with reference to fig1 - 6 ; one will now describe a preferred embodiment of a device in accordance with the present invention . regarding the software - related aspects thereof , the functionality and processes of the present invention can be implemented by an appropriate programming of the software of a known enslaved ( rate - responsive ) pacemaker . the invention can preferably be applied to the commercial implantable devices marketed by ela medical , montrouge , france , such as the symphony and rhapsody brand pacemakers and comparable commercial and / or proprietary devices of other manufacturers . these devices are equipped with programmable microprocessors , including circuits intended to acquire , format and process electrical signals collected by implanted electrodes and various sensors . 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 and functionality of the invention , described in more detail below . implementing the features of the invention into these devices is believed to be easily feasible by a person of ordinary skill in the art , and will therefore not be described in detail in this document . as shown in fig1 , the device comprises a sensor 10 providing a signal that is representative of the patient &# 39 ; s metabolic demand , typically a transthoracic impedance signal , the analysis of periodical variations thereof ( amplitudes and successive periods ) being performed by block 12 that provides a minute ventilation ( mv ) signal . the device also comprises a physical sensor allowing to sense the patient &# 39 ; s movements , typically an acceleration ( g ) sensor 14 associated with a sampling circuit 16 providing a succession of digitized samples g i with a step i = 125 ms for instance . based upon concurrently provided mv and g information , the device performs an enslavement of the “ dual sensor ” type ( block 18 ) as described in the aforementioned ep a 0750920 and ep a 0919255 and their us counterpart u . s . pat . nos . 9 , 722 , 991 and 6 , 336 , 048 , which disclosures are hereby incorporated herein by reference in their entirety , preferably a control of pacing rate and eventual adaptation of operating parameters . that enslavement function is not per se part of this invention , yet is discussed in the incorporated references and so will not be described in detail in this document . however , the enslavement algorithm has the advantage of comprising a discrimination function , between phases of activity and phases of rest of the patient ( block 18 ), based upon instantaneous indications from mv and g sensors , resulting in a status indicator 20 able to take at least two values of “ activity ” and “ rest ” ( some other values being possible , for example , “ recovery after exercise ”, that will be assumed to be an activity phase , or “ sleep ” which is a particular case of rest phase ). this invention proposes to store the data provided by mv and g sensors into the device memory , in distinct ways for activity phases and rest phases . more precisely , the device comprises a first memory 22 gathering the characteristic parameters in activity phase , notably : the time elapsed in activity phase over the last 24 hours ( t act ), the sum of the measurements of g sensor in activity phase , weighted over the last 24 hours ( σg i / t act ), and the mean minute ventilation ( mv act ) over the activity phases , measured over the last 24 hours . fig2 shows more precisely the manner that the data σg i is being obtained and updated . as stated above , the g sensor provides a series of digitized samples g i with step intervals of i = 125 ms for example , of which the variations over time is shown in fig2 . also , the status indicator 20 allows to distinguish between activity phases ( act ) and rest phases ( rep ). the device proceeds to the summing of the values g i of the samples over the last 24 hours , but inhibits that summing during rest phases , thus only summing values corresponding to periods of activity . a weighted value is obtained by dividing the total of that summing ( over the last 24 hours ) by the duration of the activity phase t act . fig3 shows an example of the variations of this parameter mv act over time for a duration of several weeks : the crosses indicate values that are computed everyday , and stored into the memory 22 , the full line representing a moving average over 7 days . the device further comprises a second memory 24 gathering the characteristic parameters in rest phase , notably : the time elapsed at rest over the last 24 hours ( t rep ), and the average minute ventilation ( mv rep ) during rest phases , measured over the last 24 hours . the parameter mv rep is an average of minute ventilation mv during rest periods . these various information , memorized and updated in the two arrays of values 22 and 24 are then subjected to an analysis ( block 26 ) applying a certain number of inference rules providing a series of clinical indices , such as an index 28 related to the acceleration ( index g ), an index 30 related to minute ventilation in activity ( index mv act ), an index 32 related to minute ventilation at rest ( mv rep ), etc . that analysis is performed periodically , for example , each day , with a daily update of the various clinical status indices . some different methods of analysis are possible . a first method involves comparing the stored values with various fixed references , and detecting the overshoot of low and / or high thresholds . one other advantageous method that will be described in detail below concerns analyzing the variations of data from one day to the following day ( or over any other periodic interval , such as from one week to the following week , etc .) and comparing that variation to a reference value corresponding to a physiologically admissible variation , taking into account the clinical status of the patient . the overshoot of that limit will reveal the degradation of the patient &# 39 ; s status in view of the corresponding criterion ( activity , basal ventilation , etc .). one will now describe in detail for example the inference rule corresponding to the criterion g , the rules corresponding to the other criteria being applied similarly . the memory 22 containing the parameters related to activity phases contains a series of values somme_g ( i ), the step i corresponding here to a time interval of one day . rather than applying the inference rule to the isolated daily value , one preliminarily calculates a moving average over one week , so as to weight the variations of activity from one day to the next , that average can be expressed by following formula : the rule related to the parameter g will be referred to as “ rule r 1 ”, and the result of its application will be an indicator likely to endorse three values , representative of the evolution of the patient &# 39 ; s clinical status in view of that parameter g : + 1 ( improvement ), − 1 ( degradation ) and 0 ( stability ). indeed , the increase of activity is a favorable element in the patient &# 39 ; s clinical array , which justifies the score + 1 , while a reduction