the origin of the invention lies in the creation of a fast and reliable algorithm that can be implemented with limited data - processing resources by operating the modeling of the physiological signal by a decomposition in “ bumps ,” a bump being a well - known mathematical concept that , until now , has never proposed to be applicable to the analysis of a physiological signal . a bump , as illustrated in fig2 is a monotonic function that is parameterized such that it is defined by three successive intervals , a first increasing parameterized function g 1 , an affine function d , and a second decreasing parameterized function g 2 . according to the invention , the device for analyzing seeks to compose a set of bumps such that the graph of the sum of these bumps is as similar as possible to a known monodimensional signal . the optimization of the parameters of each bump is operated by any suitable mathematical method making it possible to obtain a graph as close as possible to the original signal to be modeled . on fig4 one thus illustrates , in the particular case of one beat of an ecg signal : a ) a set of bumps , in this example , elaborated in a manner that is described below . c ) the original signal which , as one can see it , is very close to the modeling obtained in ( b ). advantageously , one uses a particular type of bump , illustrated in fig3 that is derived from the general definition given above in reference to fig2 . this particular type of bump is defined by a reduced number of parameters ( five in this case ) and , in practice , the obtained modeling proves extremely reliable and close to the original signal in the case of the analysis of a ecg signal , notwithstanding the reduced number of the parameters in the definition of the bump . for this purpose , one uses as affine part d of the bump a horizontal segment ( i . e ., a curve of null slope ) and , in order to be able to carry out the stage of adaptation of the parameters by traditional algorithms of multidimensional optimization , the monotonic functions g 1 and g 2 are half - functions ones of equal amplitude a . under these conditions , the bump is a continuous function , defined by five parameters and derivable compared to each one of these parameters , which are : 1 ) μ : temporal position , for example , the ordinate position of the medium of the segment d ; 2 ) σ1 : half standard deviation of first gaussian g 1 ; 4 ) σ2 : half standard deviation of second gaussian g 2 ; and the decomposition of the temporal signal into bumps is operated in an iterative manner in two stages : 1 ) selection of the most relevant bump by a selection algorithm applied to a set of predetermined bumps preserved in a library of bump - types stored in computer memory . this selection stage can advantageously use a method of orthogonalisation in itself known ( see , for example , j korenberg , et al ., “ orthogonal parameter estimate for non - linear stochastic systems ,” international journal of control , 48 , 193 - 210 , 1988 ). 2 ) adaptation of the bump parameters which was selected at the preceding stage , i . e ., research of the five parameters μ , σ1 , σl , σ2 , and a indicated above , so that the particular bump finally obtained approaches the original signal as much as possible to model it . this adaptation stage can advantageously use a method of nonlinear optimization under constraints , in itself known ( see , for example , minoux , programmation mathématique ( 1983 ), dunod , 1983 ). thus , modeling in n signal bumps will be made out of n time two stages . the maximum number n of bumps either is defined in advance , or dynamically adapted according to the precision of the required modeling . in the current case of an ecg signal , a heart beat modeled over five bumps proves in practice satisfactory : the limit of five bumps corresponds to the simple case where a bump represents a characteristic p , q , r , s or t wave of the ecg recording , as illustrated on the fig4 a ( fig4 b representing the modeled wave obtained by composition of the five bumps of fig4 a and fig4 c representing the original signal analyzed by the device of the invention ). the signal modeled in bumps is then analyzed to locate the characteristic waves of the cardiac activity . each bump is then allotted a label ( p , q , r , s , t or other ) according to its form and its location compared to the other bumps . one can advantageously use for this labellisation a method with hidden markov chains ( cmc or hmm , hidden markov models ), a method in itself known and described for example by l r rabiner , “ a tutorial one hidden markov models and selected applications in speech recognition ”, proceedings of the ieee , 77 ( 2 ), 257 - 286 ( 1989 ). this labeling of the bumps makes it possible to recognize the various components of a typical pqrst complex and thus to detect easily the atypical waves , which are those that , precisely , present the most interest for the diagnosis of the rate disorders . each wave of the ecg having been identified , it is then possible to measure each characteristic parameter of a given wave and — especially — to study it in a dynamic way . the curves obtained can be analyzed individually or in relation to other curves , because the modeling gives stabilized forms that allow effective correlations . one can thus analyze in a relevant way the variability of the t wave by the measurement of t wave amplitude variations and / or a temporal shift of the t wave compared to the qrs wave . one can also analyze the parameters of the bump representing the t wave , evaluate the variability of the pr interval , the amplitude of the wave p , etc . the recognition of the waves also makes it possible to withdraw a known wave from a signal . this operation is very useful to uncover a wave of low amplitude ( for example , the p wave ) which occurs in a synchronous way in relation to a wave of large amplitude , for example , the q , r , s or t waves . this automatic subtraction of a given wave from the signal makes it possible to reveal subjacent