurate autonomic assessment of patients than was ever possible
with prior art methods that failed to consider such discriminant
equations. In short, the multiple equations allow for like
individuals, such as Caucasian, 30-year old males, to have their
HRV test results compared against other Caucasian, 30-year old
males. Multiple equations may allow that same Caucasian, 30-year
old male to have his results, using one equation, compared against
30-year old, Caucasian males with hypertension and, using a second
equation, against 30-year old, Caucasian males with diabetes. In
doing so, the patient's autonomic function is assessed in a more
accurate and precise manner than would be the case with prior art
methodologies.
[0094] Returning to the example with 128 patients, after a
discriminant equation was derived, the 128 patients' test data were
entered into the equation to calculate the outcome, or root, of the
discriminant function. The outcome values are presented in FIG.
7A-7D. These outcomes may be identified as an autonomic ranking or
autonomic dysfunction rank (ADR).
[0095] In one embodiment of the invention, when the ADR is greater
than 0, the patient is considered healthy. When ADR is equal to,
for example, 0, the patient is still healthy but could be
considered "borderline" for autonomic dysfunction. As an ADR grows
negative, a more severe autonomic dysfunction is indicated.
Autonomic pathophysiology indicates there is a gradual transition,
through a "borderline" phase, from a healthy condition to a
pathological one. Taking this approach, a "borderline zone" may be
defined, for example, as plus/minus 5% of the variance of the
discriminant function derived from the entire set of 128 patients.
Therefore, if ADRmin=-12.1825 and ADRmax=11.3844, then R=23.5669
and the borderline zone will range from -1.1784 to +1.1784.
[0096] 9. Store Discriminant Equation
[0097] The newly derived equation should be added to a database,
step 135, along with any previously derived and still valid
equations, for use with future patients. In one embodiment of the
invention, the process is continued until a statistically
significant number of patients have been examined and one or more
discriminant equations have been derived. As an example of
achieving a statistically significant data set, analyzing patterns
among patient gender, fourteen different categories of age, five
categories of race, and two categories of health (e.g., those with
and without clinically evident autonomic dysfunction) may require
12,500 patients assuming each unique combination of variables
should have about 44 data points recorded.
[0098] 12,500 patients were pursued in the present example for at
least the following reasons. In discriminant analysis, the number
of observations for a group that will be studied should be higher
than the total number of parameters that will be tested for that
group. Therefore, using all four previously described HRV tests
will produce 22 HRV parameters. Consequently, more than 22
observations should be made for each group that will be studied. To
be conservative, 44 data points were gathered, which doubles the
required minimum number of observations (22). Regarding the number
of groups to be studied, two patient genders, fourteen different
categories of age, five categories of race, and two categories of
health result in 280 different groups or types of patients that
were to be studied. 280 groups multiplied by 44 data points per
group equates to 12,500 tests that should be considered. While a
specific example of what constitutes a statistically relevant
population has been addressed herein, a determination of when a
statistically valid amount of data has been collected is well known
to those of ordinary skill in the art and may vary from that
described above.
[0099] 10. Choose Applicable Discriminant Equation for New
Patient
[0100] Moving back to step 120 in FIG. 1, once a normative data set
has been created from a statistically significant population of
patients, new patients may be evaluated in relation to the norms
found within the population data set. In one embodiment of the
invention, the new patient is subjected to the Metronomic Breathing
test, Valsalva test, Orthostatic test and Short-Term Resting HRV to
produce ECG data in step 110, after first having background data
taken in step 105. The ECG data is then measured to obtain HRV
parameters in step 115. One may choose to use multiple tests
because an autonomic abnormality may manifest itself in, for
example, the Valsalva test but not the Orthostatic test. Patients
with specific severe cardiac conditions, however, may only be
capable of Short-Term Resting HRV testing due to the patient's
elevated risk for abnormal cardiac events.
