Abstract:
Miniature heart-monitoring devices, such as defibrillators and cardioverters, are implanted in humans to detect and correct abnormal heart rhythms Microprocessors and stored instructions, or algorithms within these devices govern how they interpret and react to abnormal heart rhythms. Algorithms that are too simple lead to unnecessary shocking of the heart, while those that are too complex consume considerable battery power. Accordingly, the inventor devised a relatively simple yet accurate algorithm for determining appropriate therapy options. One version of the algorithm computes three statistics—a range statistic, a minimum interval statistic, and a dispersion index—from a set of depolarization intervals. A scalar interval dispersion assessment, based on the three statistics, is then compared to a threshold to identify a rhythm as a flutter or fibrillation.

Description:
CROSS REFERENCE TO RELATED APPLICATION(S) 
   This application is a continuation of U.S. patent application Ser. No. 09/955,491, filed on Sep. 18, 2001 now U.S. Pat. No. 6,681,134, which is a continuation of U.S. patent application Ser. No. 09/343,924, filed on Jun. 30, 1999, now issued as U.S. Pat. No. 6,314,321, the specifications of which are incorporated herein by reference. 

   TECHNICAL FIELD 
   The present invention concerns heart-monitoring devices and methods, particularly implantable defibrillators, pacemakers, and cardioverters, and methods for processing heart-signal data. 
   BACKGROUND OF THE INVENTION 
   Since the early 1980s, thousands of patients prone to irregular and sometimes life threatening heart rhythms have had miniature heart-monitoring devices, such as defibrillators, pacemakers, and cardioverters, implanted in their bodies. These devices detect onset of abnormal heart rhythms and automatically apply corrective electrical therapy, specifically one or more bursts of electric current, to their hearts. When the bursts of electric current are properly sized and timed, they restore normal heart function without human intervention, sparing patients considerable discomfort and often saving their lives. 
   The typical implantable heart-monitoring device includes a set of electrical leads, which extend from a sealed housing through the veinous system into the inner walls of a heart after implantation. Within the housing are a battery for supplying power, a capacitor for delivering bursts of electric current through the leads to the heart, and heart-monitoring circuitry for monitoring the heart and determining not only when and where to apply the current bursts but also their number and magnitude. 
   The monitoring circuitry generally includes a microprocessor and a memory that stores a computer program. The computer program, or more generally the signal-processing algorithm, instructs the microprocessor how to interpret electrical signals that naturally occur during expansion and contraction of a heart muscle. The algorithm also instructs the processor what, if any, electrical therapy should be given to correct abnormal heart rhythms. 
   In general, these algorithms are either too complex or too simple. Complex algorithms require considerable processing time and power to implement. Greater processing time generally lengthens device response time, and greater power requirements generally shorten the lifespan of the batteries in these devices. Simple algorithms, though faster and less-power-hungry, are often less accurate in interpreting heart electrical signals, leading devices to overlook some heart conditions, to apply unnecessary electrical therapy, or to apply the wrong type of therapy. 
   Accordingly, there is a continuing need for algorithms that are not only energy-efficient, but also highly accurate in diagnosing and treating abnormal heart rhythms. 
   SUMMARY OF THE INVENTION 
   To address this and other needs, the inventor has devised new methods for processing heart electrical signals and selecting appropriate therapy options. An exemplary embodiment of the method computes three statistics—a range statistic, a minimum interval statistic, and a dispersion index—from a set of atrial depolarization intervals, which indicate the time between successive depolarizations in the atria of a heart. More particularly, after rejecting the two shortest and two longest intervals, the exemplary embodiment defines the range statistic as the difference between a first and last one of the remaining intervals, the minimum interval as the smallest of the remaining intervals, and the dispersion index as the standard deviation of the remaining intervals. 
   The exemplary embodiment then uses the three statistics to compute a number, which the inventor calls an interval dispersion assessment (IDA), to quantify the current rhythmic state of a heart. If this number is greater than a threshold value, typically experimentally determined, the exemplary embodiment interprets the current rhythmic state of the heart as, for example, an atrial or ventricular fibrillation. On the other hand, if the number is less than the threshold value, the exemplary embodiment interprets the rhythmic state as an atrial flutter or ventricular tachycardia. 
   Other exemplary methods use the three statistics to define a point in a three-dimensional space. The space is defined by three axes which correspond to the three statistics, making it possible to plot the “position” of the point in the space. These methods also define a surface, for example, a plane in the space, based on a set of values for the three statistics. The set of values are determined using a threshold value as a constraint. Position of the point above or below the surface can then be used to identify a rhythmic state corresponding to the point as, for example, an atrial flutter or atrial fibrillation or as a ventricular tachycardia or ventricular fibrillation. 
   Ultimately, the exemplary method and other methods embodying teachings of the present invention can be incorporated into medical devices, for example, pacemakers, defibrillators, or cardioverter defibrillators, to identify and treat abnormal rhythmic conditions both efficiently and accurately. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  is a block diagram of an exemplary implantable heart monitor incorporating teachings of the present invention. 
       FIG. 2  is a flow chart illustrating an exemplary method incorporating teachings of the present invention. 
       FIG. 3  is an exemplary graph of a three-dimensional function incorporating teachings of the present invention. 
       FIG. 4  is exemplary graph of another three-dimensional function incorporating teachings of the present invention. 
   