signals , for example , very early waves p , occurring just after the preceding qrs waves and which very often remained masked with the use of the prior known analysis techniques . the labellisation of the p waves provides results that will then make it possible for the doctor to improve the diagnosis considerably . the example just described for implementing the invention can have many embodiments . thus , rather than to model directly the signal collected on each ecg channel after filtering , it is advantageous to carry out , in a dynamic way ( i . e ., for each beat ), a preliminary analysis of its principal components ( hereinafter referred to as “ pca ”, or principal component analysis ). this technique , of itself known ( see , for example , i . t . jolliffe , principal component analysis , springer , 1986 ) concerns using the signals simultaneously obtained on channels x , y and z and recorded on several tracks , by seeking in a three - dimensional space a significant axis of maximum amplitude ( pca1 axis ) and by expressing the temporal variation of the beat by its projection on this principal axis , whose position is computed with each beat . this pretreatment makes it possible in particular to model only the ecg on one track , containing the maximum of information , rather than on the set of original tracks . the pca of pqrst complex ( and / or qrs complex and / or the p wave ) makes it possible moreover to obtain permanently the plan of projection of the cardiac electric signals , which plan is related mainly to the position of the heart in space . an analysis of the movement of this plan or , more simply , of the axis of the principal component ( pca1 axis ), makes it possible to obtain an image of the movement of the heart which can be used with various objectives such as : 1 ) the detection of breathing ( which has the property to move the heart with each cycle ), and the discrimination between thoracic or abdominal breathing ; indeed , the displacement of the heart , intervening in axes or in different curves , results in a modulation of one or more angles of the principal components which , analyzed using adapted mathematical tools , allows the extraction of one or more curves of the breathing , for example , abdominal and thoracic : 2 ) the detection of the position of the body , with gravity causing anatomical changes that are transmitted to the heart and ecg signal ; 3 ) the compensation of the deformations of the ecg signal introduced by the changes of position which move the heart : knowing the electric position , a dynamic matrix calculation applied to derivations xyz or to the standard ecg derivations makes it possible to compensate for the influence of these changes and to recreate a stabilized signal . this stability is extremely useful for various analyses , in particular the comparisons between two recorded ecgs ; 4 ) the creation of an original ecg by a spatial projection of the signal according to three components x , y and z . in relation to an xyz ecg , this original ecg has two advantages , namely : ( i ) a very great stability , since it is not subjected to the variations of position of the heart , and ( ii ) a signal level that is a maximum in the first channel corresponding to the projection on the principal axis . it can be supplemented by the information on the angles of the projection axes , which then represent the positional changes of the heart , i . e ., the effect of the body changes of position and of the breathing . these various techniques resulting from pca analysis in themselves are known , but the implementation of the invention makes it possible to largely improve the effectiveness , thanks in particular to the labeling of the bumps . the labeling of the bumps makes it possible to improve the effectiveness of this pca - type analysis by carrying it out , if needed , on a particular wave . suitable devices for which the present invention has application include , for example , ambulatory holter recorder and analyzer available from ela médical , montrouge france . these devices are known under the trade marks syneflash and syneview . in the case of implementation in an active implantable medical device such as an implantable pacemaker , such devices also are available from ela médical , montrouge france . these devices are microprocessor - based systems having circuits for receiving , conditioning and processing detected electrical signals , and capable of receiving software instructions by telemetry , storing them in memory , and then executing those instructions to perform the functions described above in implementing the present invention . the creation of suitable software instructions for controlling an implant to perform the aforementioned functions of the present invention are believed to be within the abilities of a person of ordinary skill in the art . 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 .
a device for analyzing a signal , in particular a physiological signal such as an electrocardiogram or electrogram . the signal to be analyzed is preferably one that was previously collected , filtered , sampled and digitized . the device memorizes the digitized signal , and analyzes it by decomposing the signal into a plurality of n elementary waves , and classifying each n elementary waves by recognizing at least one parameter characteristic of each wave , and allotting a standardized label , selected among a plurality of predetermined labels , according to one or to more of the characteristic parameters thus recognized . the decomposition of the signal is into n parameterized bump functions , where each bump function is a continuous function defined by three successive intervals , respectively , a first monotonic parameterized function , an affine function , and a second monotonic parameterized function , with one of the monotonic parameterized functions being increasing and the other decreasing . the parameterized functions are preferably half - gaussian functions , and the affine function preferably has a null slope .