[0101] In step 145, a new patient that is, for example, a 35 year
old, Caucasian, non-smoking man with clinically evident signs of
autonomic dysfunction caused by diabetes (Patient 1) is evaluated
against the discriminant equation derived earlier. In addition, a
31 year old, Caucasian, non-smoking man who is apparently healthy
(Patient 2) is also evaluated against the above discriminant
equation. However, the above discriminant equation may not be
selected for a 30 year old, Hispanic, smoking woman with clinically
evident signs of autonomic dysfunction caused by CAD because the
above equation is based on data from 30-35 year old Caucasian,
non-smoking men. Still, an investigator may choose to evaluate the
exemplar Hispanic woman against all known discriminant equations,
including the one that is the subject of the present example,
associated with individuals aged 30 to 35 years. In contrast, the
investigator may choose to compare the exemplar Hispanic woman only
with other smoking, 30-year old Hispanic women. Therefore, in one
embodiment of the invention, one or more discriminant equations are
selected in response to the background data from the new patient.
This selection may be performed automatically by the invention or
manually by the clinician.
[0102] 11. Generate Autonomic Ranking
[0103] Using Patients 1 and 2 as examples, the patients may be
subjected to, for example, the 5-min resting HRV test, Slow
Metronomic breathing test and Orthostatic test, producing HRV data
as shown in FIG. 8. These results should contain all eight
parameters values called for by the previously derived exemplar
discriminant equation. In step 150, the eight HRV parameters are
input into the selected discriminant equation and processed to
produce autonomic rankings, as seen in step 155, that are
indicative of the patient's autonomic function, as follows:
[0104] Patient 1:
(21.7134*1.1075)+(0.0936*31.48)-(0.0628*27.51)+(0.0008*63.64)+(3.7881*1.08-
3)-(0.0020*572)-(0.010*372)+(0.0056*592)-39.6343=-11.828(ADR)
[0105] Patient 2:
(21.7134*1.3966)+(0.0936*110.76)-(0.0628*52.63)+(0.0008*1020.37)+(3.7881*1-
.349)(-0.0020*748)-(0.0100*620)+(0.0056*936)-39.6343=1.1659(ADR)
[0106] 12. Present Autonomic Rankings to Clinician
[0107] Although the discriminant function produces a positive
autonomic ranking of 1.1659 for Patient 2, the value falls into the
borderline zone, instead of normal or abnormal zones, as
illustrated in step 160 and by Point 910 in FIG. 9. Even though
there is no clinical manifestation of autonomic dysfunction, the
patient will be considered "borderline." Thus, while Patient 2
showed no clinically evident signs of autonomic dysfunction, he is
clearly at risk for developing such dysfunction. The autonomic
ranking may be classified as being indicative of a propensity for
Patient 2 to develop a specific illness such as diabetes.
Considering many individuals have autonomic dysfunction that does
not manifest itself clinically, the results for Patient 2 are
critical. Patient 2 can now work with his clinician to manage his
lifestyle towards autonomic improvement. Furthermore, the effects
of any prescribed regimen can be evaluated when subsequent test
results are compared to the first autonomic ranking. Concerning the
exact display illustrated in FIG. 9, one of ordinary skill in the
art will appreciate that the autonomic ranking may be presented to
the clinician or patient in many different ways and that the
various display embodiments are within the scope of the present
invention. For example, an exemplar display may be three
dimensional with clouds or sectors that identify different scores
that are indicative of different maladies. The patient's ADR could
then be plotted in view of these clouds or sectors. The patient may
then readily realize his proximity to different maladies. The
clinician may then order specific tests for maladies that the
patient is at risk for contracting. The clinician may also make
referrals to, for example, an oncologist for a patient who is
borderline for pancreatic cancer. In one embodiment of the
invention, the referral to other doctors or necessity for other
tests may be performed automatically by the invention.