   DESCRIPTION OF THE PREFERRED EMBODIMENTS 
   The following detailed description, which references and incorporates  FIGS. 1-4 , describes and illustrates one or more specific embodiments of the invention. These embodiments, offered not to limit but only to exemplify and teach the invention, are shown and described in sufficient detail to enable those skilled in the art to practice the invention. Thus, where appropriate to avoid obscuring the invention, the description may omit certain information known to those of skill in the art. 
     FIG. 1  shows an exemplary implantable heart-monitoring device (or pulse generator)  100  incorporating teachings of the present invention. Device  100  includes a monitoring system  110 , a lead system  120 , a therapy system  130 , a power system  140 , and an interconnective bus  150 . Monitoring system  110  includes a processor or microcontroller  112  and a memory  114 . Memory  114  includes one or more software modules  116  which store one or more computer instructions in accord with the present invention. Some embodiments of the invention replace software modules  116  with one or more hardware or firmware modules. In the exemplary embodiment, processor  112  is a ZiLOG™ Z80 microprocessor (with a math coprocessor), and memory  114  is a read-only memory. However, the invention is not limited to any particular microprocessor, microcontroller, or memory. 
   Lead system  120 , in the exemplary embodiment, includes one or more electrically conductive leads—for example, atrial, ventricular, or defibrillation leads—suitable for insertion into a heart. One or more of these are suitable for sensing electrical signals from a portion of the heart and one or more are suitable for transmitting therapeutic doses of electrical energy. Lead system  120  also includes associated sensing and signal-conditioning electronics, such as atrial or ventricular sense amplifiers and/or analog-to-digital converters, as known or will be known in the art. 
   In some embodiments, lead system  120  supports ventricular epicardial rate sensing, atrial endocardial bipolar pacing and sensing, ventricular endocardial bipolar pacing and sensing, epicardial patches, and Endotak® Series and ancillary leads. In some embodiments, lead system  120  also supports two or more pacing regimens, including DDD pacing. Also, some embodiments use a housing for device  100  as an optional defibrillation electrode. The invention, however, is not limited in terms of lead or electrode types, lead or electrode configurations, pacing modes, sensing electronics, or signal-conditioning electronics. 
   Therapy system  130  includes one or more capacitors and other circuitry (not shown) for delivering or transmitting electrical energy in measured doses through lead system  120  to a heart or other living tissue. In the exemplary embodiment, therapy system  130  includes aluminum electrolytic or polymer-based capacitors. However, other embodiments use one or more other devices for administering non-electrical therapeutic agents, such as pharmaceuticals, to a heart. Thus, the invention is not limited to any particular type of therapy system. 
   In general operation, lead system  120  senses atrial or ventricular electrical activity and provides data representative of this activity to monitoring system  110 . Monitoring system  110 , specifically processor  112 , processes this data according to instructions of software module  116  of memory  114 . If appropriate, processor  112  then directs or causes therapy system  130  to deliver one or more measured doses of electrical energy or other therapeutic agents through lead system  120  to a heart. 
     FIG. 2 , which shows an exemplary flow chart  200 , illustrates an exemplary data-processing method embodied within software module  116  and executed by processor  112 . Flow chart  200  includes blocks  202 - 220 , which are arranged serially in the exemplary embodiment. However, other embodiments of the invention may execute two or more blocks in parallel using multiple processors or a single processor organized as two or more virtual machines or subprocessors. Moreover, still other embodiments implement the blocks as two or more specific interconnected hardware modules with related control and data signals communicated between and through the modules. For example, block  206  can be built as a range-determination module, block  208  as a minimum-interval-determination module  208 , block  210  as a dispersion-index-determination module  210 , and block  212  as an interval-assessment module  212  which receives inputs from modules  208 - 210  and outputs a signal or quantity based on these inputs. Thus, the exemplary process flow is instructive to software, firmware, and hardware implementations. 
