 |
Description  |
|
|
BACKGROUND OF THE INVENTION
I. Field of the Invention
This invention relates generally to medical electronic diagnostic and/or
treatment apparatus and more particularly to an implantable device having
means for logging heart rate variability data over extended time periods
for subsequent read-out and analysis.
II. Discussion of the Prior Art
Currently available implantable cardiac pacemakers and defibrillators are
capable of collecting and storing a variety of information relating to the
instantaneous electrical response of the heart and providing this
information in real time, via telemetry link, to an external programmer
which may readily be equipped with substantial data processing, storage
and display resources. Certain other implantable devices are capable of
logging a limited amount of parametric data, typically in the form of
various event counters, which is retained in the device for future
telemetry and analysis. These arrangements provide useful, but limited,
information regarding the status of the heart or progression of the
disease.
Accurate diagnosis requires evaluation of extensive data over an extended
period of time. Long-term telemetry of raw data from an implanted device
is undesirable because the low energy available to the implanted
transmitter requires that the external receiver be worn by the patient in
close proximity to the implanted device. Even then, the transmission of
voluminous raw data may prematurely deplete the implanted battery. The
preferred device currently used for logging diagnostically useful cardiac
data is known as a Holter monitor. It is an external data recorder which
monitors and stores cardiac electrical data sensed via surface EKG
electrodes. It is desirable to incorporate the data logging function
exemplified by the Holter monitor into the implantable pacemaker. However,
generally speaking, prior art implantable cardiac devices, such as
pacemakers, lack the memory capacity to store the voluminous raw data
and/or the power to telemeter it. Well known signal compression techniques
offer insufficient compression.
Lossless signal compression techniques, such as run length encoding, are
well known. Since only redundant elements of the signal are eliminated all
of the raw data is recoverable from the compressed signal with exactly the
same resolution that it was quantized. However, all lossless compression
techniques suffer from an insufficient compression ratio and an
intolerable processing load. The highest compression ratios are achieved
by first extracting a feature, or set of features, from the raw data,
which is well correlated to the cardiac diagnostic process, and then
employing lossy compression on the extracted features. Ideally, lossy data
compression allows most of the data to be discarded, preserving only the
minimum set of data required to adequately characterize the extracted
features.
Heart Rate Variability (HRV) has been identified as a feature of cardiac
activity which is particularly useful for both diagnosis and prognosis.
HRV is defined as a measure of the beat-to-beat variance in sinus cycle
length over a period of time. It is used as a measure of parasympathetic
tone. It has been determined that individuals with diminished HRV have
reduced vagal tone and probably are at increased risk of death following
myocardial infarction. Various researchers have employed spectral analysis
to characterize HRV. (See Coumel et al., "Heart Rate and Heart Rate
Variability in Normal Young Adults", J. Cardiovascular Electrophysiology,
Vol. 5, No. 11, November 1994.) Others have elected to characterize Heart
Rate Variability by considering the beat-to-beat variability in heart
rate. (See Ori, et al., "Heart Rate Variability", Clinics, Vol. 10, No. 3,
August 1992.) This beat-to-beat characterization has been expanded to a
two-dimensional histogram which displays the probability density function
of the absolute beat-to-beat interval difference as a function of
interval. The process of forming such a two-dimensional histogram is
defined as "binning". This process will be subsequently described in
detail. In summary, although the prior art shows several ways that heart
rate variability data, accumulated over an extended period of time, may be
processed to make the data more understandable to a clinician, we are not
aware of any teaching of how logging of HRV data might be accomplished
within an implantable pacemaker.
OBJECTS OF THE INVENTION
It is an object of this invention to provide a method and apparatus for
very efficiently processing, logging and disseminating the essential
features relating to HRV accumulated from a continuous long term
monitoring of cardiac activity. The method is sufficiently conservative of
data memory, program memory and power consumption that it may be
incorporated within an implantable pacemaker to log a 24 hour period of
cardiac activity accumulated for subsequent telemetry. The 24 hour data
can be converted to a single index through calculation of a standard
deviation. These standard deviations can be stored for long-term trend
analysis.
It is a further object of this invention to provide a method of processing
and displaying HRV data received via telemetry from the microprocessor in
a manner most easily understood by a clinician and which will provide the
greatest graphical contrast between a normal and an abnormal HRV pattern,
for example, in a sick heart.
