System for characterizing a cardiac rhythm and associated method

Abstract

System for characterizing a cardiac rhythm, configured to implement, by computer, the following steps: ■ generating an input vector, the components of the input vector comprising, among indicators of sets of indicators of probability of presence only each indicator of probability of presence of a selection, ■ generating, using a global classifier, global indicators of probability of presence of arrhythmias in a time window from the input vector, the system comprising a selector making it possible to select and / or deselect at least one of the indicators of probability of presence such that the selection is composed of each indicator of probability of presence selected.

Description

[0001] SYSTEM FOR CHARACTERIZING A HEART RHYTHM AND ASSOCIATED METHOD

[0002] Field of invention

[0003] The field of the invention is that of the characterization of cardiac rhythms in the context of their detection on recordings of a biological signal. The detection of abnormalities in the cardiac rhythm allows a specialist to put in place a possible treatment either to relieve symptoms or to prevent more serious abnormalities that could be potentially lethal.

[0004] Characterization of a patient's heart rhythm is typically performed using an electrocardiogram (ECG). An ECG is a recording of the electrical activity of cardiac cells on the surface of the body or subcutaneously. An ECG comprises one or more views (or "leads") of this electrical activity acquired simultaneously by respective cutaneous or subcutaneous electrodes.

[0005] Electrocardiograms typically include 12 leads acquired over a period of 10 seconds.

[0006] The series of electrical waves of a heartbeat are called by international convention, in their temporal order, P, Q, R, S and T. The P waves correspond to the electrical activity of the atria, and the QRS waves to the electrical activity of the ventricles. By convention, the term RR interval is used to designate the time between two consecutive heartbeats. In this patent application, heartbeat means a part of an electrical signal comprising the R wave, for example, centered on the R wave, the duration of which is the RR interval.

[0007] The use of miniature and portable devices for continuous recording of electrocardiograms, such as cardiac Holters, is increasing rapidly, due in particular to the aging of the population. In addition, these recordings cover increasingly long periods of time, from 24 hours to 1 month, because their performance is directly linked to the duration of the recording. The usual analysis, using dedicated software, then supervised by a specialist, is becoming illusory. For example, a 30-day recording includes 3,000,000 heartbeats. A 1% error on 3,000,000 beats requires the specialist to review a large number of false positives and, even more critically, the specialist risks missing false negatives.

[0008] There is therefore a growing need for optimal characterization of heart rhythms to help rhythmologists detect arrhythmias.

[0009] State of the art

[0010] Automatic systems for characterizing heart rhythms are known, processing previously digitized ECGs.

[0011] A disadvantage of known automatic systems is the insufficient reliability of the results obtained, given the mass of data collected.

[0012] One aim of the invention is to limit this drawback.

[0013] Summary of the invention

[0014] To this end, the invention relates to a system for characterizing a heart rhythm comprising a data processing unit configured to implement, by computer, the following steps:

[0015] ■ when a selection from among presence probability indicators of sets of presence probability indicators consists of the presence probability indicators of the sets of presence probability indicators, generating, for each arrhythmia of the plurality of arrhythmias, using a set of elementary classifiers, a set of presence probability indicators of the arrhythmia over a time window using descriptors of a set of at least one derivation of an electrocardiogram of a patient acquired over the time window, so as to generate the indicators of the sets of presence probability indicators,

[0016] ■ when the selection includes an incomplete part of the presence probability indicators of the presence probability indicator sets and is non-empty, generating each indicator of the selection using at least one of the elementary classifiers using at least one descriptor of at least one derivation of the set of at least one derivation, ■ generating an input vector, the components of the input vector including, among the indicators of the presence probability indicator sets only each indicator of the selection,

[0017] ■ generating, using a global classifier, global indicators of the probability of presence of arrhythmias in the time window from the input vector, the system comprising a selector making it possible to select and / or deselect at least one of the indicators of probability of presence so that the selection is composed of each selected indicator of probability of presence.

[0018] According to one embodiment, the data processing unit is configured to implement, by computer, the following steps:

[0019] ■ for each arrhythmia of a plurality of arrhythmias, generating, using a set of elementary classifiers, a set of indicators of probabilities of presence of the arrhythmia over a time window using descriptors of a set of at least one derivation of an electrocardiogram of a patient acquired over the time window, at least one indicator being able to be alternately in a selected state and in a deselected state,

[0020] ■ generating an input vector from the sets of indicators generated for the arrhythmias, the components of the input vector comprising, among the indicators of the sets of indicators generated for the arrhythmias, only each indicator of a selection of indicators,

[0021] ■ generate, using a global classifier, global indicators of the probability of the presence of arrhythmias in the time window from the input vector.

[0022] The system includes a selector for selecting and / or deselecting at least one of the indicators such that the indicator selection is composed of each selected indicator.

[0023] In other words, according to this embodiment, the data processing unit is configured to implement, by computer, the following steps: ■ For each arrhythmia of the plurality of arrhythmias, generate, using a set of elementary classifiers, a set of indicators of probabilities of presence of the arrhythmia over a time window, using the descriptors of a set of at least one derivation of the patient's electrocardiogram acquired over the time window,

[0024] ■ generating an input vector, the components of the input vector comprising, among the presence probability indicators of the presence probability indicator sets, only each presence probability indicator of a selection,

[0025] ■ generating, using a global classifier, global indicators of the probability of presence of arrhythmias in the time window from the input vector, the system comprising a selector making it possible to select or deselect at least one of the indicators of probability of presence so that the selection is composed of each selected indicator of probability of presence.

