U.S. patent application number 14/690986 was filed with the patent office on 2015-08-13 for method and apparatus for detection of atrial fibrillation.
The applicant listed for this patent is The Research Foundation for The State University of New York. Invention is credited to Ki Chon, Ernst RAEDER.
Application Number | 20150223711 14/690986 |
Document ID | / |
Family ID | 40305308 |
Filed Date | 2015-08-13 |
United States Patent
Application |
20150223711 |
Kind Code |
A1 |
RAEDER; Ernst ; et
al. |
August 13, 2015 |
METHOD AND APPARATUS FOR DETECTION OF ATRIAL FIBRILLATION
Abstract
Disclosed is a method for Atrial Fibrillation detection that
includes monitoring consecutive patient heartbeats, analyzing heart
beat intervals from the monitored heartbeats, performing a
calculation of the randomness the heart beat intervals, performing
a calculation of the variability of consecutive heart beat
intervals of the heart beat intervals, and performing a calculation
of the complexity of the heart beat intervals.
Inventors: |
RAEDER; Ernst; (Setauket,
NY) ; Chon; Ki; (Stony Brook, NY) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The Research Foundation for The State University of New
York |
Albany |
NY |
US |
|
|
Family ID: |
40305308 |
Appl. No.: |
14/690986 |
Filed: |
April 20, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13839341 |
Mar 15, 2013 |
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14690986 |
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12671847 |
Feb 2, 2010 |
8417326 |
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PCT/US2008/072099 |
Aug 4, 2008 |
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13839341 |
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61084389 |
Jul 29, 2008 |
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60953508 |
Aug 2, 2007 |
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Current U.S.
Class: |
600/508 |
Current CPC
Class: |
A61B 8/5223 20130101;
A61B 5/02405 20130101; A61B 5/0456 20130101; A61B 5/04012 20130101;
A61B 5/0245 20130101; A61B 5/046 20130101; A61B 8/5269 20130101;
A61B 8/02 20130101; A61B 5/7203 20130101 |
International
Class: |
A61B 5/046 20060101
A61B005/046; A61B 8/02 20060101 A61B008/02; A61B 5/00 20060101
A61B005/00; A61B 5/04 20060101 A61B005/04; A61B 8/08 20060101
A61B008/08; A61B 5/0456 20060101 A61B005/0456; A61B 5/024 20060101
A61B005/024; A61B 5/0245 20060101 A61B005/0245 |
Claims
1. An Atrial Fibrillation detection method comprising: monitoring
consecutive patient heartbeats; analyzing a plurality of heart beat
intervals from the monitored heartbeats; performing a calculation
of randomness the plurality of heart beat intervals; performing a
calculation of variability of consecutive heart beat intervals of
the plurality of heart beat intervals; and performing a calculation
of complexity of the plurality of heart beat intervals.
2. The method of claim 1, wherein the calculation of the randomness
comprises a Turning Points Ratio analysis.
3. The method of claim 1, wherein the calculation of the
variability comprises deriving a root mean square of the
consecutive heart beat intervals of the plurality of heart beat
intervals.
4. The method of claim 1, wherein the calculation of the complexity
comprises calculating Shannon Entropy of the plurality of heart
beat intervals.
5. The method of claim 1, wherein an ectopic beat is identified by
detecting a short-long sequence in an interbeat interval of the
monitored heartbeats.
6. The method of claim 5, wherein the signature short-long sequence
comprises an ectopic coupling interval followed by a compensatory
pause between RR intervals.
7. The method of claim 6, wherein ectopic beats and an associated
compensatory pause are excluded from the obtained consecutive
patient heartbeats to provide a clean time series.
8. The method of claim 7, wherein the compensatory pause is longer
than the ectopic coupling interval.
Description
PRIORITY
[0001] This application is a continuation application of U.S.
application Ser. No. 13/839,341 filed Mar. 15, 2013, which is a
continuation application of U.S. application Ser. No. 12/671,847
filed Feb. 2, 2010, and claims priority to PCT/US2008/072099 filed
Aug. 4, 2008, to U.S. Provisional Application No. 61/084,389, filed
Jul. 29, 2008, and to U.S. Provisional Application No. 60/953,508,
filed Aug. 2, 2007, the contents of each of which is incorporated
herein by reference.