[0108] Returning the above example, in contrast to Patient 2,
Patient 1 has a very negative autonomic ranking of 11.828. This
ranking confirms the clinical assessment of autonomic dysfunction.
Now that Patient 1 has an objective ranking to corroborate his
clinical assessment, he may more easily monitor the effectiveness
of therapy or a change a lifestyle upon his autonomic function by
comparing his future autonomic rakings with the present ranking.
Along these same lines, pharmaceutical companies may easily track
the efficacy of certain drugs by using these HRV results.
[0109] 13. Amend Normative Database
[0110] After step 155 and, for example, step 160, some or all of
Patient 2's background information, HRV data and ECG data may be
added to the normative database where discriminant analysis may
again be performed. This step allows for the database to consider
additional data that is of critical import for HRV analysis,
especially considering the possible lack of normative values
addressing, for example, the relationship between HRV and CAD or
the relationship between race, smoking status, pancreatic cancer
and HRV.
[0111] In one embodiment of the invention, a patient's autonomic
ranking for condition 1, obtained in year 1, may later be compared
with the patient's autonomic ranking for condition 1, obtained in
year 2. The normative values may be archived on the test evaluation
server 415 as the normative database grows to ensure a patient's
autonomic test results in year 2 can be compared against normative
values from year 1. Similarly, a patient's test results from year 1
can be archived so they can later be compared with normative values
from year 2, thereby allowing a health care provider to more fully
take advantage of updated normative values as they develop. In this
way, the invention could periodically test prior test results
against updated normative values to determine if a patient's
autonomic ranking should be revised in light of improved normative
values and/or newly derived discriminant equations.
[0112] Thus, an alternative embodiment of the invention entails
ongoing health care for the patient. As HRV testing becomes more
popular with clinicians, normative databases will be more populated
with data. As these normative databases grow, new discriminant
equations will be derived or determined and previously determined
discriminant equations may be modified.
[0113] Returning to the above example concerning Patient 1, a
clinician may continue to monitor Patient 1 over time. For example,
the clinician may input Patient 1's HRV test parameters from
Patient 1's initial HRV test into a newly determined discriminant
function, derived from background data and physiologic data from a
second population of patients, to produce an alternative, or new,
autonomic ranking. The alternative ranking may indicate that
Patient 1's initial HRV parameters, which produced a "borderline"
ranking, may now indicate an "abnormal" ranking based on updated
normative values. The invention could then alert the clinician to
contact Patient 1 to reassess any prescribed therapy or to conduct
further testing, such as a test for diabetes in Patient 1's case.
In one embodiment of the invention, the patient's various autonomic
rankings are displayed in proximity to one another so the patient
can readily appreciate how his autonomic function has changed over
time.
[0114] In yet another alternative embodiment, the clinician may
collect new physiologic data, such as ECG readings and/or the
resultant HRV parameters, from Patient 1. The clinician may then
input the additional physiologic data from Patient 1 into the
initially derived discriminant function to produce a second
autonomic ranking, wherein the second autonomic ranking is
indicative of Patient 1's alternative autonomic function. The two
autonomic rankings could then be compared with one another to
determine how Patient 1's autonomic function is progressing. The
embodiment of the invention could indicate to the clinician that
there has been a change between the two autonomic rankings that
exceeds a predetermined amount. If the change was for the worse,
the clinician could then order needed tests, such as a test for
diabetes in Patient 1's case. As an additional embodiment, Patient
1's new physiologic data could be input into newly derived
discriminant equations to provide up to date autonomic function
results. The two autonomic rankings could be displayed in proximity
to one another thus facilitating comparisons between the two
rankings.