SUMMARY OF THE INVENTION
Referring to FIG. 1(a), a two-dimensional histogram indicative of the rate
variability of a normal heart over a 24 hour interval is illustrated. The
X axis corresponds to the interval between an instant pair of consecutive
heartbeats with the interval increasing from left to right. The Y axis
corresponds to the absolute difference between the instant interval and
the immediately preceding interval with the difference increasing from the
background to the foreground. The X axis and the Y axis are quantized into
bins to form the two-dimensional histogram. The Z axis corresponds to the
base 2 logarithm of the frequency of occurrence for each bin. The
resulting histogram has the appearance of a mountain with foothills
projecting to the right and front. The mountain corresponds to the more
typical cardiac interval variations while the foothills correspond to the
more atypical and varied activity. It is believed that the entire texture
of this histogram is diagnostically useful. The spatial and quantitative
relationship of the foothills to the mountain is of particular interest.
FIG. 1(b), showing a corresponding presentation for a diseased heart,
provides support for this belief. Note that the diseased heart exhibits
much less variability. Even without considering the beat-to-beat interval
variations, there is less variability of the intervals themselves as shown
by the higher and narrower peak for the diseased heart. Further, the
foothills indicative of beat-to-beat variability are absent. Logarithmic
compression of the Z axis greatly enhances the contrast between FIGS. 1(a)
and 1(b) in that the foothills are effectively magnified while the
essential information regarding the shape of the mountain is preserved.
Fortuitously, the logarithmically compressed binning also yields a very
high degree of data compression which can be accomplished with a
computationally simple process.
In a preferred embodiment of the invention, an implantable stimulator, such
as a pacemaker or defibrillator or monitoring device, processes and logs
multiple sets of two-dimensional histogram data similar to what is plotted
in FIG. 1. Data is accumulated only for sensed beat intervals and not any
intervals associated with paced events. Each set corresponds to a fraction
of a day and there are a sufficient number of sets to continuously span a
24-hour interval. One possibility may comprise 24 one-hour sets.
Hereafter, the data accumulated in this manner over a 24-hour interval
will be referred to as the histographic log. Data accumulation is
preferably initiated by a telemetry command and proceed automatically to
completion. Alternatively, an ongoing mode of logging could be initiated
which terminates in response to a particular cardiac condition sensed by
the pacemaker. This would provide HRV data coincident with a significant
cardiac episode. For either mode, the data is retained for subsequent
retrieval via telemetry. The external pacemaker programmer may then
develop a display or hard copy presentation of the data. Presentations
contemplated include individual histograms for each data set or composite
histograms of a plurality of data sets. One particularly useful mode of
display, particularly for a video terminal device, is to display the bins
of FIG. 1 as a uniform array of squares where the X and Y axes of the
display correspond to the X and Y axes of FIG. 1, respectively. The value
for each bin, i.e., the Z axis of FIG. 1 is represented as varying colors
or shades of grey to thus produce a contour map form of presentation.
Alternatively, the data may be displayed as a time lapse graphical display
showing motion over, say 24 hours.
A single pair of indices, i.e., the mean value and standard deviation of
the absolute value of the beat-to-beat difference provides the best
summary characterization of the more detailed information provided by the
two-dimensional histogram. These indices may be computed for a
substantially shorter period than provided by the histographic log to more
thoroughly characterize the short and long term trends of heart rate
variability. For example, a 24-hour period may be characterized with 288
pairs of data, each pair computed for a five-minute interval. The data can
be accumulated in a one-dimensional, 1152 byte Trend array contained in
the RAM memory of the pacemaker. The pacemaker logs the square of the
variance to thereby move the computationally intensive burden of
performing the square root operation required to compute the standard
deviation from the pacemaker to the external pacemaker programmer.
Hereafter, the set of indices accumulated in this manner will be referred
to as the "time domain log". Although the histographic log and the time
domain log each have independent diagnostic value, in a preferred
embodiment, a single implantable pacemaker can, alternatively, as
commanded via telemetry, generate either log, storing the accumulated data
in a common region of memory.