BACKGROUND OF THE INVENTION
[0002] The present invention applies an algorithm for detection of
Atrial Fibrillation (AF), which is one of the most common cardiac
arrhythmias, afflicting approximately 2-3 million Americans. The
incidence and prevalence of AF increase with age. With the graying
of the baby boomers, it is estimated that 12-16 million individuals
may be affected by 2050 and be at risk of significant mortality and
morbidity from this arrhythmia.
[0003] AF has a prevalence of 17.8% and an incidence of 20.7/1,000
patient years in individuals older than 85. At age 55, the lifetime
risk of developing AF is approximately 23%. AF is an independent
risk factor for death (relative risk in men is 1.5 and in women
1.9). Furthermore, AF is a major cause of ischemic stroke, the
impact of which increases with age and reaches 23.5% in patients
older than 80. Accurate detection of AF is crucial since effective
treatment modalities such as chronic anticoagulation and
antiarrhythmic therapy, as well as radiofrequency ablation, are
available but carry risks of serious complications. Despite the
ubiquity of the arrhythmia, its diagnosis rests largely on the
presence of symptoms and on serendipity. Unfortunately, since
patients are often unaware of their irregular pulse, the diagnosis
is often only established during a fortuitous doctor visit. If
episodes of AF occur interspersed with normal sinus rhythm, the
diagnosis presents an even greater challenge.
[0004] When AF is suspected, ambulatory monitoring can be performed
in an attempt to document the arrhythmia. However, this approach is
time consuming and not cost-effective for screening asymptomatic
populations. Limitations of currently available technology include
electrocardiography (for less than 10 seconds) and long-term
monitoring. Ambulatory Holter monitoring is limited to no more than
48 hours and is cumbersome because it requires several leads
connecting to a device worn on the patient's waist. After
completion of the recording, the monitor is returned for data
analysis by a cardiologist. Accordingly, real-time monitoring is
not possible with conventional devices.
[0005] Conventional monitoring devices also include event monitors,
which are small devices carried by a patient for up to 30 days. The
patient will activate the event monitor upon when experiencing an
irregular heart beat. A cardiologist will subsequently analyze
recordings obtained by the event monitor.
[0006] For patients with very infrequent but potentially serious
rhythm disturbances, an implantable loop recorder can be used. The
implantable loop recorder continually records and overwrites the
electrocardiogram for more than one year. When patients experience
an event, they can freeze the recording and transmit the
information to a cardiologist.
[0007] Several companies presently offer ambulatory heart monitors
without AF detection capability. For example, CardioNet provides a
3-lead ECG monitor system which records and transmits data
wirelessly to a hand held PDA for subsequent modem or Internet
transmission. See, Rothman, et al., Diagnosis of Cardiac
Arrhythmias Journal of Cardiovascular Electrophysiology, Vol. 18,
No. 3, March 2007, U.S. Pat. No. 7,212,850 and U.S. Publ. No.
2006/0084881 of Korzinov et al.
[0008] Conventional systems also include wireless transmission of
ECG data, as discussed in U.S. Pat. No. 5,522,396, a 12-lead Holter
ECG system, as discussed in U.S. Pat. No. 6,690,967, and an event
recorder system, as discussed in U.S. Pat. No. 5,876,351.
[0009] An AfibAlert device monitors for AF during a 45-second
testing period. However, the AfibAlert device does not provide a
continuous or real-time detection and monitoring of the heart, and
therefore cannot alert if AF happens at any other time. In
addition, the cost of the AfibAlert device is relatively high for
wide acceptance by the general population. Furthermore, the 90-93%
accuracy of the AfibAlert device is below the accuracy of the
detection algorithm of the present invention.
[0010] A number of algorithms have been developed to detect AF.
Such conventional algorithms can be categorized based on P-wave
detection and an interbeat (RR) interval (RRI) variability (HRV).
Since there is no uniform depolarization of the atria during AF,
there is no discernible P-wave in the ECG. This fact has been
utilized in detection of AF by trying to identify whether the
P-wave is absent. However, in most cases the location of the P-wave
fiducial point is very difficult to find. Moreover, the P-wave may
be small enough to be corrupted by noise that is inherent in
surface measurements. The methods in the second category do not
require identification of the P-wave and are based on the
variability of RRI series. However, few algorithms in this category
show high predictive value for clinical application. A notable
exception is discussed by Duverney et al. in High Accuracy of
Automatic Detection of Atrial Fibrillation using Wavelet Transform
of Heart Rate Intervals, Pacing Clin Electrophysiol 25: 457-462,
2002, and by Tateno et al. in Automatic Detection of Atrial
Fibrillation using the Coefficient of Variation and Density
Histograms of RR and delta RR Intervals, Medical & Biological
Engineering & Computing 39: 664-671, 2001.
