as discussed above , the present invention relates to the apparatus for performing and analysing paced electrograms . the present invention includes various methods and arrangements each of which will be discussed in detail in turn . aspects of the processing of an electrogram 100 produced from stimulating a heart by inserting electrodes therein , for example , as described with reference to fig9 to 11 will firstly be described . an aspect of particular importance to the interpretation of electrograms is the post - processing of signals so as to reduce noise , so that potentials may be distinguished from noise in the signal , and so that the potentials can be recorded accurately . as is known in the art , signals inherently contain a mixture of noise and data , the noise originating from background sources such as fluorescent lights , x - ray heads , motors , monitors , other equipment commonly found in labs or hospitals , or poor catheter connections . reducing noise from a signal can be crucial , particularly in cases where a noisy signal has similar characteristics to a data signal ; in the case of electrograms , it is particularly important to identify and filter out erroneous peaks , which may produce false results when the electrogram 100 is analysed . in a first arrangement a series of templates is used to filter out the noise from an electrogram , as will be described with reference to fig1 and 17 a to 17 e . fig1 shows a single template 102 which may be correlated to a noisy simulated electrogram trace 100 , together with the simulated electrogram 106 after it has been filtered with the template . fig1 a shows the processing of an output signal 108 with a template 110 . the frequency components of the signal 108 ( in the time domain ) are first ascertained by applying a discrete fourier transform ( dft ) ( or fast fourier transform equivalent ) to the signal 108 . this can be shown as a representation 112 of the signal in the frequency domain after the transformation , where the discrete frequency components can be seen . a high - pass filter is then applied to the frequency components , which has the effect of truncating the lower frequency components ( such as those below 150 hz , which may be generated by hospital or laboratory equipment , for example ) whilst leaving the high frequency components ( the abscissa of the representation has been shifted from that of the representation after the dft ) present in the signal 114 . the template 110 to be compared with the signal 108 is then modified by means of a dft . the discrete frequency components of the template are then compared to those of the signal ( after filtering ) 114 , and an inverse dft is applied , to produce a correlator output 116 for that template . as an alternative to correlating signals 114 and the frequency components of the template , the signal and the template could be convolved . this process can be repeated for a number of templates . fig1 b shows an electrogram 118 , together with two examples ( 120 , 122 , each referred to as a “ trace ”) of the electrogram 118 after it has been correlated with a template . a first correlated trace 120 has been correlated with template 1 , while a second correlated trace 122 has been correlated with template 2 . it should be noted that the scale on the ordinate is different for the three traces shown in fig1 b . as can be seen from the first and second correlated traces , as a result of processing with the respective templates there are some peaks 124 common to both outputs , and some 126 different to both outputs . fig1 c shows a trace 128 derived from the electrogram 118 by high pass filtering the electrogram 118 , and further shows a trace 130 produced by correlating the electrogram 118 with a template , such as template 2 . fig1 c further shows schematic representations of a series of 4 different templates 132 . the noise in a trace , such as an electrogram representing raw data , or such as a correlated trace produced by correlating an electrogram with a template , as described above can be further reduced by a second method , shown in fig1 d . fig1 d shows an electrogram 134 , and a correlated trace 135 derived from this electrogram ( by correlation with a template , as discussed above , for example ). the peak to peak amplitude 136 of the correlated trace 135 in the range 200 ms to 400 ms is analysed , and any potentials having an amplitude below this are categorised as noise . in this way , the parameter of maximum peak to peak value is effectively used as an amplitude threshold for peaks which may be caused by data , i . e . any peak having an amplitude below this threshold is considered to be noise , and any peak having an amplitude above this threshold is analysed , as it may be caused by data . in terms of a value for this threshold , an amplitude value of approximately 5 μv has been found to be suitable . this threshold amplitude is then applied to the trace in the range 10 ms and 200 ms ( which is the part of the trace which is typically analysed , using the techniques described herein , for example ). in this case , any peaks having a lower amplitude than this threshold are discarded , and the peaks 137 having amplitudes greater than the threshold are retained for analysis . thus , in the example described above , four templates are used remove parts of the electrograms which do not fit with the template , and produce a correlated trace corresponding to each template . the correlated trace is then processed as shown in fig1 d to further reduce the amount of noise in the trace , and since this method is essentially accept / reject based on the amplitude , a binary method of reducing