one will now describe a preferred embodiment of the invention with reference to the drawings fig1 - 7 . the present invention can be preferably implemented by an appropriate programming of the software of a known enslaved pacemaker . suitable devices include those implantable devices marketed by ela medical , montrouge , france , such as the symphony and ela rhapsody brand pacemakers . these and other similar devices are equipped with programmable microprocessors and memory , including circuits intended to acquire , format and process electrical signals collected by implanted electrodes and various sensors and store ( memorize ) such signals and data . it is also possible to upload towards these devices , by telemetry , pieces of software that will be stored in internal memory and run so as to implement the features of the invention , described in more detail below . implementing the features and functionality of the invention into these devices is believed to be within the abilities of a person of ordinary skill in the art , subject to the variations of implementation that such persons may employ , and will therefore not be described in detail in this document . with reference to fig1 , a device according to a preferred embodiment of the present invention includes a sensor 10 ( characterized as a minute ventilation sensor ( ve ) providing a signal representative of the patient &# 39 ; s metabolic needs , typically a transthoracic impedance signal . an analysis of the periodic variations ( amplitudes and successive periods ) is performed by stage 12 , which provides a minute ventilation measurement signal ( ve ). the device also includes a physical sensor that detects the patient &# 39 ; s movements , typically an acceleration ( g ) sensor 14 associated to a sampling circuit 16 that provides a succession of digitized samples , having a sampling step of 125 ms , for instance , providing an acceleration signal ( g ). based upon the information ve and g concurrently provided , the device is thus operating an enslavement of the “ dual sensor ” type as described in european patent ep 0 , 750 , 920 and its counterpart u . s . pat . no . 5 , 722 , 996 , and european patent ep 0 , 919 , 255 and its counterpart u . s . pat . no . 6 , 336 , 048 cited above , which patents are incorporated herein by reference in their entirety , and more preferably provides an enslavement of the pacing rate and eventual adaptation of operating parameters . that enslavement function as such is not part of the present invention though , and will therefore not be described further into details . nevertheless , the enslavement algorithm has the advantage to provide a function of discrimination between a patient &# 39 ; s activity and rest phases ( stage 18 ) based upon the instantaneous information delivered by ve and g sensors 10 and 14 , resulting in a status index able to take at least two values : “ activity ” (“ act ”) and “ rest ” (“ rest ”). it should be understood that other values are also possible , for example : “ recovery after exercise ,” which will be assimilated to an activity phase , or “ sleep ” which is a particular case of rest phase . for the implementation of the invention , the device is collecting , in a distinct manner for activity phases and rest phases , the data provided by the ve and g sensors 10 and 14 , and is memorizing , based on said data : the sum w ( i ) of the measurement of sensor signal g in activity phase , the average minute ventilation veact ( i ) during activity phases , measured over the last 24 hours , and the average minute ventilation verest ( i ) during rest phases , measured over the last 24 hours . fig2 shows more precisely the way w ( i ) is obtained and updated , that datum being representative of the cumulative effort developed by the patient over the last 24 hours : as said above , the sensor g is providing a series of digitized samples g , for example , with sampling intervals of 125 ms , the variation of which over time is illustrated on fig2 . also , the status indicator allows to distinguish activity phases ( act ) and rest phases ( rest ). the device operates a summing of the values of the samples acquired over the last 24 hours , but inhibiting said summing during rest phases , and therefore only summing values corresponding to periods of activity . the parameter veact ( i ) is an average value of minute ventilation ve during activity phases . the parameter verest ( i ) is an average value of minute ventilation ve during rest phases . for these two parameters , the obtained average is weighted by dividing it by the duration spend in activity or rest phases , respectively . typically , these various parameters : w ( i ), veact ( i ) and verest ( i ) are evaluated and memorized on a periodic basis , preferably a daily basis , from the first day i = 1 following the implantation of the device or the last visit with the practitioner , until a duration that can typically reach around i = 90 days , in the case of quarterly routine visits . based upon the raw values w ( i ), veact ( i ) and verest ( i ) thus determined and memorized , the device then calculates ( stage 20 ) a moving average over 7 days w ( i )/, veact ( i )/, verest ( i )/. advantageously , these averages are also subjected to a low - pass filtering of 2 nd order ( or any other smoothing algorithm of the low - pass type ), in order to smooth rough variations thereof , and to let only the slowest evolutions appear . the slowest evolutions are the variations that are believed to be significant from a long - term viewpoint . fig3 shows as a representative example , the variations of the parameter veact ( i ) over time for a duration of several weeks . the crosses show the values veact ( i ) calculated daily , the full line representing the moving average veact ( i ) over seven days , also calculated daily . the following step ( stage 22 ), characteristic of the invention , is a segmenting step , which models each of the three curves w ( i )/, veact ( i )/ and verest ( i )/ in the form of a continuous succession of line segments whose endpoints are points of the curve under consideration . the modeling is preferably performed by an application of the method of multiple linearizations as described below , with reference to fig4 . starting from an initial point p ( i ) located at an abscissa i ( i ≧ 7 , the averages of the very first days being not significant ), one considers a point p ( j ) at an abscissa j ( j & gt ; i ). that point defines on curve c a chord p ( i ) p ( j ), that is : a segment s 1 . one then applies a regression formula consisting of calculating , for all points on curve c between p ( i ) and p ( j ), the sum of the absolute values of the successive variances e between curve c and segment s 1 . one then increases j , i . e ., one is moving point p ( j ) towards the straight line , until the sum calculated based upon the regression formula reaches a first given threshold value ( the first threshold value proper to each of the three curves , is predetermined or programmable and has been previously evaluated based upon results of clinical studies ). the threshold value cannot be “ defined ” as standard value , because the aim of this threshold is to be adapted by the physician - practitioner when looking the result of linearization . for each period of follow - up ( and also each patient ) the same threshold may “ divide ” a curve onto various number of segments . the threshold therefore may be considered as a “ zoom ” value . for example , given a curve over n days , there is a threshold value a , which converts the curve onto 1 single segment , and a value b ( b & lt ;& lt ; a ) for which the curve will be converted into n different segments . a threshold value between a and b then will convert the curve into multiple lines , between 1 and n , corresponding to more or less “ precision ” according to the selected threshold . it has been found that if you have changes in the slope of more than 5 to 10 %, it defines a new period for ventilation , and similarly for activity sensor a change of around 10 % implies a new period . it should be understood however , that these selected thresholds are not medical or physiologic values , but rather only mathematic values useful to linearize the curves . as soon as one finds a value j for which the threshold is exceeded , one considers that the corresponding point p ( j ) in principle accounts for the second endpoint of the segment , said segment covering the period [ i , j ]. however , one additional criterion is applied , by operating an additional verification for all successive points comprised between p ( i ) and p ( j ): if the distance between the segment p ( i ) p ( j ) and curve c is , for one given point , higher than a second predetermined threshold , then one replaces p ( j ) by that new point p ( k ). the considered segment then becomes the segment p ( i ) p ( k ), instead of segment p ( i ) p ( j ). in other words , if for a given point p ( k ) the variance between the curve and the initial segment ( resulting from the application of the regression formula ) is higher than a given limit , then one will use as the new point , that corresponding to the maximal variance . indeed , the purpose of the segmenting operation is to let “ remarkable dates ” appear , corresponding to junction points of successive segments , and avoid an excessive smoothing of the curve that would tend to eliminate such remarkable dates . thus , on fig4 , segment s 1 initially considered , is replaced by segment s 2 , thereby revealing the date k , corresponding to point p ( k ), as a remarkable date , meaning it has an unusual characteristic ( it has a value that is too different from the linearization ; the “ error ” done by linearization s 1 is too large compared as the real curve ) as compared to the evolution of the measurement . the segmentation algorithm is then reiterated until the end of the curve : the point p ( k ) is in turn used as a starting point for the algorithm , which searches for a segment s 3 between p ( k ) and a point p ( l ) likely to fulfill the criteria described above , and so on . at the end of the process , one obtains for each of the three curves w ( i )/, veact ( i )/ and verest ( i )/ a series of successive segments , joined to each other around successive remarkable dates . fig5 illustrates a representative shape of a portion of such a characteristic after segmenting . the following step ( stage 