[0011] Duverney et al. use wavelet transform of an RRI time series
where the sensitivity and specificity was 96.1% and 92.6% for AF
beats, respectively, on a European database consisting of 15
subjects. Tateno et al. compare the density histogram of a test RRI
(and ARRI) segment with previously compiled standard density
histograms of RR (and ARR) segments during AF using the
Kolmogorov-Smirnov test, to report a sensitivity of 94.4% and
specificity of 97.2% for AF beats for the MIT BIH Atrial
Fibrillation database. However, the accuracy of the Tateno et al.
algorithm relies on the robustness of training data and that their
results were based on a limited database. However, in most clinical
applications, it may be difficult to obtain such large databases of
training data.
[0012] In view of a general consideration of AF as being a random
sequence of heart beat intervals with markedly increased
beat-to-beat variability, the present invention combines four
statistical techniques to exploit a Root Mean Square of Successive
RR interval Differences to quantify variability (RMSSD), a Turning
Points Ratio (TPR) to test for randomness of the time series, a
Shannon Entropy (SE) to characterize its complexity and a
autocorrelation (ACORR) index to characterize correlation between
the first two RR intervals. In contrast to the Tateno-Glass method,
the algorithm of the present invention does not require training
data. See, Lu S, Chon K H, and Raeder E, Automatic Real Time
Detection of Atrial Fibrillation, Heart Rhythm 4: S36 (2007).
[0013] The present invention provides a method and apparatus for
utilizing an algorithm that accurately detects, in a real-time
manner, the presence of AF utilizing piezoelectric or ECG signals.
The present invention also provides a portable blood pressure cuff,
for home monitoring.
SUMMARY OF THE INVENTION
[0014] The present invention provides an Atrial Fibrillation
detection method that includes monitoring consecutive patient
heartbeats, analyzing a plurality of heart beat intervals from the
monitored heartbeats, performing a calculation of randomness the
plurality of heart beat intervals, performing a calculation of
variability of consecutive heart beat intervals of the plurality of
heart beat intervals, and performing a calculation of complexity of
the plurality of heart beat intervals.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] The above and other objects, features and advantages of
certain exemplary embodiments of the present invention will be more
apparent from the following detailed description taken in
conjunction with the accompanying drawings, in which:
[0016] FIGS. 1(a)-(f) show threshold values for AF detection;
[0017] FIG. 2 shows results of a turning points analysis;
[0018] FIGS. 3(a)-(e) show an AF episode, including RMSSD, TPR,
Shannon Entropy and ACORR;
[0019] FIG. 4 shows a piezoelectric sensor incorporated in a blood
pressure cuff;
[0020] FIG. 5 provides a comparison of RR intervals obtained from a
commercial ECG device and PPV values obtained utilizing the
piezoelectric sensor of the present invention;
[0021] FIGS. 6(a)-(b) show an integrated wireless ECG device and
wireless ECG collection of the present invention; and
[0022] FIG. 7 is a flowchart showing operation of a preferred
embodiment of the present invention.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0023] The following detailed description of preferred embodiments
of the invention will be made in reference to the accompanying
drawings. In describing the invention, explanation about related
functions or constructions known in the art are omitted for the
sake of clearness in understanding the concept of the invention, to
avoid obscuring the invention with unnecessary detail.
[0024] A preferred embodiment of the present invention utilizes a
Turning Points Ratio (TPR) to determine whether an RR interval
sequence is random, for application of the TPR nonparametric
statistical test comparing each point in the time series to
neighboring points. Ectopic beats and associated compensatory pause
are excluded from the obtained consecutive patient heartbeats to
create a clean time series, i.e., devoid of premature beats,
unperturbed by ectopic beats.
[0025] FIG. 1(a) shows an original heart beat interval time series
from a section of file 5162 of a MIT BIH Atrial Fibrillation
database. FIGS. 1(b)-(e) show calculation of the RMSSD, TPR,
Shannon Entropy and ACORR, respectively, for the same segment. FIG.
1(f) shows final detection results based on whether the above
statistics cross respective thresholds that are shown in dashed
lines for FIGS. 1(b)-(c). In FIG. 1(a) a long-term recording is
shown with an episode of AF embedded in normal sinus rhythm in
which random behavior of AF is clearly observed. As shown in FIGS.
1(b) through (f), the combination of TPR, RMSSD, SE and ACORR
greatly enhances the accuracy of AF detection.