noise is used . the resultant correlated traces may be used in a number of different ways . for example traces having high correlation values ( i . e . one or more peaks in the resultant correlated trace have a correlation of 1 or approaching 1 ) can be used as the corrected trace , on which the electrogram analysis is subsequently based . alternatively , the correlation values associated with different templates can be compared with each other , so as to obtain an average . alternatively , two or more of the correlated traces may be processed with an “ and ” operator , to remove solitary peaks , or and “ or ” operator , together with a threshold value can be used , to show peaks above a threshold value which have been picked up by one template . it has been found that having a series of templates to filter different electrograms is sufficient to filter electrograms from the majority of patients . for example , a series of four templates may be used . these templates may be defined on the basis that they resemble data in a typical electrogram , and they may be time - dilated versions of each other . for example the series may comprise a master template ( template 1 ), which is shorter than the others , meaning that template 1 will typically pick up most of the data in the electrogram , together with some noise . the other templates are of varying lengths , and the longest of these will typically pick up less data ( i . e . it may miss small electrogram peaks caused by data ), but will also pick up less noise . fig1 e shows an electrogram 138 , together with a representation of the peaks determined by using the binary noise reduction techniques described above for each of the templates . preferably , the templates are used as follows : an electrogram is correlated with template 1 , and a correlated output trace is produced . this correlated output trace is then further analysed using the accept / reject threshold method discussed above . these steps are then repeated with the other three templates , to produce three other output traces , corresponding to the respective output traces from templates 2 , 3 and 4 . the output trace from template 1 is then compared to the traces produced from the other templates 2 , 3 and 4 . the output trace produced from one of templates 2 , 3 and 4 having the highest number of peaks in common with those produced with template 1 ( or otherwise showing the best match with the output trace produced with template 1 ) is then selected for each electrogram . this selected output trace is compared with template 1 , by using an “ and ” operator , for example , as discussed above . the same four templates can be used for electrograms produced from different patients . for example , an electrogram from patient 1 is processed with templates 1 - 4 , in the manner described above . the output trace from template 1 is then compared with the output traces from the other templates . in this example , the correlated output trace from template 4 is selected on the basis that it is the best match to the output trace from template 1 . thus , the outputs from template 1 and template 4 are compared for patient 1 , and the corrected trace output from this comparison is used for analysis of data collected from patient 1 . in a further example , the output trace from template 2 best matches the output trace from template 1 for patient 2 . therefore these output traces are compared for patient 2 . thus , the series of 4 templates can be used to tailor the noise reduction in electrograms for a variety of patients . considering again the step of high - pass filtering the dft signal 112 , filtering an electrogram in this manner may be used to determine abnormalities in the conduction substrate of the heart muscle , since if a signal passing through myocardium is subject to delayed conduction ( as discussed above ) a small , distinct activation wavefront may be incident on an output electrode , causing a small peak in the electrogram . this peak may be superimposed on part of a larger peak , or in another area having high gradient , so it may be difficult to see . if the electrogram having such a small peak is filtered with a high - pass filter these small peaks will become more noticeable . since the peaks are indicative of a delayed conduction through myocardium , analysis of these peaks may be used to determine information about the conduction paths and / or discontinuous conduction paths through the myocardium . aspects of analyzing a paced electrogram in order to determine a cardiac condition will now be described with reference to fig1 . as mentioned above , each of the peaks of an electrogram 140 can be plotted on a graph 142 according to their delay from a given pacing signal . this is shown in fig1 for a given electrogram , where dotted lines show the delay having been determined for each of the peaks . each of these peaks is represented as points on the graph 142 , the points having an x co - ordinate according to the s1s2 coupling interval at which the electrogram 140 was taken . fig1 shows the peaks of three electrograms 150 a , 150 b , 150 c , plotted as a function of delay against s1s2 coupling interval . this gives a “ conduction curve ”, i . e . a graphical representation of the speed of conduction though the heart at different s1s2 coupling intervals . fig2 shows four examples of conduction curves plotted over a range of values of the s1s2 coupling interval . thus , graphs of the s1s2 coupling interval ( x axis ), plotted against time delay for the signal to reach the other electrodes ( y axis ), can be plotted for different patients . such a representation is referred to as paced electrogram