DESCRIPTION OF THE DRAWINGS
FIG. 1(a) is a two-dimensional histogram of HRV for a normal healthy heart;
FIG. 1(b) is a two-dimensional histogram of HRV for a sick heart;
FIG. 2 is a block diagram representation of a DDD pacemaker incorporating
the present invention;
FIG. 3 is a memory map for the HRV log in accordance with the present
invention;
FIG. 4 is a plot of histogram bin counters vs. beat-to-beat difference in
R-R interval;
FIG. 5 is a logic block diagram of the logarithmic compression algorithm
employed in implementing the present invention;
FIG. 6 is a software flow diagram showing the manner in which the algorithm
of FIG. 5 can be implemented in firmware;
FIG. 7 is a program flow chart of the software routine used in generating a
time domain log.
DETAILED DESCRIPTION OF THE INVENTION
FIG. 2 shows a system block diagram of the instant invention. It is
representative of a prior art, implantable microprocessor-controlled,
dual-chamber demand pacemaker modified to incorporate the invention, but
the invention is not limited to this embodiment. implantable pacemaker is
shown enclosed by dashed line box 2 Either Read Only Memory 10 or RAM
memory 30 contains a program which is executable by Central Processing
Unit 20 to perform the well-known functions of DDD pacing and telemetry of
data stored in memory 30 to an external programmer device enclosed by
dashed line box 42. The programmer 42 is entire conventional. The ROM 10
also contains subprograms for selectively generating both a histographic
log and a time domain log. The pacing program conditionally calls one of
these subprograms for each occurrence of a sensed ventricular event
(heartbeat) preceded by a ventricular event. A pair of status flags
determines which, if either, of these subprograms is called. Random Access
Memory 30 provides the temporary storage required by all programs,
including the data logging subprograms. It also provides storage for the
data accumulated in the logging process. The telemetry subprogram controls
the additional status flags associated with data logging and to transmit
the accumulated log data to the external programmer 42.
Although a histographic HRV log may be implemented with a range of
parametric values, for this description a square array of bins, e.g.,
16.times.16 bins with each bin being four bits deep, is assumed. FIG. 3
shows a memory map of the region of Random Access Memory 30 used to
generate such a log. Each two-dimensional histogram is packed into a 128
byte block of memory designated Histogram 1 to Histogram 24. Each byte
contains two 4-bit bins. The counter array contains a one-byte counter for
each bin of the histogram array or for 16.times.16 bins, 256 bytes are
needed. These comprise a set of working counters which operate
cooperatively with a histogram bin storage array, which is also a counter
array, to perform logarithmic compression of the histogram data as it is
accumulated. For each bin, the corresponding counter in the counter array
functions as a variable weight pre-scaler for the histogram bin counter,
i.e., the prescale value increases in accordance with the log of the
number of events for a given bin.
TABLE 1
______________________________________
Bin Counter Event Count
Pre-Scale Divisor
______________________________________
0 0 1
1 1 1
2 2 1
3 3 1
4 4-7 4
5 8-11 4
6 12-15 4
7 16-19 4
8 20-51 32
9 52-83 32
10 84-115 32
11 116-147 32
12 148-403 256
13 404-659 256
14 660-915 256
15 916-1171 256
______________________________________
Table 1 shows how the pre-scale divisor varies to yield a logarithmically
compressed count in the corresponding histogram bin counter. For the first
four hits, each hit increments the bin counter by one. As used herein, the
term "hit" is used to indicate when a measured RR interval and a computed
absolute value of the difference in successive RR intervals fall within
the range of a given bin. Upon a bin count reaching four, the pre-scale
divisor is changed to four. Thus, the bin counter is not again incremented
until the eighth hit. The pre-scal divisor also switches to 32 and then to
256 when the bin count reaches 8 and 12 respectively. As the pre-scale
divisor is progressively incremented, the higher values of the bin counter
acquire a greater weight, i.e., higher bin counter values represent a
greater range of hits.
The solid stepped line of FIG. 4 graphically shows the value of the
histogram bin counter as a function of input event counts, where an input
event is a beat-to-beat difference which is within the range of a given
histogram bin. The results shown graphically in FIG. 4 are produced by an
algorithm based on Table 1. Note that these results generally correspond
to the theoretical logarithm of the input event count which is shown as a
dashed line in FIG. 4.