[0026] In a computer generated random time series, the probability
of an interval being surrounded by either two higher or two lower
intervals ("Turning Point") is equal to 2/3. Given three random
numbers a.sub.1,a.sub.2,a.sub.3 where
a.sub.1>a.sub.2>a.sub.3, there are six combinations to
generate a series. Among them,
(a.sub.1a.sub.3a.sub.2),(a.sub.2a.sub.3a.sub.1),(a.sub.2a.sub.1a.sub.3)
and (a.sub.3a.sub.1a.sub.2) include turning points while
(a.sub.1a.sub.2a.sub.3) and (a.sub.3a.sub.2a.sub.1) do not. Given a
random series of length n, the expected number of turning points
is
2 n - 4 3 , ##EQU00001##
and the standard deviation is
16 n - 29 90 . ##EQU00002##
Hence, the expected Turning Points Ratio (TPR) of a random series
is provided in Equation (1):
TPR = 2 n - 4 3 n .+-. 16 n - 29 90 ( 1 ) ##EQU00003##
[0027] Confidence limits of this ratio are defined to estimate
randomness boundaries in a time series. A series with ratios below
the lower 95% confidence interval exhibits periodicity (e.g. sinus
rhythm) whereas TPRs above the upper 95% confidence limit
approaching 1.0 are evidence of alternans where ultimately every
point is a turning point ("ABABAB" pattern).
[0028] FIG. 2 shows an analysis of one thousand (1000) random
numbers subjected to turning points analysis. As expected, panel
(a) shows the TPR of the random number sequence is .about.2/3. When
increasing levels of alternans are imposed, as shown in panels (b)
through (d), the TPR increases above the 95% confidence limit for
randomness until approaching unity.
[0029] In the present invention, a Root Mean Square of Successive
Differences is preferably performed as a second component of the
algorithm. In the present invention, beat-to-beat variability is
estimated by the root mean square of successive RR differences
(RMSSD). Since AF exhibits higher variability between adjacent RR
intervals than periodic rhythms such as sinus rhythm, the RMSSD is
expected to be higher. For a given segment a(i) of RR intervals of
some length l, the RMSSD is given by Equation (2):
RMSSD = 1 128 j j + l = 1 ( a ( j + 1 ) - a ( j ) ) 2 ( 2 )
##EQU00004##
[0030] A third component of the algorithm of the present invention
is Shannon Entropy (SE), which provides quantitative information
about the complexity of a signal. Complexity refers to the
difficulty in describing or understanding a signal. For example,
signals with discernible regular patterns are easier to describe
than signals with a higher degree of irregularity. The SE
quantifies how likely runs of patterns that exhibit regularity over
a certain duration of data also exhibit similar regular patterns
over the next incremental duration of data. For example, a random
white noise signal is expected to have the highest SE value (1.0)
whereas a simple sinusoidal signal will have a very low SE
(.about.0.2) value. Thus, the SE values of normal sinus rhythm and
AF can be expected to differ significantly.
[0031] Calculation of SE of the RR interval time series is
performed by first constructing a histogram of the segment
considered. The eight maximum and eight minimum RR values in the
segment are considered outliers and are removed from consideration.
The remaining RR intervals are sorted into equally spaced bins
whose limits are defined by the minimum and maximum RR interval
after removing outliers. To obtain a reasonably accurate measure of
the SE, at least 16 such bins are needed. Based on an ROC curve
analysis, the segment length for AF detection was set at 128
beats.
[0032] An estimation of probability is performed as a next step in
the calculation of SE, preferably by computing for each bin as the
number of beats in that bin divided by the total number of beats in
the segment (after removing outliers), for example see Equation
(3):
p ( i ) = No . of beatsinbin ( i ) Totalnumberofbeatsinthesegment =
No . of beatsinbin ( i ) 128 - 16 = No . of beatsinbin ( i ) 112 (
3 ) ##EQU00005##
[0033] The SE is then calculated utilizing Equation (4):
SE = - i = 1 16 p ( i ) log ( p ( i ) ) log ( 1 16 ) ( 4 )
##EQU00006##
[0034] The autocorrelation function is also used to characterize
correlation between among the current and past samples of RR
intervals. A practical estimate is provided by Equation (5).
.PHI. ^ xx ( .tau. ) = 1 R - .tau. .intg. 0 R x ( t ) x ( t - .tau.
) t ( 5 ) ##EQU00007##
[0035] Thus, .phi..sub.x,x(.tau.) is a measure of how correlated
x(t) is with its past value .tau. seconds earlier.
