the following detailed description , which references and incorporates fig1 - 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 . fig1 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 . fig2 , 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 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 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 , fig3 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 , fig3 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 , fig4 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 . 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 .
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 .