[0026] According to another embodiment, when the selection comprises an incomplete part of the presence probability indicators of the presence probability indicator sets and is non-empty, only each presence probability indicator of the selection is generated from among the presence probability indicators presence probability indicator sets.

[0027] Advantageously, the value of each component of the input vector associated with one of the deselected indicators being fixed at the same predetermined value.

[0028] Advantageously, the selector makes it possible to individually select and / or deselect each indicator from a subset of at least one of the indicators.

[0029] These indicators are advantageously generated or likely to be generated using the elementary classifiers of the sets of elementary classifiers.

[0030] Advantageously, the set of at least one derivation comprises several derivations and the selector comprises a first selector making it possible to deselect and / or select the derivations individually, so that each first indicator generated or capable of being generated, using each respective elementary classifier of a predetermined subset of the elementary classifiers, from a deselectable derivation is deselectable.

[0031] Advantageously, the subset is composed of each elementary classifier configured to generate first indicators of probabilities of presence of arrhythmias from first descriptors of the set of at least one derivation, each first indicator being generated from a single derivation.

[0032] Advantageously, the selector comprises a second selector making it possible to individually select and / or deselect groups of elementary classifiers from the sets of elementary classifiers so that each indicator generated, or capable of being generated using a deselected group, is in the deselected state, the groups of elementary classifiers using respective descriptors distinct from the set of at least one derivation, the elementary classifiers of the same group using the same descriptors.

[0033] Advantageously, the selector comprises a third selector making it possible to individually select and / or deselect the elementary classifiers of the sets of classifiers so that each indicator generated or likely to be generated using a deselected elementary classifier is in the deselected state.

[0034] Advantageously, the system comprises a user interface allowing a user to select and / or deselect at least one of the elementary indicators.

[0035] Advantageously, the selector includes an automatic selector configured to:

[0036] ■ check whether a condition for selecting or deselection of at least one indicator is met,

[0037] ■ automatically selecting or respectively deselecting said at least one indicator when the selection or respectively deselecting condition is met. In a particular embodiment, the generation of the set of indicators of probability of presence of the arrhythmia comprises:

[0038] ■ generate a first set of indicators of probability of presence of the arrhythmia in the time window using a first classifier using values ​​from the set of at least one derivation,

[0039] ■ generating a second set of indicators of probability of presence of the arrhythmia in the time window, using a second classifier from first portions, preceding the R wave, of combinations of second portions of beats of the set of at least one derivation,

[0040] ■ generate a third set of indicators of probabilities of presence of the arrhythmia in the time window using a third classifier using a set of statistical indicators representative of the distribution of R_R intervals of the electrocardiogram.

[0041] Advantageously, each of the first, second and third classifiers is configured to discriminate sinus rhythm from arrhythmia.

[0042] Advantageously, the first portions are devoid of R waves. Advantageously, the data processing unit is configured to define a sequence of states of the individual's heart rate, taken from arrhythmias and sinus rhythm, from sets of global indicators generated for successive time windows.

[0043] Advantageously, the definition of the state sequence uses a Viterbi algorithm configured to determine the most probable state sequence likely to be obtained by a hidden Markov model from the global indicator sets.

[0044] Advantageously, the system comprises an acquisition system comprising electrodes configured to acquire all of at least one lead of the electrocardiogram.

[0045] Advantageously, the acquisition system is capable of forming an acquisition device comprising a processing sub-unit configured to implement a descriptor generator to generate the descriptors of the set of at least one lead. Advantageously, the arrhythmias are atrial fibrillation and atrial flutter.

[0046] Advantageously, each of the elementary classifiers and the global classifier is a trained classifier.

[0047] The invention also relates to a method for characterizing a heart rhythm comprising the following steps, implemented by computer:

[0048] ■ when a selection from sets of presence probability indicators consists of the sets of presence probability indicators, generating, for each arrhythmia of the plurality of arrhythmias, using a set of elementary classifiers, a set of presence probability indicators of the arrhythmia over a time window using descriptors of a set of at least one derivation of an electrocardiogram of a patient acquired over the time window, so as to generate the presence probability indicators of the sets of presence probability indicators,

[0049] ■ when the selection includes an incomplete part of the indicators of the indicator sets and is non-empty, generating each indicator of the selection using at least one of the elementary classifiers using at least one descriptor of at least one derivation of the set of at least one derivation,

[0050] ■ generate an input vector, the components of the input vector comprising, among the indicators of the sets of presence probability indicators, only each presence probability indicator of the selection,

[0051] ■ generate, using a global classifier, global indicators of the probability of the presence of arrhythmias in the time window from the input vector,

[0052] ■ generating, using a global classifier, global indicators of the probability of presence of arrhythmias in the time window from the input vector, selecting or deselecting at least one of the presence probability indicators so that the selection is composed of each selected presence probability indicator.