[0036] For noisy or broadband data, the autocorrelation at all
delays other than 0 will be close to 0. This fact is utilized for
the detection of AF from its RR interval series by taking the
difference between the autocorrelation at delay 0 and at delay 1
and comparing with some threshold. In addition, the autocorrelation
at delay 0 is always normalized to 1 so as to enable comparison
with a fixed and easy-to-compute threshold. A threshold of 0.02 was
used for ACORR that is any value that is greater than 0.02 is
considered as AF.
[0037] In the present invention, a filtering of ectopic beats is
preferably also performed. Ectopic beats occurring during regular
sinus rhythm are a potential cause of erroneous detection of AF
since they confound all three components of the algorithm.
Typically, a premature beat is characterized by the combination of
a short coupling interval to the preceding normal RR interval,
followed by a compensatory pause which is longer than both the
ectopic coupling interval and the subsequent normal RR
interval.
[0038] Thus, if the i-th RR interval is premature and the i-th+1 RR
the compensatory pause, then RR[i-1]>RR[i]<RR[i+1] and
RR[i]<RR[i+1]>RR[i+2], yielding at least two additional
turning points and three if RR[i+1]>RR[i+2]<RR[i+3]. In order
to recognize the characteristic short-long RR interval sequence of
ectopic beats a ratio RR[i]/RR[i-1] is computed for each RR
interval in the time series. For a regular sinus rhythm, this ratio
is close to unity and fluctuations around it represent physiologic
variability, referring to beat-to-beat RR interval adjustment
caused by autonomic nervous control. In the case of ectopy, the
sequence of ratios is RR[i]/RR[i-1].ltoreq.0.8,
RR[i+1]/RR[i].gtoreq.1.3, and RR[i+2]/RR[i+1].ltoreq.0.9.
Preferably, rather than relying on an arbitrary fixed ratio,
diverse ectopic beats with varying coupling intervals are captured
by searching for RR sequences which satisfy the conditions
RR[i]/RR[i-1]<Perc1 and RR[i+1]/RR[i]>Perc99 and
RR[i+1]/RR[i+2]>Perc25 (where Perc1, Perc99, and Perc25 are the
first, 99th, and 25th percentile of RR ratios, respectively). When
an ectopic beat is encountered, it is excluded from further
analysis along with its compensatory pause.
[0039] The present invention utilizes the following threshold
definitions. Optimal cut-points for the algorithm of the present
invention are identified by plotting the ROC for RMSSD, selecting a
threshold that optimizes sensitivity so that a maximum number of
possible AF beats can pass through to the next step. Such threshold
definition minimizes the likelihood that true AF beats are filtered
out in the first step of the analysis cascade.
[0040] In a preferred embodiment, a threshold of 9.8% of the mean
RR interval of the 128-beat segment was used, based on inspection
of the ROC, to yield a sensitivity and specificity of 99.1% and
79.33% for AF beats, respectively.
[0041] Next, keeping the RMSSD threshold fixed, a Turning Points
analysis was added and a second ROC was constructed by varying only
the confidence interval of the expected turning points ratio. As
discussed above, the expected TPR of a random series is
0.666.+-.confidence interval. The ROC is obtained by varying the
confidence interval of the TPR and plotting the corresponding
sensitivity against the specificity. Again, the TPR threshold is
selected so as to maximize the sensitivity without compromising on
the specificity (e.g. this resulted in the sensitivity and
specificity of 97.06% and 86.47% for AF beats, respectively).
[0042] Based on this analysis, sensitivity and specificity for AF
detection are optimal for a confidence interval of the TPR between
0.527 and 0.8. Using the same approach for SE reveals the optimal
cut point to be 0.8. For the AFIB database (N=23 subjects), a
threshold of 0.8 for the SE gave a sensitivity of 95.06% and
specificity of 96.68% of all AF beats. Using the same criteria on
the 200 series of the MIT BIH Arrhythmia database (N=25 subjects)
gave a sensitivity of 88.13% and a specificity of 82.01% for AF
beats. For the 100 series in the same database (N=23 subjects), the
specificity was 98.38% for AF beats. Since there are no true AF
beats in this series, the sensitivity cannot be quantified.
[0043] Testing was performed utilizing a 200 series of a MIT BIH
Arrhythmia database (N=25 subjects), which is the most challenging
database because it contains many artifacts, including Atrial
Premature Beats (APB), Ventricular Premature Beats (VPB). Removal
of VPB prior to data analysis was found to increase sensitivity and
specificity on the 200 series of the MIT BIH Arrhythmia database to
88.24% and 88.01% for AF beats, respectively.