24 ) calculates , for each of the three segmented curves and for each of the periods between two remarkable dates , an index hereinafter referred to as “ specific index ”. the specific index is preferably determined based upon the slopes of successive line segments , by comparing each slope value to two limit values , positive and negative , referred to as “ variation sensitivity ”. if the slope is comprised between these two values , it is considered as stable , and the index “ 0 ” is allocated to corresponding segment . otherwise , one allocates an index ‘+ 1 ’ or ‘− 1 ’ if the slope is positive or negative respectively . fig5 shows as an example , the variations of the specific index for the series of segments of the curve . in other words , after defining a certain number of remarkable dates t 1 , t 2 , t 3 , t 4 , . . . through segmenting , one determines an index that reflects the variation of the parameter under consideration , between two successive remarkable dates : low or no variation ( specific index =‘ 0 ’), weighty increase ( specific index =‘+ 1 ’) or weighty decrease ( specific index =‘− 1 ’). these data are obtained for each of the three parameters w , veact and verest , but with remarkable dates that are not necessarily the same for each of them . the following step ( stage 26 ) determines a synthetic index , hereinafter referred to as “ composite index ”, based upon various specific indices and remarkable dates . the composite index is determined based upon a truth table combining all possible values of the three specific indices determined above . this truth table , of which an example is given in fig6 , is established as a function of medical advices and results of clinical studies , and is supposed to reflect the relative weight of the various parameters when they are combined with each other . the truth table allocates , for each day , a value ‘− 2 ’, ‘− 1 ’, ‘ 0 ’, ‘+ 1 or ‘+ 2 ’ to the composite index . the value allocated is therefore likely to change every time a remarkable date has been detected for one of the three parameters w , veact and verest . advantageously , changes to the composite index are transposed into a graphical format ( stage 28 ), through a step that can be executed by the programmer of the practitioner in charge of visiting the patient , once the programmer has retrieved from the device memory , the data to be displayed . fig7 , as an example , shows such a graphical display , that can be preferably presented in the form of a colored bar with different colors that are function of the index , for instance : dark green → light green → white → yellow → red for the respective values of the index + 2 , + 1 , 0 , − 1 , − 2 . the graphical code is advantageously displayed on a screen simultaneously with each , or only one , of the three curves w ( i )/, veact ( i )/ and verest ( i )/ as shown on fig7 . the graphical display allows the practitioner , for example , to immediately identify a period of general degradation ( in red ), determine the remarkable dates corresponding to that evolution , and eventually perform a more precise diagnosis by examining the variations of w , veact and verest and / or other parameters memorized by the device during the period under consideration . for example , on fig7 , one can see that the episode of aggravation comprised between t 1 and t 2 is very probably related to the factor veact ( t 1 and t 2 are remarkable dates for only veact ), whereas the episode of serious aggravation comprised between t 6 and t 7 is not related to this factor ( t 6 and t 7 are not remarkable dates for veact ), but probably to variations of the other factors , not shown on this drawing . hence the practitioner will immediately recognize , for the period under consideration , precise time marks that will help in formulating a diagnosis and in interrogating the patient . one skilled in the art will appreciate that the present invention can be practiced by other than the described embodiments , which are presented for purposes of illustration and not of limitation .
an active implantable medical device , preferably a device for pacing , resynchronization , defibrillation and / or cardioversion of a patient , that includes functionality that assists in the diagnosis of the patient &# 39 ; s clinical status . this devices comprises circuits for measuring one physiologic parameter , preferably minute ventilation , and circuits for measuring a physical parameter , preferably acceleration , control logic for discriminating between activity and rest phases of the patient , and analysis circuits , to process and combine these signals and memorize the obtained results in the form of a data history . the analysis will establish characteristics providing , for successive dates , representative values , for a given period of time , of the physical signal and physiologic signal during activity phases of the patient , and / or of the physiologic signal during rest phases . these circuits search for remarkable dates for each of the characteristics and allocate specific indices of clinical status respective to each of the periods comprised between the remarkable dates , then combined the specific thus obtained into one single composite index .