[0053] In one embodiment, the method comprises:

[0054] ■ For each arrhythmia of a plurality of arrhythmias, generating, using a set of elementary classifiers, a set of indicators of probabilities of presence of the arrhythmia over a time window using descriptors of the set of at least one derivation of an electrocardiogram of a patient acquired over the time window, the indicators being able to be alternately in the selected state and in the deselected state,

[0055] ■ generating an input vector from the sets of indicators generated for the arrhythmias, the components of the input vector comprising, among the indicators of the sets of indicators generated for the arrhythmias, only each indicator of a selection of indicators,

[0056] ■ generating, using a global classifier, global indicators of the probability of presence of arrhythmias in the time window from the input vector, selecting or deselecting at least one of the indicators so that the selection of indicators is composed of each selected indicator.

[0057] In other words, in this embodiment, the method comprises:

[0058] ■ For each arrhythmia of the plurality of arrhythmias, generating, using the set of elementary classifiers, the set of indicators of probabilities of presence of the arrhythmia over the time window, using the descriptors of the set of at least one derivation of the patient's electrocardiogram acquired over the time window,

[0059] ■ generating the input vector, the components of the input vector comprising, among the presence probability indicators of the sets of presence probability indicators, only each presence probability indicator of the selection, ■ generating, using the global classifier, global presence probability indicators of the arrhythmias in the time window from the input vector,

[0060] ■ select or deselect at least one of the presence probability indicators so that the selection is composed of each selected presence probability indicator.

[0061] According to another embodiment, when the selection comprises an incomplete part of the presence probability indicators sets of presence probability indicators and is non-empty, only one of the indicators of the presence probability indicator sets is generated, each presence probability indicator of the selection.

[0062] The invention also relates to a computer program product comprising a readable information medium, on which is stored a computer program comprising program instructions, the computer program being loadable onto a data processing unit and adapted to cause the implementation of steps or steps of the method according to the invention, when the computer program is implemented on the data processing unit.

[0063] The invention also relates to a readable information medium comprising program instructions forming a computer program, the computer program being loadable onto a data processing unit and adapted to cause the implementation of step or steps of the method according to the invention when the computer program is implemented on the data processing unit.

[0064] Brief description of the figures

[0065] Other characteristics and advantages of the invention will emerge on reading the detailed description which follows, with reference to the appended figures, which illustrate:

[0066] Figure 1: a schematic view of an example of a system according to the invention,

[0067] Figure 2: A schematic view of a bypass,

[0068] Figure 3: a view of functional blocks of an example system according to the invention,

[0069] Figure 4: a representation of a heartbeat, Figure 5: a block diagram of the steps of an example of the method according to the invention.

[0070] Description of the invention

[0071] The invention relates to a method for characterizing a heart rhythm and to a SYS system configured to implement the method according to the invention.

[0072] The SYS system comprises, for example, as visible in Figure 1, an ACQ acquisition system for an electrocardiogram, noted ECGk in the rest of the text, of a patient during an acquisition time window noted Fk ​​in the rest of the text.

[0073] Acquisition system

[0074] The ACQ acquisition system comprises a set E of cutaneous or subcutaneous electrodes making it possible to simultaneously acquire during the same acquisition time window Fk of duration T, a set of at least one elementary analog signal Ski analogs with i = 1 to N and N is an integer greater than or equal to 1 representing the number of derivations of the electrocardiogram ECGk acquired over the time window.

[0075] N is advantageously between 1 and 12, but may, alternatively, be greater than 12.

[0076] Each elementary signal Ski represents the evolution of the value Vi (t) of the potential difference between electrodes of the set E as a function of time t during the duration T of the acquisition window Fk.

[0077] The ACQ acquisition system also includes an analog-to-digital converter AD to convert the analog Ski elementary signals into digital signals called Dki derivations in the following text, the signals being sampled at a predetermined sampling frequency.

[0078] The sampling frequency is advantageously between 100 Hz and 2000 Hz, preferably between 100 Hz and 1000 Hz. It is, for example, equal to 200 Hz.

[0079] The acquisition device may include a combiner configured to combine digital signals such that at least one branch is a combination of digital signals.

[0080] Each lead Dki is one of the digital signals making up the patient's ECGk electrocardiogram, acquired during the acquisition time window of duration T. It is a sequence of potential difference values ​​Vi taken at regular time intervals over the duration T. An example of a lead Dkj is shown schematically in Figure 2.

[0081] The acquisition system may include derivation processing modules, for example to filter the derivations from the analog converter, before delivering the Dkj derivations.

[0082] The duration T of the acquisition window is advantageously greater than or equal to 10s. It is, for example, between 10s and 30 min. It is, for example, equal to 60 seconds. It is defined so that each derivation includes a plurality of heartbeats.

[0083] The set E of electrodes may comprise at least one electrode intended to be installed on the surface of the skin and / or at least one electrode intended to be installed subcutaneously.

[0084] Alternatively, the SYS system is devoid of the acquisition system and is intended to receive the leads of an ECGk of a patient acquired during the acquisition time window Fk by an external acquisition system, or directly descriptors of such leads as we will see in the remainder of the description.

[0085] Processing system

[0086] The system SYS comprises a processing system S configured to process, by computer, the set of at least one derivation Dkj or the descriptors of the set of at least one derivation Dkj so as to generate indicators of probabilities of presence of arrhythmias of a predetermined set of arrhythmias, during the acquisition window Fk.

[0087] The probability of an arrhythmia being present during the acquisition window Fk generated from a lead means the probability that the arrhythmia is present over the time window Fk.

[0088] In the example in Figure 2, the set of arrhythmias consists of atrial fibrillation, noted AF, also called atrial fibrillation, and atrial flutter, noted FLA, also called atrial flutter.