[0044] For clinical applications, a most relevant objective is
detection of AF in a given recording, not necessarily every single
AF beat. Using this criterion, a sensitivity of 100% was achieved
for both the AF and arrhythmia databases. The results of use of the
present invention are summarized in Table 1, which provides AF
detection accuracy.
TABLE-US-00001 TABLE 1 AF beats AF episodes (Sensitivity %/
(Sensitivity %/ Database Specificity %) Specificity %) MIT-BIH AFIB
93.51/97.03 .sup. 100/99.11 (N = 23) MIT-BIH Ar- .sup. NA/98.38 NA
rhythmia 100 (note: no AF in series (N = 23) this database) MIT-BIH
Ar- 88.24/88.01 100/100 rhythmia 200 series (N = 25) ScottCare
Holter Not available 100/96 (N = 23)
[0045] Furthermore, automatic real time detection of AF in a
clinical setting appears feasible with the combined use of TPR,
RMSSD and SE, as the algorithm takes only 2.5 seconds to compute
24-hour Holter data which contains approximately 100,000 beats. The
algorithm needs 1.5 to 2 minutes of RR interval data for an SE test
of 128 beats, with computation time of a 128-beat data segment on
the order of 1-2 milliseconds.
[0046] FIGS. 3(a)-(e) show an AF episode, including RMSSD, TPR,
Shannon Entropy and ACORR, as an example calculation, with the
final detection using the corresponding thresholds for a sample
recording from the MIT BIH Atrial Fibrillation database. FIG. 3(a)
shows an episode of AF embedded in Sinus Rhythm from the MIT-BIH
Atrial Fibrillation database is shown, FIG. 3(b) shows an RMSSD,
FIG. 3(c) shows a TPR, FIG. 3(d) shows SE, and FIG. 3(e) shows
ACORR. Dotted lines in (b-e) represent threshold values as
determined by the ROC. A final detection result as to whether an AF
is detected is displayed in FIG. 1(f).
[0047] In another preferred embodiment of the present invention, a
piezoelectric sensor is utilized to obtain RR intervals. This will
facilitate a shift from current clinical practice of centralized AF
detection (i.e. making the diagnosis at a doctor's office, clinic
or hospital) to a distributed model relying on the patients
themselves to obtain the data. The present invention "piggy-backs"
on daily blood pressure checks made at home, in a pharmacy, or even
in select stores. In the preferred embodiment, a signal is acquired
through a blood pressure cuff adapted with an embedded
piezoelectric sensor, to obviate the need for an
electrocardiogram.
[0048] FIG. 4 shows a piezoelectric sensor incorporated into a
blood pressure cuff for placement on a finger or on the brachial
artery, and FIG. 5 provides a comparison of RR intervals obtained
from a commercial ECG device and PPV obtained via a piezoelectric
sensor.
[0049] A preferred embodiment of the present invention embeds a
piezoelectric crystal in a blood pressure cuff, as shown in FIG. 4.
A signal from the piezoelectric crystal is utilized to obtain
statistical criteria to diagnosis/exclude AF. In the preferred
embodiment, a peak systolic blood pressure is derived from
successive heart beats. The preferred embodiment allows for remote
patient monitoring in an essentially burden-free manner. The
preferred embodiment allows diagnosis to be made of asymptomatic
patients that is not addressed in conventional systems.
[0050] As shown in FIG. 5, a close correlation exists between ECG
and piezoelectric sensor derived signals. The device of the present
invention does not impose an additional burden on the patient,
other than an additional three to five minute data collection
period. Moreover, since recording of an electrocardiogram with its
attendant cost is avoided, since the piezoelectric sensor is
reusable and does not require separate energy source, the
incremental cost is minuscule compared to the potential public
health benefit.
[0051] FIG. 6(a) shows a prototype of a wireless two-channel ECG
circuit and FIG. 6(b) shows wireless data collection of ECG
developed in accordance with the present invention. FIG. 7 provides
a flowchart summarizing data acquisition and the analysis
algorithm.
[0052] While the invention has been shown and described with
reference to certain exemplary embodiments of the present invention
thereof, it will be understood by those skilled in the art that
various changes in form and details may be made therein without
departing from the spirit and scope of the present invention as
defined by the appended claims and equivalents thereof.
* * * * *