   In process block  202 , processor  112  of device  100 , receives data representative of all or part of an electrogram, including atrial (or ventricular) electrical activity. From this data, the processor calculates the time between successive atrial (or ventricular) depolarizations. In other words, the processor computes a set of pp-intervals (or atrial intervals) from the electrogram, with each pp-interval based on the time between one atrial depolarization and the next occurring atrial depolarization in the electrogram. Other embodiments compute intervals based on other generally recurrent features in the electrogram. 
   At process block  204 , the processor selects a predetermined number Y of the computed intervals for further processing, thereby defining a data window. For example, one embodiment extracts the last 12 consecutive intervals; another extracts the first or last 48 consecutive intervals; and yet another extracts the last or first 3 or 6 consecutive intervals. The selection process, in the exemplary embodiment, also entails sorting the computed intervals by magnitude and rejecting a certain number of the smallest intervals, for example the two smallest, and a certain number of the largest intervals, for example the two largest. Thus, the present invention is not limited to any particular number of intervals or to any particular scheme of selecting these intervals. 
   As shown in process blocks  206 ,  208 , and  210 , the processor then uses the set of Y intervals to calculate three statistics. In block  206 , the processor determines the range of the last Y intervals, that is, the time, or temporal distance, between the earliest or first intervals and the most-recent or last interval included in the set of Y intervals. Some embodiments define and calculate the range based the time between the average of a first subset of the intervals and the average of a second subset of the intervals. 
   Thus, for example, one embodiment averages the earliest or first three intervals to determine a first composite interval, averages the latest or last three intervals to determine a second composite interval, and then computes the range as the difference of the first and second composite intervals. Moreover, variations of this embodiment, average the intervals using a weighted averaging scheme to give one or more of the intervals greater or lesser significance within the resulting composite interval. 
   Still other embodiments replace the range statistic with stability measurements as used in the existing Ventak™ family of devices manufactured by Guidant Corporation of St. Paul, Minn. Stability measurments are weighted averages of the differences between successive intervals. For example, if there are three intervals, one stability measurment would be the average of the difference of the first and second intervals and the difference of the second and third intervals. Weights may be chosen to emphasize or deemphasize the relative importance of certain intervals, for example, older or younger intervals. 
   In block  208 , the processor determines a minimum interval from the set of Y intervals. In the exemplary embodiment, the processor selects the smallest interval in the set of Y intervals. However, in other embodiment, determining the minimum interval entails averaging two or more of the smallest intervals and/or selecting a minimum interval from a subset of the Y intervals. For example, some embodiments reject one or more of the intervals as a false interval, based on their length, to prevent them from corrupting the process of determining a minimum interval. 
   Block  210  entails determining a third statistic, that is, a dispersion index, based on the distribution or dispersion of the set of the Y intervals. The exemplary embodiment computes this dispersion index as the variance or standard deviation of all or a portion of the Y intervals. More specifically, computing the variance entails computing a mean, or average, interval using the relevant intervals, summing the squares of the interval deviations from the average interval (that is, subtracting the mean interval from each relevant interval to obtain a difference, squaring each difference, and adding the squared differences together), and dividing the total sum of these squares by the number of relevant intervals. Variance can be succinctly expressed as
 