The logarithmic compression algorithm is implemented in hardware as
illustrated in the logic block diagram of FIG. 5. As will be explained, it
is also implementable in software. With reference to FIG. 5, counter 50 is
shown as a 4-bit synchronous counter whose count value corresponds to an
instant bin of the histogram array. Counter 52 is an 8-bit synchronous
counter corresponding to the counter from the aforementioned counter array
which is paired with counter 50 to function as a pre-scaler. Each
heartbeat generates a signal on event line 54 which is connected to the
clock inputs of counters 50 and 52. Thus, the counters are incremented in
response to a heartbeat event when their respective count enable lines, 56
and 58, are active. AND gates 60, 62 and 64 decode the outputs of counter
52 for values of 3, 31 and 255 respectively. Multiplexor 66 selects one of
these decode outputs depending upon the permutations of the two most
significant bits of counter 50. Thus, the states of counter 50 equal to
00xx, 01xx, 10xx and 11xx invoke pre-scale divisors of 1, 4, 32 and 256
respectively. Counters 50 and 52 are cleared by a signal on "Begin" line
68. A logical "1" on hit line 70 signals that the beat-to-beat difference
is within a given bin and is to be counted by counter pair 50 and 52. The
output of AND gate 72 remains ZERO unless count saturation, i.e., 1171
counts is reached. Thus, AND gate 74 remains enabled to activate clock
enable line 56, causing counter 52 to be clocked by the event signal 54.
Initially, line 6 (P=T) is a logical "1" since the "0" input of
multiplexor 66 is selected. Thus, each hit also fully enables AND gate 78
to activate counter enable line 58 and cause counter 50 to be incremented.
At this time, reset line 80 is also activated, via OR gate 82, thus
holding counter 52 cleared. When the count in counter 50 reaches 4 the "1"
input of multiplexor 66 is selected. Now, AND gate 78 is enabled only when
AND gate 60 is enabled to invoke a pre-scale value of 4. That is, every
fourth hit increments counter 50 and resets counter 52. Pre-scale values
of 32 and 256 are invoked as counting proceeds in counter 50 with the
selection of multiplexor input "2" and then input "3". When both counters
have filled, AND gates 64 and 84 are enabled to disable AND gates 74 and
78 which freezes the counters to prevent wrap around.
FIG. 6 is a software flow diagram which embodies the algorithm of FIG. 5 in
firmware rather than hardware. Here, the counters C and P (50 and 52) of
FIG. 5 are arrays of counters contained in RAM memory 30. This program is
called by the main pacing program for each spontaneous heartbeat (sensed R
wave) when the histographic log function is enabled. Each iteration of the
program corresponds to a "Hit" signal in FIG. 5. The program fetches the
specified counter pair from memory, operates on the counters in the manner
of FIG. 5 and stores the results in memory. When initialized, the variable
"Cbase" contains the base address corresponding to the beginning of the
first histogram array of RAM memory 30. Similarly, "Pbase" contains the
base address corresponding to RAM memory for the pre-scaler array.
Variable "RR" contains a value indicative of the R-to-R interval, i.e.,
the elapsed time since the previous heartbeat. Upon being called, step 100
tests the Hour Flag. If an hour has elapsed, Cbase is adjusted at step 102
to point to the next histogram array. At step 104, Cbase is tested against
a maximum Cbase limit, Cbase Mx, to assure that the program does not log
data outside of the histogram arrays. If the Hour Flag is cleared,
execution proceeds directly to step 106 where a scaled value of the R-to-R
interval, "RRS", which corresponds to the bins of the histogram, is
computed. For example, if the pacing program counts elapsed time at 128
counts/second, the variable RR in counts may range from 38 to 255
corresponding to a heart rate of 30 to 200 BPM. In this case, scaling
coefficients a =0.069 and b=2.626 would be appropriate. Steps 108 and 110
test the value of RRS and clip it, if necessary, to assure that RRS is in
the range of 0-15.