[0115] As a normative database grows, the discriminant equations
will become more discriminating and be able to connect autonomic
rankings to indicators of whether a patient suffers from, or is at
a heightened risk for contracting, a specified illness, such as,
for example, diabetes, coronary artery disease, anxiety,
depression, sudden cardiac death, myocardial infarction and
hypertension. Other HRV-related maladies are described further in
the Task Force Report for Heart Rate Variability: Standards of
Measurement, Physiologic Interpretation, and Clinical Use,
Circulation Vol. 93. No 5, 1996, which is incorporated herein by
reference. Those of ordinary skill in the art will readily
appreciate that the methods and apparatuses described herein may be
used to identify other maladies not specifically mentioned or
described and that identification of such maladies is included
within the scope of the invention.
[0116] The end result of steps 100 through 160 is that a patient
with certain characteristics can be compared with like individuals
in a very specific and accurate fashion. Thus, the normative
database, discriminatory equations, autonomic test parameters and
background patient data will allow a forty year old, Caucasian man
with pancreatic cancer and a history of heavy smoking to have his
autonomic data compared with like individuals to determine his
predisposition for maladies found within those like
individuals.
[0117] One of ordinary skill in the art will appreciate that there
are a number of other alternative embodiments available which allow
for the identification of autonomic dysfunction patterns and for
the application of the identified patterns to new patient data, and
that such embodiments are within the scope of the present
invention. In addition, the various embodiments of the invention
are not directed solely towards traditional HRV testing. For
example, certain embodiments of the invention may be used for HRV
and spirometric testing of non-human animals, such as horses,
cattle, dogs and cats, are within the scope of the invention. The
invention may be used in other non-traditional settings. For
example, embodiments of the invention may be used for battlefield
or civilian assessment of biological warfare efforts. HRV testing
may evaluate whether an individual has been exposed to a toxin or a
chemical or biological agent. The effects of such agents may have
immediate or delayed expression in the afflicted individual. This
expression may manifest itself by a decrease in autonomic function.
The various embodiments of the invention may be used to detect such
a decrease in autonomic function. Embodiments of the invention may
then monitor improvements in the autonomic function as well. In
short, one of ordinary skill in the art will appreciate that
application of the invention is not limited to traditional HRV
testing and that non-traditional uses of the invention are
encompassed with in the scope of the invention.
[0118] 14. Application Service Provider Model and Other Alternative
Embodiments
[0119] An alternative embodiment of the invention concerns an
Application Service Provider ("ASP") model. Generally, in an ASP
model, a business offers software application capabilities, from
centralized data centers via wide area networks, including the
Internet, to remote users. For users, an ASP is a kind of
outsourcer wherein users are not required to buy and own software
applications accessed from the ASP. For example, Microsoft may
provide to users access to the most current versions of
applications such as Microsoft Word and Microsoft Excel over the
Internet from a web server running such applications. Microsoft may
then charge the users on a per use basis. Generally, in the long
run such programs will be more up to date than the off the shelf
versions available for purchase by users. Another advantage of the
ASP model is that users can run available applications with a thin
client, also known as NetPCs or NetStations. The ASP will provide
such thin clients with access to applications such as word
processing and spreadsheet applications, will store a user's
personal files, and provide all necessary processing power for
running such applications.
[0120] Referring to FIG. 10, there is illustrated a block diagram
of an ASP system configured in accordance with an embodiment of the
present invention. A user at their client machine with a browser
1002 loaded thereon has access to the Internet 1003. Please note
that the present invention should not be limited to the Internet,
but is also applicable to any local area network, wide area
network, or global communications network. The user will type in a
URL into their browser 1002 to access the web server 1001 of the
ASP they desire to contact. Once the user has accessed the ASP, the
user will then be able to select an application 1004 being run on
the ASP's web server 1001. Such an application 1004 could be a
spreadsheet program, such as Microsoft Excel or an application for
measuring autonomic function. In such a process, instead of the
user having to purchase the software for the application and load
it onto their client machine, the user may use their browser 1002
to access all of the features of the application 1004 over the
Internet 1003 through the web server 1001 of the ASP. Typically,
GUIs (graphical user interface) of the application will be sent to
the user for viewing on their browser 1002, and the user will
insert data, for example a letter or memo they wish to create in a
word processing application, which data will be uploaded from the
browser 1002 to the application 1004 running on the web server 1001
of the ASP. The process for performing this function is well known
in the art.