fractionation analysis ( pefa ); in the present specification this is also referred to as a conduction curve . pefa graphs for patients with a history of vf show increasing fractionation ( more horizontal lines are shown on the graph ): the maximum delay increases as the coupling interval gets shorter ; the spread of delays is greater and the spread increases as the coupling interval gets shorter ; and the verp is higher . these latter characteristics of the graph seem to be due to the fact that as the frequency of the pulses increases , an increasing number of cells will be refractory when the signal reaches them ; as a result , progress of an activation front , such as the activation front 22 shown in fig1 to 8 will be delayed as described above with reference to fig1 to 8 . a known method of analysis of a conduction curve will now be described with reference to fig2 and 22 . fig2 shows a conduction curve on which various parameters are marked . the parameter s1s2 delay indicates the value of s1s2 at which the fractionation begins to increase , and δed is a measure of an increase in delay over a given s1s2 interval . in this example δed relates to the increase in fractionation between an s1s2 of 350 ms and 5 ms above the verp . results of the values of δed against s1s2 delay for a number of different conduction curves , constructed using electrograms from different patients , can be represented graphically , and such a graph is shown on fig2 . the points presented on fig2 identify the type of patient to which the results relate . it is possible to construct a line , such as the line a , which enables a basic analysis of the risk of vf . this line a is defined as a linear quantity , known as “ discriminant line ”, indicative of a basic vf risk . for convenience this line is defined as a “ fractionation ” of 60 ms . it should be noted that the quantity fractionation having the units of time , is different from what is meant by the term fractionation above ( this being indicative of the splitting of the lines shown on a pefa graph ). if a point ( and thus patient ) lies to the right of the line , the patient corresponding to the point tends to be at greater risk of vf , and if a point lies to the left of the line , the corresponding patient tends to be at less of at risk of vf . as mentioned above , lqts patients show a longer qt interval ( i . e . the interval between the qrs complex and the end of the t wave in an electrogram ), and in order to distinguish between vf caused by hcm and lqts the present inventor has developed a novel analytical technique based upon the representation of fig2 . fig2 shows a schematic version of the graph of fig2 , showing how each point is analysed to determine the linear quantity fractionation assigned to each point , according to this technique . referring to fig2 , the technique involves constructing a normal b to the line a ; each point is then projected onto the normal , and its distance from the line a along the normal is determined . thus , the distance of each point from the line a , measured normally to line a is determined . this distance represents the fractionation of that patient , relative to the risk threshold of 60 ms of fractionation . since the line a has been defined as a single quantity indicative of risk , the distance of a point from line a , i . e . the fractionation of a point , can be used to determine whether other factors show a correlation in the likelihood of vf . for example , since patients having lqts show a long qt interval it may be that there is a correlation with the length of the qt interval with the risk of vf . in an attempt to identify risk of vf for patients with qt , the inventor has investigated the relationship between fractionation and the qt interval , corrected according to the rate of the heartbeat , according to bazett &# 39 ; s formula : where qtc is the qt interval corrected for rate , and rr is the interval from the onset of one qrs complex to the onset of the next qrs complex , measured in seconds . such a relationship is shown in fig2 . surprisingly , the graph shown in fig2 does not show a significant correlation of vf risk with qtc , since the vf patients are scattered vertically . therefore the inventor has developed a further novel analysis technique , involving quantification of rate of increase of delay and being based upon conduction curves , such as those introduced with reference to fig2 . an example of how the rate of increase of delay can be measured will now be described with reference to fig2 , which shows conduction curves of an lqts patient , and an hcm patient . two straight lines 200 a , 200 b and 210 a , 210 b are matched to the upper line of the respective conduction curves ( these lines are shown as dotted , and are elevated for clarity ) by means of , for example , piecewise fitting and continuous analysis of the straight lines 200 a , 200 b and 210 a , 210 b . the angle α between the two lines for each graph is measured , and it can be seen from inspection of fig2 that the angle for lqts patients differs from that associated with hcm patients . fig2 shows further examples of conduction curves from lqts vf and lqts patients . as can be seen from this figure , there is a variation in conduction curves from patients having the same disease . in particular , a large variation can be seen between the three conduction curves for the hcm patients . these graphs illustrate that it can be non - trivial to identify a cardiac condition from merely looking at the graphs without performing an analysis . once the values of the angle α have been determined they are compared with a known value . this known value may be calculated