[0089] This example is not limiting. Generally, the set of arrhythmias comprises several arrhythmias taken from atrial fibrillation, atrial flutter, ventricular tachycardia, supraventricular tachycardia, at least one bradycardia, for example atrioventricular block. The system S comprises a data processing unit TR, for example a processor assembly PO comprising one or more processors, and a memory assembly MEM comprising one or more memories in which the functional building blocks of the system are stored, produced in the form of software or codes executable by the processor assembly PO. The data processing unit is configured to implement the method for characterizing arrhythmias by executing the functional building blocks stored on the memory assembly MEM, i.e. by using the different functionals, and described below.The steps of the arrhythmia characterization process are the steps implemented by the execution of the different functional building blocks.

[0090] Functional bricks

[0091] Figure 3 shows an example of a processing system S on which functional bricks of the processing system S appear.

[0092] The system S comprises a classifier C configured to generate a set of indicators of probabilities of presence of the arrhythmia over the time window Fk, for each time window Fk of a succession of time windows (with 1 = 1 to K where K is the number of time windows) and for each arrhythmia of a set of AF, FLA arrhythmias. The set of indicators of probabilities of presence is generated from descriptors of the set of at least one derivation Dki of an electrocardiogram ECGk acquired during the time window Fk.

[0093] The processing system S advantageously comprises a generator of the descriptors GD configured to generate the descriptors from the set of at least one derivation Dki. Alternatively, the descriptor generator GD is external.

[0094] Elementary classifiers

[0095] For this purpose, the classifier C includes a set of elementary classifiers for each arrhythmia. In other words, in the case of M arrhythmias, the classifier C includes M sets of elementary classifiers. M is an integer greater than or equal to 2.

[0096] Thus, in the example of Figure 3, the classifier C comprises a first set of elementary classifiers MO_FA, RR_FA, PW_FA associated with atrial fibrillation, i.e. configured to generate a first set of indicators IMFAki, IMFAk2, IRRFAk, IPFAki and IPFAk2 representative of probabilities of presence of atrial fibrillation, and a second set of elementary classifiers MO_FLA, RR_FLA, PW_FLA associated with atrial flutter, i.e. configured to generate a second set of indicators IMFAki, IMFLAk2, IRRFLAk, IPFLAki, IPFLAk2 representative of probabilities of presence of atrial fibrillation FA.

[0097] The different elementary classifiers associated with the same arrhythmia, i.e., from the same set of elementary classifiers, belong to distinct groups in the sense that they are configured to generate the respective indicators from respective descriptors distinct from the different Dki derivations or from the derivation in the case of using a single derivation. On the other hand, the elementary classifiers associated with different arrhythmias and using the same descriptors belong to the same group.

[0098] Advantageously, each elementary classifier is configured to discriminate a derivation characterizing the presence of an arrhythmia from a derivation characterizing the presence of a sinus rhythm, from the descriptors of a derivation.

[0099] For this purpose, each classifier was previously trained from the descriptors of training derivations, acquired over the same duration T and having the same sampling frequency as the derivations of the set of at least one derivation. The training derivations comprise only first training derivations characterizing a sinus rhythm and other training derivations, the other training derivations characterizing the presence of the arrhythmia processed by the classifier so as to discriminate a derivation of an atrial fibrillation AF from a derivation of a sinus rhythm RS.

[0100] Advantageously, each set of elementary classifiers associated with an arrhythmia comprises a first classifier, called morphological, a second classifier, called P wave analyzer and a third classifier called RR analyzer.

[0101] These classifiers are each configured to distinguish one of the arrhythmias from sinus rhythm based on one of the following analyses: (i) the morphology of atrial electrical activity, (ii) the presence or absence of a P wave of atrial activity, (iii) the presence or absence of a cardiac rhythm irregularity.

[0102] In the patent application, the indicators delivered by the different elementary classifiers are noted XYk where X corresponds to the type of classifier and Y is the arrhythmia sought, that is to say of which the classifier generates a probability of presence, k is the temporal order number of the time window Fk in the sequence of time windows.

[0103] In the example of Figure 3, we note X= MO for the morphological classifier, X= RR for the RR analyzer classifier and X= PW for the P wave analyzer classifier and Y= FA for atrial fibrillation and Y= FLA for flutter.

[0104] Some indicators are supplemented with an index i. The indicators of index i are generated from characteristics of a first derivation of index i, Dki, of the set of at least one derivation. For example, the indicators IMFAki and IMFLAki are generated from characteristics of the first derivation Dki. The indicators IMFAk2 and IMFLAk2 are generated from characteristics of the second derivation Dk2.

[0105] Morphological classifier

[0106] The so-called morphological classifier MO_FA, respectively MO_FLA, associated with a given arrhythmia FA, respectively FLA, is configured to process consecutive values ​​Vi of the derivation(s) Dki, Dk2 to generate a first set of indicators IMFAki, IMFAk2; respectively IMFLAki, IMFLAk2, representative of probabilities of presence of the arrhythmia FA, respectively FLA, during the time window Fk.

[0107] Each indicator IMFAki, IMFAk2 , IMFLAki, IMFLAk2 is generated from consecutive values ​​Vi of only one of the derivations Dki, Dk2. Each morphological classifier MO_FA, respectively MO_FLA, is configured to generate indicators IMFAki, IMFAk2 , respectively IMFLAki, IMFLAk2 from the values ​​of the derivation(s) Dki, Dk2.