Variance=( N− 1) −1   *Σ   N ( Y   i   −Y   mean ) 2 ,  Eq. (1)
 
where N denotes the number of relevant intervals, Σ N  denotes summation over the N relevant intervals, Y i  denotes the i-th one of the relevant intervals, and Y mean  denotes the means, or average, of the N relevant intervals. (In some embodiments, N, the number of relevant intervals, is not equal to Y.) Standard deviation is defined as the positive square root of Variance.
 
   Other embodiments of the invention use other methods to quantify dispersion. For example, one embodiment weights one or more of the intervals to give these intervals more or less significance in an otherwise conventional calculation of variance or standard deviation. Another embodiment, simply averages the absolute deviation of each relevant interval from a mean interval or from a selected one of the relevant intervals, such as the median interval. Still other embodiments of the invention use other measures of interval variation about some other parameter or measure. For example, one can generalize from the use of variance, which is a second order moment about the means of a sample set, to use higher, that is, third, fourth, and so forth, moments about the mean or another desirable quantity. Other embodiments also use versions of a stability measurement. After calculation of the dispersion index, execution of the exemplary method proceeds to process block  212 . In block  212 , the processor calculates a scalar quantity, which the inventor defines as an interval dispersion assessment (IDA), based on the three statistics. In the exemplary embodiment, this entails evaluating a predetermined scalar function at the three statistics. Mathematical, this is expressed as
 
 IDA=f (Range, Min_interval, Dispersion_index),  Eq. (2)
 
where f denotes a predetermined function including at least three variables, or degrees of freedom.
 
   More particularly, the inventor has devised two exemplary scalar functions. In a first exemplary scalar function, the IDA is directly proportional to the range and the dispersion index and inversely proportional to the minimum interval. In mathematical terms, this is expressed as
 
 IDA   1   =K *Range*Dispersion_index*(Min_interval) −1   Eq. (3)
 
where K is a constant, Range denotes the range statistic calculated in block  208 , Dispersion_index represents the dispersion index calculated in block  210 , and Min_interval is the statistic calculated in block  206 . An exemplary value for K is unity.
 
   In a second exemplary scalar function, the processor computes the AIDA according to the following equation:
 
 IDA   2   =K   1 *Range+ K   2 *Dispersion_index+ K   3 *(Min_interval) −1   Eq. (4)
 
where K 1 , K 2 , and K 3  are constants. Thus, the second exemplary scalar function defines the IDA as a weighted sum of the range, dispersion index, and minimum interval. Exemplary values for K 1 , K 2 , and K 3  are respectively 0.0001, 0.0001, and 1.00. The Min_interval term in equation (4) is indicative of a maximum rate of depolarization. If K 3  equals 6000, then this term will equal the maximum rate.
 