from a sample population of patients present in a certain experiment , for example , or may be pre - determined . in the current example an average of the values of α for different patients is taken , the values of α for each of these patients is shown on a scale , and the patients are identified by whether they have hcm or lqts , such as the scale shown in fig2 . as can be seen in fig2 , there is a clear difference of the values of α in hcm patients and in lqts patients , indicative of the fact that this method of analysis is useful for determining vf risk in relation to the cardiac condition of the patient . this process can be automated , or parts of the process can be automated , and other parts can be performed manually . the value of α gives a measure of danger of vf in a patient , as does the value of the quantity of fractionation associated with that patient which can be identified using prior - art methods . however , a gives information over and above that which can be obtained merely from a consideration of the quantity of fractionation . for example , if a graph were plotted having the quantity fractionation on the abscissa and the value of α on the ordinate for a number of different vf patients , the points would be grouped together horizontally , indicating that the patients have similar values of the quantity of fractionation , but they would be spread vertically , and the points relating to patients having the same cardiac conditions would tend to clump . this vertical spread could also be measured . in addition to the methods discussed in relation to fig2 and 27 an analysis of the rate of increase of delay over a given range of s1s2 coupling could be measured by using the absolute angle α between the two piecewise continuous lines . in this case , values of α below approximately 150 ° indicate lqts , and values of α above approximately 150 ° indicate hcm . more specifically , angles between approximately 115 ° and 135 ° indicate lqts and angles between approximately 155 ° and 170 ° indicate hcm . the conduction curves can be analyzed using a single line passing through the points , and measuring the angle between this angle and the axis . as a further alternative or additionally , a measurement of the gradient can be taken , and differences in the gradient at different values of s1s2 coupling interval can be determined . the value of a for dcm is similar to that of hcm , and the value of a for chf is roughly in between the values corresponding to hcm and lqts . fig2 shows a system comprising a computer system or processing system 300 for use in performing the analysis described in relation to fig2 and 27 . the system comprises a signal generator 302 connected to an input electrode 304 . three output electrodes 306 a , 306 b , and 306 c ( in this example ) are connected to an input unit 308 of the processing system 300 . the input unit 308 is connected to a storage unit 310 , which is in turn connected to a cpu 312 . the cpu 312 and the storage unit 310 are connected to an output unit 314 . in use , the input electrode 304 and the three output electrodes 306 a , 306 b , 306 c are inserted into a heart 316 , as shown , and the signal generating means 302 generates a pulsed input signal , which passes to the input electrode 304 , and to the heart 316 . the output electrodes 306 a , 306 b , 306 c detect the output signals , which are sent to the input unit 308 of the processing system 300 . the detected signal can then be stored in the storage unit 310 , and passes to the cpu 312 . the cpu 312 filters the signal , and may apply a series of templates to the signal to further filter the signal , as discussed above . the cpu 312 then performs the analysis discussed above , and sends the results of the analysis to the output unit 314 . as shown in fig2 the constituent parts of the processing system 300 can be configured as separate units or as a suite of software running on respective units . alternatively , the processing system 300 may comprise a single unit , having similar functionality . furthermore , fig2 shows three output electrodes , whereas more or fewer electrodes could be used . the “ fractionation ” value , discussed above in relation to fig2 and 23 can be used alone as a guide to the risk of an individual to vf . some graphs of fractionation value for various groups of patients are shown on fig2 to 33 . fig2 shows the fractionation values of a control group compared with an scd group and fig3 shows the fractionation of a non - scd group compared with an scd group . fig3 shows the fractionation of groups having various cardiac conditions , namely hcm , lqts , and dcm compared with each other and with a non - scd group . fig3 shows the graph of fig3 with a line indicative of a fractionation of 60 ms . further , fig3 shows a graph of a vf group compared with a non - vf group , with a line showing a fractionation of 60 ms . this latter figure represents a prospective study , i . e . the patients were first studied , and subsequently followed to determine whether any displayed symptoms of a cardiac condition . thus , these figures show the fractionation exhibited by the hearts of various patients relative to the fractionation of 60 ms . as discussed above , this gives a basic linear measurement of risk , and a fractionation of 60 ms is indicative of the threshold for risk ; a patient exhibiting a fractionation above 60 ms is likely to be prone to vf . as described above , the experiments were conducted by starting the stimulation at a relatively long s1s2 coupling interval , and decreasing the coupling interval . however , the experiment can be conducted by starting with a relatively short s1s2 interval , for example , below 250 , 300 or 350 ms and increasing the coupling interval to above 350 ms . this approach has the advantage that the potential effects of abnormal blood flow to the heart on the experimental results could be eliminated . this is due to the fact that starting with a long s1s2 interval and decreasing this may lead to ischemia , or a lack of blood flow to the heart , after a period of time . this means that the results for a shorter s1s2 interval , which are produced after the heart has been subjected to the stimulation for some time , may be affected by effects to do with the blood flow . running the experiment starting with a short s1s2 interval minimises this effect , since the data for the delay at short s1s2 in this case is collected before the blood flow to the heart is significantly affected by the applied stimulus . this would confirm the conclusion that the characteristics of the graph , such as increased delay and increased fractionation at short s1s2 intervals , is a electrophysiological effect , rather than being blood - flow related . as described above , the pulsing sequence used in the experiments comprises an s1s1 pulse at a constant coupling interval , followed by a premature extrastimulus pulse , s2 , where the s1s2 coupling interval is variable . other pulsing sequences can be used . for example , a pulsing sequence having a further premature extrastimulus pulse , s3 , can be used . in this case , the s1s2 coupling interval could be kept constant , and / or the s1s3 coupling interval could be kept constant , while the coupling interval of the other extrastimulus varies . the advantage of including a further premature extrastimulus is that the heart becomes stressed sooner . this has the advantage that the effects associated with a risk of vf may be seen sooner , or at a longer premature stimulus coupling interval . in place of a conduction curve , such as the one shown in fig1 above , a conduction surface can be plotted . for example the conduction surface could be plotted having delay on the z axis , s1s2 coupling interval on the x axis , and s1s3 coupling interval on the y axis . the conduction surface can be analysed in a similar way to the conduction curves of fig2 , as follows : a plane or a series of planes , parallel to respective planes normal to the surface can be constructed . a line of intersection between respective planes can then be determined . an analysis of the properties of this line ( such as the equation of the line , or the gradient of the line , or where two or more lines cross , if there are more than one line ) can be made in order to identify a cardiac condition . further , it is possible to increase the duration of the pulsing sequence , so that more pulses are applied to the heart . this stresses the heart , which may lead to effects associated with vf being shown at longer s1s2 coupling intervals , which improves the safety of the experiment . post - coronary patients , i . e . patients who have had a heart attack , for example a coronary thrombosis which has lead to myocardial infarction ( ami or mi ) and scarring of the muscle tissue of the heart , may also be at risk of vf . however , in such patients the scarring or other damage caused to the muscle tissue is generally localized ; this is different to the damage associated with hcm or lqts patients , where the heart muscle tissue instead suffers diffuse damage . this means that electrograms for hcm or lqts patients can be produced by looking at the left ventricle , for example , and this electrogram can be taken as representative of the rest of the heart . in post - coronary patients the electrogram includes a measurement taken both from the area affected by the heat attack , and from a non - affected region . therefore , the electrodes can be positioned as discussed above , i . e . having three output electrodes positioned in the right ventricular septum , the inferior wall of the right ventricle and the right ventricular outflow tract , together with an electrode positioned in a region which has been damaged following the coronary thrombosis . alternatively the electrode in the region which has been damaged may replace one of the other output electrodes . the above embodiments are to be understood as illustrative examples of embodiments of the invention . it is to be understood that any feature described in relation to any one embodiments may be used alone , or in combination with other features described , and may also be used in combination with one or more features of any other of the embodiments , or any combination of any other of the embodiments . furthermore , equivalents and modifications not described above may also be employed without departing from the scope of the invention , which is defined in the accompanying claims .
a system for use in analysis of electrograms comprising : an input signal generator ; an input electrode for applying an input signal to a driving region of a heart ; an output electrode for receiving an output signal at a driven region of the heart ; a processing system operable to receive signals indicative of said recorded value from the output electrode for analysing conduction paths through the heart , wherein the signal generator is operable to generate an input signal comprising a plurality of pulses , being spaced from each other by a pacing interval ; and the processing system being arranged to identify signal delay between the input signal and the output signal on the basis of the signal received by the output electrode in relation to the plurality of pulses , and to identify a rate of variation in signal delay over a range of values of pacing interval .