[0108] Advantageously, in the case of several derivations, each morphological classifier MO_FA, MO_FLA is configured to process the consecutive values ​​of the two derivations Dki, Dk2 so as to successively generate the indicators IMFAki and IMFAk2.

[0109] Advantageously, each morphological classifier MO_FA, MO_FLA is an artificial neural network, for example a convolutional neural network or CNN, an acronym for the English expression "convolutional neural network". Alternatively, the classifier is a transformer or a recurrent neural network.

[0110] Each morphological classifier MO_FA, respectively MO_FLA is advantageously an artificial neural network trained from consecutive values ​​of derivations acquired over the same duration and that of the acquisition window Fk with the same sampling frequency, some of which characterize a sinus rhythm and the others characterize the presence of the arrhythmia FA, respectively FLA, processed by the elementary classifier so as to discriminate a derivation characterizing an atrial fibrillation FA from a derivation characterizing a sinus rhythm RS.

[0111] This type of classifier and discriminator for arrhythmias generates characteristic distributions distinct from one arrhythmia to another and from those of a patient presenting a sinus rhythm.

[0112] This is the case, for example, with atrial fibrillation and flutter. Atrial fibrillation is characterized by irregular electrical activity, while the electrical activity in sinus rhythm is regular and ranges between 60 and 100 beats per minute, while the electrical activity in atrial fibrillation is between 300 and 600 cycles per minute.

[0113] Flutter is characterized by regular electrical activity. The heart rate in the presence of flutter is between 100 and 300 cycles per minute.

[0114] P-wave analyzer classifier

[0115] Classifier C further includes a classifier called P wave analyzer for each arrhythmia.

[0116] Each P wave analyzer classifier PW_FA; respectively PW_FLA, associated with a given arrhythmia FA, respectively FLA, is configured to process combinations, for example averages, of predetermined portions of beats present on the respective lead(s) Dki, Dk2, acquired during the acquisition window F to generate indicators IPFAi, IPFA2; respectively IPFLA1, IPFLA2, of probabilities of presence of the arrhythmia.

[0117] These portions are advantageously portions preceding the R wave and devoid of the R wave. These portions are, for example, located only before the R wave.

[0118] These portions advantageously include the P depolarization wave temporally preceding the R wave in the case of sinus rhythm or, more generally, the part of the combination likely to include the P wave in the case of sinus rhythm.

[0119] Advantageously, the combined portions, for example averaged, correspond to portions of the same predetermined duration preceding the R wave and devoid of the R wave.

[0120] Each portion is, for example; a portion of predetermined duration preceding the R wave. This duration is, for example, between 200 ms and 500 ms.

[0121] For example, the predetermined duration portion extends from a first instant to a second instant preceding the R wave and separated by a predetermined time gap from the previously detected R wave or the previously detected QRS complex.

[0122] Alternatively, each portion corresponds to a full beat.

[0123] In another embodiment, the portion follows the R wave. It is, for example, located only after the R wave.

[0124] The system S advantageously comprises a combiner, for example an averager MO, of P waves configured to generate, for each derivation Dki, respectively Dk2, a combination, for example an average Mki, Mk2 of at least a subset of the beats present on the derivation Dki, respectively Dk2, and detected by the R wave detector. The combined beats, for example averaged, advantageously have the same duration equal to the period of the beat, are, for example centered on the R wave and have a predetermined duration, advantageously defined so that the beats used for the combination or the averaging include the successive waves P, Q, R, S and T.

[0125] In other words, the combiner is configured to combine at least a subset of the beats in a lead to generate a signal that we call a combination. Alternatively, the R waves are subtracted from the combined waves before combining, or the R waves are subtracted from the combinations. This helps facilitate classification.

[0126] Combining or averaging portions including only the P wave (or, more generally, the portion of the beat likely to include the P wave) makes it easier to classify atrial activity.

[0127] The processing system S advantageously, but not necessarily, comprises an EP portion extractor configured to extract a portion Pki, Pk2 of one of the combinations or averages Mki, Mk2 of the beat, obtained from one of the derivations Dki, Dk2. This portion is, for example, the combination or average considered, it may comprise a portion preceding the R wave and the R wave, for example the entire combined or averaged PQRS complex, or a portion, for example of predetermined duration, of the combined or averaged beat, preceding the R wave and devoid of the R wave, that is to say including only a part of the combination preceding the R wave. The suppression of the QRS complex of significantly higher intensity than that of the part preceding the P wave, in particular in the case of atrial fibrillation or flutter, makes it possible to improve the performance of the classifier.

[0128] This portion is, for example, a portion of predetermined duration preceding the R wave. This duration is, for example, between 200 ms and 500 ms.

[0129] For example, the predetermined duration portion extends from a first instant to a second instant preceding the R wave and separated by a predetermined time gap from the previously detected R wave or the previously detected QRS complex.

[0130] Each P wave analyzer classifier PW_FA, PW_FLA is configured to generate indicators IPFAki, IPFAk2; respectively IPFLAki, IPFLAk2 of probabilities of presence of the given arrhythmia from portions Pki, Pk2 of combinations or averages of beats of leads Dki, Dk2.