[0121] For example, in FIG. 11, once a clinician (MD) has a
candidate (patient) (step 1100) for HRV testing, in step 1105, he
may use a browser 1002, located on his workstation 410 or testing
unit 405, to access the web server 1001 (i.e., test evaluation
server 415) and application 1004. In step 1110, if the clinician
has an account with the ASP, he may log in to the application 1004.
In step 1120, if no such account exists, he may contact the ASP to
open an account. Once the application 1004 confirms the clinician
has a viable account in step 1115, the application 1004 may display
a list of available tests in step 1125. These tests may be packaged
in any number of ways. The display may, using a pull down menu, as
an example, offer the clinician the option of selecting one
Valsalva test and one Metronomic test. The display may offer,
however, tests in packages, where purchasing one test package
amounts to purchasing one Metronomic test, one Valsalva test, one
resting HRV test and one Orthostatic test.
[0122] Each test may have a unique identifier assigned to it. This
unique identifier may be used for billing purposes by the
application 1004. For example, the unique identifier may be
associated with receivables such as spirometric mouthpieces. When
the clinician purchases a Metronomic test, the ASP may also bill
the clinician for the mouthpiece that is required for use with the
test. The mouthpieces may have been shipped, in bulk, to the
clinician at an earlier time. This may further aid in other billing
concepts. For example, a clinician could be billed for 10
spirometric mouthpieces after 10 HRV tests have been purchased. A
unique identifier may also be assigned to the patient. This will
facilitate tracking the patient's medical records because the
identifier would be stored or coupled to the server 1001. For
example, while a patient may discontinue seeing a particular
clinician, the patient would not have to transfer his files to the
office of another clinician. The second clinician could access the
patient's medical files using the patient's unique identifier, a
browser 1002, the internet 1003, the application 1004 and the
server 1001. The unique identifier of the patient may be linked to
the unique identifier associated with the test. The patient
information could be protected in any number of ways, including
using the Secure Sockets Layer (SSL) technology SSL and other
encryption/security methods described earlier.
[0123] In step 1130 of FIG. 11, the clinician chooses a test. The
application 1004, in step 1135, then may prompt, using a dialog box
for example, the user to enter background data from a patient as
well as physiologic data from a patient into the application. This
step may be implemented in an automatic fashion whereby, upon
docking the testing unit 405 to the doctor's workstation 410, the
background and physiologic data may be automatically uploaded to
the doctor's workstation 410. The application 1004 may then
interrogate the doctor's workstation 405 after the clinician
replies affirmatively to the application's prompt in step 1135. Of
course the doctor's workstation 410 may be omitted and the testing
unit 405 may interact directly with the application 1004.
[0124] After the background and physiologic data has been uploaded
to the application 1004, the application may choose, in step 1140,
one or more discriminant equations that are applicable to the
transferred data. For example, the application 1004 may have
previously derived two discriminatory equations from a population
of data. One equation may identify a pattern that discriminates
between a population of individuals with normal autonomic function
and individuals with abnormal autonomic function. Another equation
may discriminate between 30 year Asian Indian men with hypertension
and 30-year-old Asian Indian men without hypertension. If the
clinician sends data to the application 1004 concerning a
30-year-old Asian Indian man, the application 1004 may choose both
equations for application 1004 to the new patient data. If the
clinician transmits data from a 50-year-old Caucasian woman, the
application may only select the equation that discriminates broadly
between individuals with normal autonomic function and those
without normal autonomic function.