of the patient &# 39 ; s activity is conversely a unfavorable on a long term viewpoint . if m 7 g ( i )& gt ; m 7 g ( i − 6 )* α ( m 7 g ( i )), then r 1 ( i )=+ 1 ; if m 7 g ( i )& lt ; m 7 g ( i − 6 )* α ( m 7 g ( i )), then r 1 ( i )=− 1 ; α ( m 7 g ( i )) being a percentage calculated based upon a transfer function that is depending upon the current value of the considered criterion . the purpose of the a factor that is not constant is related to the need for taking into account the previous results , in order to qualify the improvement or degradation of the result : hence , for a patient with a very low activity , an increase of activity , even modest , will be a very favorable element , which is not necessarily the case for a patient with a sustained activity and a regular increase . fig4 shows the transfer function providing the admissible increase rate ( hereafter referred to as parameterthreshold ) as a function of the value of considered parameter . in the above referred - to example of parameter g , the transfer function is defined by the following formulae : a , b being the slope and intercept point of the straight line defined by the points ( low_ref , lower_limit ) and ( high_ref , higher_limit ). the principle of that transfer function is also applicable , mutatis mutandis , to the elapsed duration in activity t act . however , as for the ventilation parameter , in activity ( mv act ) or at rest ( mv rep ), the direction of the variation of the transfer function shall be reversed , as shown on fig5 . indeed , in the case when a patient has a high rest minute ventilation , even a very low increase may strongly impede him , though a patient with a lower ventilation still has a high margin of increase . the results thus obtained through the application of various rules may be subjected to a more thorough diagnosis , through the application of meta - rules allowing to define one or more higher - level indices , or “ meta - indices ”. those meta - indices are determined ( block 34 ) through retrospective analysis of the successive values taken by the different indices . those meta - rules may be built up through several ways , and only one example thereof will be provided in the following , namely defining a limit over several days ( p days ) over which a rule r has to provide a minimum number of positive values for the corresponding index ( that minimum being parameterized to the mr_threshold value ): meta - rule mr ( one value + 1 of the meta - index being representative of a significant aggravation of the patient &# 39 ; s status ): that meta - rule takes into account the fact of having null or negative index values : indeed , null values do not increase the value mr ( i ) of the meta - index , whereas negative values have the effect of “ compensating ” the positive values lately taken . thus one or many meta - indices are obtained : 36 , 38 , . . . reflecting the evolution of one of the parameters g , mv act , mv rep , . . . or a combination of these various parameters . everything can be viewed or displayed , for example , in order to allow a practitioner to quickly seize the evolution of the patient &# 39 ; s clinical status and assess the risks of sudden aggravation of that status . a representative example of such a display is proposed on fig6 . each unit of abscissa corresponds to one day , and the determined values for the following are displayed for each day : the index related to the activity g ( resulting from the application of the rule r 1 and represented by a “ x ”), the index related to ventilation during activity mv act ( resulting from the application of the rule r 2 represented by an “ o ”), and the index related to the ventilation at rest mv rep ( resulting from the application of the rule r 3 and represented by a “+”). for the sake of display clarity , and in order to prevent any risk of overlay , the marker corresponding to the value ± 1 of rule r 1 was placed at the abscissa ± 1 , the marker corresponding to the value ± 1 of rule r 2 at the abscissa ± 2 and the marker corresponding to the value ± 1 of rule r 3 at the abscissa ± 3 . it can be seen on the illustrated example , that the rule r 2 related to the ventilation in activity , shows an aggravation of the patient &# 39 ; s status ( increase of the ventilation in activity ) on the dates 14 . . . 19 , 37 , and 48 . . . 50 , an improvement of the patient &# 39 ; s status ( lowering of ventilation in activity ) on the dates 57 . . . 59 , and a stationary status elsewhere . these results shall be compared with those illustrated on fig3 , that present a variation of the parameter mv act day after day . the display also provides the results of the meta - rules , with the meta - indices mr 1 and mr 2 : the marker corresponding to the value + 1 of meta - rule r 1 was placed at abscissa + 4 , the marker corresponding to the value + 1 of meta - rule r 2 was placed at abscissa + 5 . the meta - index mr 1 results for example from the application of a meta - rule to rest ventilation , while the meta - index mr 2 results from the application of a meta - rule combining the activity level with the activity ventilation . thus , on dates 44 . . . 50 , the meta - index mr 1 signals an aggravation of the patient &# 39 ; s status related to rest ventilation , provided the index r 3 took the value − 1 over at least 5 days out of 7 . some alarms may be triggered as a function of the results given by the meta - rules ( and also by the rules ). these alarms allow to anticipate the occurrence of an event such as heart failure , so as to take necessary measures with no delay in order to prevent the occurrence of this crisis or , at least to reduce its effects . that diagnosis can thus be done very early , before any serious symptom appears and therefore well before the patient has called his practitioner or visited an emergency room department . one skilled in the art will appreciate that the present invention may be practiced by other than the foregoing embodiments , which are presented for purposes of illustration and not of limitation .
an active implantable medical device or pacing , resynchronization defibrillation and / or cardioversion , and / or a device for diagnosing patient conditions , having a predictive diagnosis of the patient &# 39 ; s status . the device measures a physiologic parameter , notably the minute ventilation ; measures a physical parameter , notably the acceleration ; discriminates between phases of activity and rest of the patient ; and includes a memory containing a plurality of fields selectively updated by statistical processing . these fields are comprising one first set containing data related to the patient &# 39 ; s activity phases , and one second set containing data related to the patient &# 39 ; s rest phases . the statistical processing is updating in a dissociated manner the first and second sets of fields , selectively as a function of the value taken by the status indicator , and the analysis evaluates at least one clinical status index based upon the data contained in the fields of both first and second sets .