[0131] Each P-wave analyzer classifier PW_FA, PW_FLA is advantageously configured to generate each indicator of probability of presence of the arrhythmia IPFAki, IPFAk2; IPFLAki, IPFLAk2 from one of the portions Pki, Pk2. In other words, each P-wave analyzer classifier PW_FA, PW_FLA is advantageously configured to generate each indicator of probability of presence of the arrhythmia IPFAki, IPFAk2; IPFLAki, IPFLAk2 that it generates from a portion Pki, Pk2 of a single combination generated from a derivation.

[0132] Each P wave analyzer classifier, PW_FA, respectively PW_FLA is able to generate indicators IPFAki, IPFAk2; respectively IPFLAki, IPFLAk2 from the portions of the combinations or averages resulting from the different derivations Dki, Dk2.

[0133] In other words, each P-wave analyzer classifier PW_FA, PW_FLA is configured to generate different indicators of probability of presence of arrhythmia IPFAki, IPFAk2; IPFLAki, IPFLAk2 from portions Pki, Pk2 generated from respective leads.

[0134] In the case of a single lead, each indicator generated by a P-wave analyzer classifier is generated from a portion of a combination obtained from the lead.

[0135] Advantageously, in the case of several derivations, each P-wave analyzer classifier PW_FA, PW_FLA is configured to successively process the portions of the combinations from the different derivations Dki, Dk2, that is to say to consecutively process the derivations, so as to successively generate the indicators IMFAki and IMFAk2.

[0136] Advantageously, each P-wave analyzer classifier PW-FA, PW_FLA is an artificial neural network, for example a convolutional neural network.

[0137] Each P wave analyzer classifier PW_FA, PW_FLA is advantageously previously trained from portions of combinations, for example averages, of beats of training derivations acquired over the same duration as the acquisition window Fk and some of which characterize the sinus rhythm and the others the arrhythmia associated with the classifier so that the classifier is able to discriminate a portion characteristic of a sinus rhythm from a portion characteristic of the arrhythmia.

[0138] P-wave analyzer classifiers are particularly interesting and discriminating in aiding the detection of arrhythmias generating characteristic P-wave shapes that are distinct from one arrhythmia to another and from the characteristic P-wave shape of a healthy patient.

[0139] This is the case, for example, with atrial fibrillation and atrial flutter. Indeed, atrial fibrillation is characterized by an absence of a dome-shaped sinus P wave replaced by irregular f waves, whereas atrial flutter is characterized by a "sawtooth" P wave, identical and regular, diphasic (the negative phase of which predominates: we then speak of an F wave), whereas a sinus rhythm presents a P wave.

[0140] Furthermore, a classifier is more accurate and therefore sensitive and reliable than using a threshold to discriminate sinus rhythm from atrial fibrillation or flutter, which are characterized by the replacement of a P wave by irregular or regular waves.

[0141] Alternatively, the combination is, for example, a median of beats.

[0142] Alternatively and / or additionally, at least one set of elementary classifiers dedicated to an arrhythmia may comprise at least one other classifier, for example, a classifier receiving a combination or average of QRS complexes and a combination or average of P waves separately to discriminate, for example, an atrioventricular block from sinus rhythm, with a number of P waves greater than the number of QRS, or a sequence of P waves not followed by QRS.

[0143] Alternatively and / or additionally, a classifier configured to discriminate leads characteristic of an arrhythmia from leads characteristic of sinus rhythm from a Fourier transform of at least one of the aforementioned descriptors may be provided.

[0144] RR analyzer classifier or regularity classifier

[0145] Classifier C advantageously includes an RR interval analyzer classifier for each arrhythmia.

[0146] Each RR, RR_FA; respectively RR_FLA interval analyzer classifier, associated with a given FA, respectively FLA arrhythmia, is configured to process statistical indicators representative of the distribution of RR intervals of the ECGk acquired during the acquisition window Fk to generate IRRFAk; respectively IRRFLAk indicators, representative of probabilities of presence of the FA, respectively FLA arrhythmia during the window Fk. The part of a derivation Dkj corresponding to a beat is made up of a succession in time of electrical waves whose appearance is represented in Figure 4. The P wave is the first in time and corresponds to the depolarization of the atria, has a low amplitude, and a dome shape in the case of sinus rhythm. Then the PR interval translates the atrioventricular conduction time.The QRS complex reflects ventricular contraction, and finally the T wave reflects ventricular repolarization following the QRS complex. By convention, the R peak is considered as a marker of ventricular systole. Since the R wave is most often the thinnest and widest part of the QRS complex, it is generally used by convention to temporally localize the heartbeat with very good precision, in practice of the order of a thousandth of a second. Thus, the time interval between two successive R waves, also called by convention the RR interval, precisely characterizes the time separating two successive heartbeats. The RR interval is the instantaneous period of the ECG signal. The instantaneous heart rate is the inverse of the RR interval.

[0147] The RR interval is common to all Dkj leads of an electrocardiogram.

[0148] The GD descriptor generator advantageously comprises a GIS statistical indicator generator characterizing the distribution of the patient's RR intervals during the acquisition window Fk from at least one derivation of the set of at least one derivation Dki. The statistical indicators generated are provided to the RR_FA, RR_FLA interval analyzer generators as we will see in the rest of the text.

[0149] The GD descriptor generator comprises a DB beat detector configured to detect and timestamp, i.e. to determine the times of occurrence or TOC, acronym for the English expression, of consecutive R waves from at least one of the leads, the distribution of the R waves being the same for all the leads and possibly to detect and timestamp other waves, for example, P and / or Q and / or S and / or T from the lead.