[0125] In step 1145, the application 1004 applies the new patient
data to the selected equations and generates one or more ADRs or
autonomic rankings. In step 1150, the application 1004 may
incorporate the autonomic ranking into a report that may be saved
on the server 1001, in step 1155, and/or be sent over the internet
1003 to the doctor's workstation 410 or testing unit 405 in step
1160. Then, considering the test is complete and a report has been
sent to the clinician, in step 1165, the application 1004 may
decrease the number of available credits for studies the clinician
has purchased by one. The application may then prompt the clinician
to order additional tests if less than a predetermined number of
tests are then available to the clinician. The process may then end
in step 1170.
[0126] The physiologic data that may be sent in step 1135 may be,
for example, raw ECG data or processed ECG data. Thus, the ECG data
from a patient's HRV study may be sent in the form it was collected
as to the application 1004. The ECG data may be, however, sent to
the application 1004 only after artifact and abnormal heartbeats
have been removed using the processes described above. Yet again,
the ECG data may be analyzed locally thus deriving physiologic data
such as HRV parameters like SDNN or RMS-SD. These HRV parameters
may be sent alone to the application 1004 or may be derived by the
application 1004 from ECG data previously sent to the application
1004. In order to create more comprehensive and increasingly
accurate normative databases, newly acquired raw ECG data and/or
processed ECG data and/or physiologic parameters or values may all
be sent to the server 1001 for further analysis at, for example, a
later time.
[0127] The application 1004 is not limited to the HRV sector. For
example, the application 1004 may be used with other medical
testing, such as in general spirometry testing. A clinician may
choose a test from the application 1004. Upon transferring
physiological data to the application 1004, the application 1004
may analyze the data and return results to the clinician or provide
other services that allow the clinician to analyze the data. Upon
purchasing a test from the application 1004, the application 1004
may bill the clinician for a spirometric mouthtube if such a device
is needed to perform the test. Various blood tests could be used
with the model as well. Upon purchasing a test from the application
1004, the clinician could be billed for a testing kit that might
include a syringe, blood tube, bandages and other related
equipment.
[0128] In addition, ECG analysis services are within the scope of
the invention. Rather than providing expensive ECG analysis
technologies within a clinician's office, physiological data (e.g.,
ECG data) could be transmitted to the application 1004 whereby the
ECG data is analyzed and test results are returned to the
clinician. The process could be repeated for that patient at later
dates. Then, the application 1004 could display the various ECG
tests to the clinician, using a browser 1002, to quickly illustrate
differences in the patient's ECG recordings over time. This aspect
of the invention would be of paramount importance by an emergency
room clinician that must quickly access a patient's medical records
without waiting for files to be forwarded to the emergency room.
The clinician could use the internet to access ECG files located on
the server 1001. Using a unique identifier associated with each ECG
test, the application 1004 could bill the clinician for, as an
example, ECG patches and the like. ECG patches are but one example
of consumable items that may be billed to an account. A person of
ordinary skill in the art will appreciate that use of other items
or tools, such as a device that may be sterilized and used again,
may also be billed to an account.
[0129] While various examples have been described above for using
the ASP model, one of ordinary skill in the art will realize that
any number of medical services may be provided with the ASP model
and that those services are encompassed within the scope of the
present invention.
[0130] Referring to FIG. 12, an example is shown of a data
processing system 1200, which may be used for implementing any of
the aforementioned embodiments of the invention, including one or
more of the client machines 1002 and the web server 1001. The
system has a central processing unit (CPU) 1210, which is coupled
to various other components by system bus 1212. Read only memory
("ROM") 1216 is coupled to the system bus 1212 and includes a basic
input/output system ("BIOS") that controls certain basic functions
of the data processing system 1200. Random access memory ("RAM")
1214, I/O adapter 1218, and communications adapter 1234 are also
coupled to the system bus 1212. I/O adapter 1218 may be a small
computer system interface ("SCSI") adapter that communicates with a
disk storage device 1220. Communications adapter 1234 interconnects
bus 1212 with an outside network enabling the data processing
system to communicate with other such systems. Input/Output devices
are also connected to system bus 1212 via user interface adapter
1222 and display adapter 1236. Keyboard 1224 and mouse 1226 are
interconnected to bus 1212 via user interface adapter 1222. Display
adapter 1236 connects display monitor 1238 to system bus 1212. In
this manner, a user is capable of inputting to the system
throughout the keyboard 1224 or mouse 1226 and receiving output
from the system via display 1238.