In a similar manner, steps 112-116 compute a scaled value of the
beat-to-beat difference .increment.RRS which ranges from 0-15. At step
112, the absolute difference of the instant R-to-R interval (RR) and the
previous R-to-R interval (RR.sub.0) is computed. Next RR.sub.0 is set
equal to RR to be available for the next iteration. Scaling coefficients
c=0.25 and d=0 would be appropriate to scale .increment.RRS to a range of
0-15. Variables RRS and .increment.RRS now define the bin in the
two-dimensional histogram which is to be incremented. At step 18, RRS and
.increment.RRS are multiplied to compute an index which identifies the
relative position of the subject bin in RAM memory 0.
Variable, P, is fetched from the counter array of RAM memory 30 using an
address which is the sum of "Idx.sub.p " and "Pbase". Idx.sub.p, itself,
is an address index for a linear bank of memory with no offset and is used
to point to P values. Since the C counters are 4-bit counters which are
packed two per byte, Idx.sub.c is formed as in step 118 of FIG. 6. The
least significant bit of .increment.RRS is saved to make a selection of
either the upper or lower nibble to thus unpack the data. Variable C is
fetched from the histogram array of memory 30 using an address which is
the sum of Idx.sub.c and Cbase. Steps 120-130 test the state of counter C
to determine the value of Mx which corresponds to the value of the
pre-scale divisor per Table 1. Counter P is incremented at step 132 and
tested against Mx at step 134. If P is less than or equal to Mx and if the
carry flag is equal to 0, execution passes directly to step 136 which
stores the current values of P and C using the indexes previously
described. C must be packed back into memory without disturbing its
associated nibble. Following step 136, execution is passed back to the
main pacing program. Alternatively, if at step 134 counter P is greater
than Mx or the carry flag is "1" then, at step 138, P is cleared and
counter, C, is incremented. Steps 140 and 142 clip C at a maximum of 15 to
prevent wrap around. For simplicity, the event counter P of FIG. 6 has
been described as being an 8-bit counter. Logarithmic compression may also
be accomplished with different length counters by computing N.sub.x
=2.sup.c -1. Alternatively, other forms of monotonic, non-linear mapping
may implemented with a ROM look-up table.
The time domain log is comprised of sequential pairs of data where each
data pair describes heart rate variability for a 5-minute interval. The
data pair is comprised of the mean value of the R-to-R interval and the
variance of the R-to-R interval. The external pacemaker programmer, that
is not subject to power constraints, extracts the square root from the
variance to obtain the standard deviation external to the implanted
device.
There is more than one approach to calculating variance. The direct
approach is inferior to the indirect method. The direct method for
estimating the variance of RR is:
.sigma..sub.RR.sup.2 =<RR.sup.2 >-<RR>.sup.2
The use of this approach requires separate and simultaneous calculation of
the 1st and 2nd moments of the data. The approach is numerically ill
conditioned, especially when the mean RR is on the order of the standard
deviation. Further, fixed-point INTEGER math is poorly suited to this
approach, since it introduces large errors in both moment estimates, which
when subtracted, yield a poor estimate of variance.
The indirect method provides an improvement in numerical performance:
.sigma..sub.RR.sup.2 =<[RR-RR].sup.2 >;RR=<RR>
If the mean is available prior to estimating variance, then this method
works fine. For a process with a non-zero mean, subtracting out the mean
prior to accumulation reduces the size of the numbers involved and avoids
the final subtraction. However, it is not possible to know the mean in
advance nor is it desirable to store the set of RR values in an array such
that the mean could be calculated followed by the variance. Although the
indirect method is preferred, modifications are still required to make the
RR variance calculation tractable in an implantable pacemaker.
The instance invention employs a novel variation of the indirect method,
where an estimate of the mean is substituted. The resulting variance can
be corrected after all the data values in the current 5-minute period have
been seen. A modified expression for the mean RR is required to instrument
this.
The usual summation required to evaluate the mean is not necessary with RR
interval data when the period of integration is fixed to a constant
interval of time. This is the case when calculating the mean RR interval
as:
##EQU1##
where N(n) is the number of RR intervals in the current period. While N(n)
is unknown until the end of the current period, the sum of all the
intervals, end-to-end over 5-minutes, is a constant 300 seconds. This
feature is a little unusual, but it greatly simplifies what must be done.
RR(n)=SUM/N(n) ; SUM=a constant
As indicated above, the variance of RR can be written as the sum of a
variance estimate and an error term.