[0150] The GIS statistical indicator generator comprises an RR series GRR generator, SRRk, configured to generate an RR series, SRRk for the time window Fk, from at least one lead of the set of leads, for example, lead Dki. The RR series GRR generator comprises an RR series builder configured to construct the RR series by calculating the R-R intervals between consecutive beats, from the timestamps of the consecutive R waves.

[0151] The series includes H terms R-Rk(h) with h= 1 to H-1 , where H is the number of heartbeats present in the derivation Dkj, h corresponds to the temporal order number of the first beat from which the RR interval is calculated, considered in the derivation Dki.

[0152] The RR series, SRRk comprises successive values ​​which are consecutive R-Rk(h) values ​​of the instantaneous RRh intervals of the chosen lead Dki during the time window Fk, or which are determined from several leads.

[0153] Alternatively, the DB beat detector is configured to detect and timestamp one of the other depolarization waves (P, Q, S, or T) in consecutive beats. The RR series constructor then uses these timestamps to construct the RR series. However, the accuracy of characterizing the time interval between two successive heartbeats obtained by this method is less good than using R waves.

[0154] The GIS statistical indicator generator comprises an elementary statistical indicator generator RR_GM configured to generate, from the RR series, at least one statistical indicator Gk characterizing the statistical distribution of the R-Rk(h) intervals of the RR series, SRRk.

[0155] This elementary generator RR_GM is advantageously configured to generate, from the RR series, parameters of a Gaussian mixture model characterizing the distribution of the RR intervals of the RR series, SRRk. In other words, the elementary generator RR_GM is configured to parameterize a Gaussian mixture model so that the Gaussian mixture model models the distribution of the RR intervals of the RR series.

[0156] The Gaussian mixture model is commonly referred to by the English acronym GMM from the English expression "Gaussian Mixture Model". The Gaussian mixture model is used to parametrically estimate the distribution of the RR intervals of the RR series by modeling it as the sum of several Gaussians. The parameterization of the model consists of determining the variance, the mean and the amplitude of each of the Gaussians of the model. These parameter values ​​are optimized according to the maximum likelihood criterion in order to approach as closely as possible the desired distribution. This optimization is, for example, carried out using a known iterative procedure called expectation-maximization EM.

[0157] The elementary generator RR_GM is configured to calculate at least one elementary statistical indicator from at least one parameter of the Gaussian mixture model parameter. It is, for example, configured to calculate the ratio of the means of the Gaussians of the model.

[0158] The Gaussian model includes for example 1 or 2 Gaussians.

[0159] The statistical indicators derived from the parameters of the Gaussian model are characteristic and discriminating. In the case of a model comprising 3 Gaussians, the sinus rhythm comprises three Gaussians with the same center due to the stability of the RR interval. The ratio of the means of the Gaussians is equal to 1. We know that the cardiac cycle of a flutter can only take values ​​that are multiples of a basic duration. The ratio of the means of the Gaussians can therefore only take a predetermined number of values. This indicator is therefore particularly relevant for highlighting the presence of atrial flutter. The distribution of the RR intervals is therefore modeled by respective Gaussians centered on these three respective values. The distribution of the RR intervals in the case of atrial fibrillation is very irregular. The ratio of the means of the Gaussians can therefore take an unlimited number of values.

[0160] In the non-limiting example of Figure 3, the GIS statistical indicator generator comprises a generator of a sequence of ratios between consecutive RR intervals given by the following formula:

[0161] RAk(h) = R-Rk(h+1) / R-Rk(h) for h = 1 to H-2.

[0162] This series of ratios is characteristic of atrial fibrillation and flutter and allows these arrhythmias to be distinguished from each other and from sinus rhythm.

[0163] Indeed, in the case of sinus rhythm, the RA(h) ratios all have a value approximately equal to 1 since the heart rate is approximately stable and even if it increases, for example in the case of exercise, the RR interval between the first beat and the second beat is approximately equal to the RR interval between the second and third beats. In the case of flutter, the RA(h) ratio can take only a certain number of known discrete values. In the case of atrial fibrillation, the RAk(h) ratios can take a large number of values.

[0164] Advantageously, the GIS statistical indicator generator includes a generator of indicators of probabilities of presence of arrhythmias from the sequence of ratios between RR intervals.

[0165] Advantageously, the generator includes RR, RA_FA, RA_FLA ratio classifiers. Each ratio classifier is configured to discriminate one of the FA or FLA arrhythmias from sinus rhythm.

[0166] For this purpose, each of the ratio classifiers is previously trained by series of RAk(h) ratios generated from training derivations, some of which are characteristic of the arrhythmia considered and the others of sinus rhythm, to discriminate the series of ratios resulting from sinus rhythm from those resulting from the arrhythmia considered.

[0167] Each ratio classifier is configured to generate a statistical indicator IFAk, respectively IFLAk, which is an indicator of the probability of presence of one of the AF or FLA arrhythmias considered from the series of ratios RAk(h).

[0168] We thus obtain statistical indicators Gk, IFAk, IFLAk of ​​the probability of presence of the respective arrhythmias in the window considered Fk.

[0169] Ratio classifiers are, for example, artificial neural networks. These classifiers are, for example, CNNs. Alternatively, at least one ratio classifier is a transformer or a recurrent neural network.