[0131] Embodiments of the invention may be implemented as a
computer system programmed to execute the method or methods
described herein, and as a computer program product. According to
the computer system implementation, sets of instructions for
executing the method or methods are resident in the random access
memory 1214 of one or more computer systems configured generally as
described above. Those of ordinary skill in the art will appreciate
that the computer program product or software program instructions
are capable of being distributed as one or more program products,
in a variety of forms. Processor 1210, from either a client machine
1002 and/or server computer 1001, may execute one or more of the
computer program products stored in memory 1214. Client computer
1002 and server computer 1001 may be individually programmed to
collectively execute the process or processes of the invention
described herein. Until required by the computer system, the set of
instructions may be stored as a computer program product in another
computer memory, for example, in disk drive 1220 (which may include
a removable memory such as an optical disk or floppy disk for
eventual use in the disk drive 1220). Further, the computer program
product can also be stored at another computer and transmitted when
desired to the user's workstation by a network or by an external
network such as the Internet. One of ordinary skill in the art
would appreciate that the physical storage of the sets of
instructions physically changes the medium upon which it is stored
so that the medium carries computer readable information. The
change may be electrical, magnetic, chemical, biological, or some
other physical change. While it is convenient to describe the
invention in terms of instructions, symbols, characters, or the
like, the reader should remember that all of these and similar
terms should be associated with the appropriate physical
elements.
[0132] As yet another embodiment of the invention, an embodiment of
the invention entails a networked data processing environment. The
data processing environment is an arrangement, as previously
described, of one or more client computers 1002 and server
computers 1001 (generally "hosts") connected to each other by a
network 1003, for example, the Internet. One of ordinary skill in
the art will recognize that, for example, WiFi, satellite
communication and the like may constitute a network. Users access
information and interface with network 1003 and server computer
1001 through a client computer 1002.
[0133] Note that the invention may describe terms such as
comparing, validating, selecting, identifying, or other terms that
could be associated with a human operator. However, for at least a
number of the operations described herein, which form part of at
least one of the embodiments, no action by a human operator is
required. The operations described are, in large part, machine
operations processing electrical signals to generate other
electrical signals.
[0134] Additionally, the foregoing detailed description has set
forth various embodiments of the present invention via the use of
block diagrams, flowcharts, and/or examples. It will be understood
by those of ordinary skill in the art that each block diagram
component, flowchart step, and operations and/or components
illustrated by the use of examples can be implemented, individually
and/or collectively, by a wide range of hardware, software,
firmware, or any combination thereof. The present invention may be
implemented as those of ordinary skill in the art will recognize,
in whole or in part, in standard Integrated Circuits, Application
Specific Integrated Circuits (ASICs), as a computer program running
on a general-purpose machine having appropriate hardware, such as
one or more computers, as firmware, or as virtually any combination
thereof and that designing the circuitry and/or writing the code
for the software or firmware would be well within the skill of one
of ordinary skill in the art, in view of this disclosure. It will
also be understood that certain of the above-described structures,
functions and operations of the above-described embodiments are not
necessary to practice the present invention and are included in the
description simply for completeness of an example embodiment or
embodiments. In addition, it will be understood that specific
structures, functions and operations set forth in the
above-referenced patents and publications can be practiced in
conjunction with the present invention, but they are not essential
to its practice. It is therefore to be understood that within the
scope of the claims, the invention may be practiced otherwise than
as specifically described without actually departing from the
spirit and scope of the present invention. Finally, all patents,
publications and standards referenced herein are hereby
incorporated by reference.
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