.sigma..sub.RR.sup.2 (n)=.sigma..sub.est.sup.2 (n)+Error(n)
The estimated variance is simply expressed:
##EQU2##
Where N.sub.0 =N(n-1)
N.sub.1 =N(n)
But the exact expression for the variance of RR is
.sigma..sub.RR.sup.2 =<RR.sup.2 >-(SUM/N.sub.1).sup.2
Combining the information in the immediately preceding two equations
yields:
##EQU3##
Simplifying and solving for the error correction gives:
##EQU4##
An expansion of the error function above provides a correction which may
be practically computed in a pacemaker's microprocessor. After some
manipulation and retaining, only the first two terms in the expansion, the
above error correction equation reduces to:
##EQU5##
The corrected expression for the variance becomes:
##EQU6##
Where: N.sub.1 =N(n)
N.sub.0 =N(n-1)
.increment.N=N(n)-N(n-1)
RR(n-1)=SUM/N(n-1)
The next step is to apply node scaling to the accumulation step in the
preceding equation. During the simulation of RR intervals, values which
occasionally exceeded 255 were observed. This was with an RR resolution of
1/128 or 128 counts corresponding to 1 second. In order to avoid clipping,
all RR values were right shifted one bit. This allows the inputs to the
accumulator to be represented in 8-bits.
Further scaling is required to insure that the accumulation sum fits within
a 16-bit representation. To accomplish the above, the variance equation is
modified:
##EQU7##
Where: X.sub.0 =RR(n-1)
SUM=300*Countsec
Countsec=Counts/Second=128.
.varies.=8
FIG. 7 shows a flow diagram of a subprogram which employs the novel
variance algorithm to generate a time domain log. As with the histogram
subprogram, this program is called by the main pacing program for each
spontaneous heartbeat (sensed R wave) when the time domain log function is
enabled and passes the variable RR indicative of the R-to-R interval. Upon
initialization, the variable, N, which counts the number of events in a
5-minute period, is cleared, the variable, Idx, is set to a value
corresponding to the first address of the log data array and a dummy,
non-zero value is chosen for No. When the subprogram is called, step 200
tests the 5-Minute Flag, which indicates the end of the 5-minute sampling
period. If this flag is cleared, step 202 increments the variable N. At
step 204, RR is shifted right one place to prevent overflow in subsequent
calculations. Variable, X.sub.0 is one half of the mean value computed for
the preceding period. This provides the estimate for the mean of the
current period. The difference between the adjusted value of RR and
X.sub.0 is squared, multiplied by 1/2 and added to VSum. Sixteen-bit
multiplication is used to perform the square operation and VSum is
accumulated in a register.
As the subprogram is re-entered with each new value of RR, VSum
progressively accumulates the sum of the squares of the difference between
RR and the estimated mean. If the 5-minute flag is set, the subprogram
branches to compute the mean value and the square of the variance. At step
208, which is repeated every five minutes, the instant and previous values
of N are used to compute the correction required to compensate for the
difference between the estimated and actual value of the mean value. At
Step 210 The variable .sigma..sup.2 is computed. First, N is divided by 8
(.varies.=8), then VSum is divided by the scaled value of N. Finally, the
error correction is subtracted to obtain the actual value. Overflow and
truncation errors are minimized by employing well-known scaling techniques
at steps 208 and 210. At step 212, the mean value of the R-to-R interval
is computed by dividing a constant Sum by N. This computation relies upon
the fact that for sinus rhythm the sum of the R-to-R intervals is a
constant. Paced events are ignored and their time is thus subtracted from
the 300 seconds. On the first pass after initialization, Step 214 is
skipped. Otherwise, it stores the computed data in RAM memory 30, using
Idx as a pointer. If Step 216 determines that Idx is greater than Mx, a
Log Full Flag is set to inhibit the pacer program from making further
calls to this subprogram, which would overrun the log data array. At step
220, N.sub.0 is set equal to N to prepare for the next error calculation
and N is cleared. The variable X.sub.0 is set to one half the Mean to
provide a new estimate for the next set of calculations. With valid values
of N.sub.0 and X.sub.0 now established, subsequent calculations will be
accurate and will be logged at step 214 .
* * * * *
|
|
 |
|
 |
Description |
|