   The three statistics, range, minimum interval, and dispersion index can be combined in an unlimited number of ways to derive an IDA. For example, one embodiment averages IDA 1  and IDA 2  to determine another IDA, and another simply adds them or portions of them together to determine another IDA. Thus, the invention is not limited to any particular form of mathematical combination. 
   After calculating one or more IDAs, the exemplary method proceeds to process block  214 , which entails comparing at least one calculated IDA to a therapy threshold. If the IDA is greater than the therapy threshold, it indicates a first heart condition, such as atrial or ventricular flutter, and the processor branches to block  218  at which it directs therapy system  130  to diagnose the current rhythmic state as pace-terminable, which means that pacing pulses are likely to restore normal heart function. If the IDA is less than the therapy threshold, it indicates a second heart condition, such as atrial or ventricular fibrillation, and the processor branches to block  216  to diagnose the current rhythmic state as non-pace terminable, meaning that pacing pulses are not likely to restore normal heart function. An exemplary therapy threshold for discerning atrial flutter and atrial fibrillation using the first exemplary IDA is 2.25 or 5.0, and an exemplary threshold for discerning atrial flutter and atrial fibrillation using the second IDA is 0.001 or 2.25. Generally, one can determine therapy thresholds for an IDA in accord with the present invention, through experimentation using actual heart data. 
   After making the appropriate diagnosis in block  216  or block  218 , the processor executes block  220 , directing therapy system  130  to apply a therapy appropriate for the classification of the current rhythmic state represented by the intervals. The inventor forecasts that the use of the interval dispersion assessment as a determinant of therapy choice will ultimately result in more accurate therapy choices than is possible with algorithms of similar complexity. Moreover, the accuracy of the exemplary interval dispersion assessment or other versions may even rival that of more complex algorithms while saving considerable power and processing time. 
   The comparison of the scalar IDA to scalar threshold is a very simple way of discerning one condition from another condition, for example, atrial flutter or ventricular tachycardia from atrial or ventricular fibrillation. However, another aspect of the invention stems from realization that equations (3) and (4), which are used to compute the exemplary IDAs, can be set equal to a threshold value to define a set of ordered triples, which actually define surfaces in a three-dimensional space. 
   For example,  FIGS. 3 and 4  show three-dimensional surfaces developed by setting each of the functions equal to an exemplary threshold values and evaluating them over specific domains of interval range, minimum intervals, and dispersion indices. 
   More specifically,  FIG. 3  shows a surface  300  plotted in the three-dimensional spaced by an inverse-minimum-interval axis  302 , a range axis  304 , and a dispersion axis  306 . Surface  300  represents a set of minimum intervals, ranges, and dispersions indices which jointly make equation (3) equal 5, with K equal 1. Similarly,  FIG. 4  shows a surface  400  plotted in a space defined by an inverse-minimum-interval axis  402 , a range axis  404 , and a dispersion axis  406 . Surface  300  represents a set of minimum intervals, ranges, and dispersions indices which jointly make equation (4) equal 0.01, with K 1 , K 2 , and K 3  respectively 0.0001, 0.0001, and 1.00. 
   Surface  300  and surface  400  divide their respective spaces into two subspaces. One subspace, denoted AF 1  for atrial flutter, contains points generated from pace terminable rhythms, and the other subspace, denoted AF for atrial fibrillation, contains points that are non-pace terminable. Thus, in some embodiments, which provide a graphical display for displaying surface  300  or  400 , the relation of a point to the surface can be used to diagnose rhythmic states. If an IDA point lies above surface  300  or  400 , the processor deems the rhythm that produced the IDA, for example, certain atrial fibrillations, as non-pace terminable. Conversely, if the IDA point lies below the surface or oscillates above and below the surface over time, the processor deems the associated rhythm pace terminable. 
   Thus, the three statistics that the exemplary embodiment uses to define an IDA also define a point in a three-space which lies on the surface or on either side of the surface. One can therefore discriminate a condition using its coordinate position relative to a linear or non-linear surface. Similarly, one can choose two of the three statistics and define a line of demarcation in a two-space defined by ordered pairs of the chosen two statistics. 
   In some embodiments, implantable device  100  includes a wireless transceiver, which permits use of an external programmer to interrogate and program device  100  via bi-directional radio communications. At a minimum, this allows adjustment of one or more of the thresholds and other parameters defining an IDA. These thresholds and parameters can then be set and changed based on observations of a specific patient or group of patients. In other embodiments, the inventor contemplates replacing or supplementing an existing software module or algorithm with one in accord with the present invention. In still other embodiments, the exemplary methods are used to classify atrial rhythms which are, for example, between 100 and 200 beats per minute, inclusive. 
   CONCLUSION 
   In furtherance of the art, the inventor devised new methods for processing data representative of a heart electrogram and selecting appropriate therapy options. An exemplary embodiment of the method entails computing three statistics—a range statistic, a minimum interval statistic, and a dispersion index—from a set of depolarization (or polarization) intervals. More particularly, the exemplary embodiment defines the range statistic as the difference between a first and second one of the depolarization intervals, the minimum interval as the smallest of a subset the intervals, and the dispersion index as the standard deviation of the intervals. The exemplary embodiment then uses the three statistics to compute a scalar interval dispersion assessment (IDA), which it compares to a threshold to identify an appropriate therapy option. Ultimately, the exemplary method and other methods incorporating teachings of the present invention, can be incorporated into an implantable medical device, for example, a defibrillator or a cardioverter defibrillator, to identify and treat abnormal rhythmic conditions efficiently and accurately. The teachings of the present invention can also be incorporated into other applications which require classification of system conditions or states based on recurrent events. 
   The embodiments described above are intended only to illustrate and teach one or more ways of practicing or implementing the present invention, not to restrict its breadth or scope. The actual scope of the invention, which embraces all ways of practicing or implementing the teachings of the invention, is defined only by the following claims and their equivalents.