[0170] The statistical indicators generated by the GIS statistical indicator generator are provided to the RR, RR_FA and RR-FLA analyzer classifiers.

[0171] Advantageously, each RR, RR_FA, RR_FLA analyzer classifier is an artificial neural network, for example a multilayer perceptron or MLP, acronym for the Anglo-Saxon expression “Multilayer Perceptron”.

[0172] This RR_FA, RR_FLA classifier receives as input a vector composed of at least one statistical indicator generated by the GIS generator, for example the Gk indicator characterizing the R_R distribution and the probability indicator generated for the arrhythmia considered IFAk, IFLAk.

[0173] Each R_R RR_FA, RR_FLA analyzer classifier is advantageously previously trained from the same vector generated by the GIS statistical indicator generator from derivations, for example, measured during the same duration as the training window and the first of which characterize the arrhythmia considered and the others of which characterize the sinus rhythm, to discriminate these arrhythmias.

[0174] In the embodiment of figure 1, the ACQ acquisition system is an acquisition device, that is to say an object, connected to the system S, by communication means of the system SYS, for example wireless or wired allowing unidirectional or bidirectional communication between the system S and the ACQ acquisition device so that the ACQ acquisition device is able to transmit the derivations to the system S. These are for example the communication means described in the rest of the text.

[0175] In a particular embodiment, the processing unit comprises a first processing sub-unit configured to implement the generator of the descriptors GD from the set of at least one derivation Dki or at least the statistical indicator generator GIS and, preferably the beat detector, and a second processing sub-unit configured to implement the classifiers C.

[0176] Advantageously, the ACQ acquisition system is capable of forming an acquisition device comprising the set of electrodes E and the first processing sub-unit. It comprises, for example, the set of electrodes and a housing capable of being mechanically connected to the set of electrodes or mechanically connected to this set and containing the first processing unit.

[0177] Advantageously, the housing also contains the analog-to-digital AD converter.

[0178] The ACQ acquisition device is then connected to the second processing sub-unit, by communication means of the SYS system, for example wireless or wired, allowing one-way or two-way communication between the second processing sub-unit and the ACQ acquisition device so that the ACQ acquisition device is able to transmit the descriptors that it has generated to the second processing sub-unit so that it can execute the classifier C. In hardware, each processing sub-unit can be implemented in one of the forms described above for the processing unit.

[0179] The ACQ acquisition device is advantageously portable or implantable on an individual subcutaneously.

[0180] Segmentation of the approach

[0181] The use of classifiers analyzing in parallel distinct descriptors of one or more leads to generate indicators of the probability of presence of different arrhythmias requires less training data than a single classifier analyzing the consecutive values ​​of the lead(s) to obtain indicators of equivalent reliability or sensitivity. Indeed, the segmented analysis of the descriptors of the leads makes it possible to limit the number of degrees of freedom of the neural networks. The training of neural networks is facilitated.

[0182] Furthermore, the segmented approach is more robust to artifacts present on the leads.

[0183] Furthermore, performing in parallel a morphological analysis of the signal, a statistical analysis of the temporal distribution of the ECG signal as well as an analysis of the P wave to generate indicators and to generate global probability indicators from these probabilities is particularly effective for discriminating between atrial flutter, atrial fibrillation and sinus rhythm. As seen previously, the descriptors analyzed by these different classifiers (morphology, P wave, statistical indicators of the distribution of RR intervals) present particularities (shapes, values) typical of the different arrhythmias and sinus rhythm and discriminating between the different arrhythmias and sinus rhythm and these descriptors are understandable for the end user because they simulate the basic rules of clinical electroradiology.

[0184] Furthermore, carrying out different analyses makes it possible to maintain good performance when one of the classifiers is less efficient for a derivation presenting a particular aspect, for example in the case of an abnormally regular rhythm in the case of atrial fibrillation.

[0185] The generation of different probability indicators for the same arrhythmia and the same derivation, by the different classifiers allows a rhythmologist to whom these indicators are provided, to improve his knowledge of the descriptors of the rhythm analyzed compared to the generation of a single indicator which allows him to more easily identify an arrhythmia characterized by the ECGk or to identify an elementary classifier not relevant for the detection of an arrhythmia.

[0186] Furthermore, analyzing arrhythmias by different sets of elementary classifiers improves classification performance and limits the amount of training data.

[0187] Furthermore, analyzing the leads independently allows the rhythmologist to select the indicators he wants to deduce information on the rhythm, particularly when a lead is obviously degraded, for example when it has been incorrectly installed on the patient, which allows him to more easily and reliably identify an arrhythmia characterized by the ECGk.

[0188] Generating probability indicators time window by time window provides information to the rhythmologist regularly throughout the acquisition of a global ECG comprising a succession of time windows. This allows for faster detection of arrhythmias and more precise identification of the time of occurrence of arrhythmias.

[0189] Input Vector Generator

[0190] The system advantageously comprises a vector generator GV configured to generate an input vector V from the sets of probability indicators generated by the classifier C.

[0191] The probability indicators are the probability indicators generated by the elementary classifiers of classifier C.

[0192] The vector V is composed of a number A of components, where A is greater than or equal to the number L of probability indicators generated by the classifier C, that is to say generated by the elementary classifiers of the classifier C.

[0193] The vector Vk is capable of being such that each of a number L of its components is one of the probability indicators. Each probability indicator corresponds to a predetermined component, that is to say to a predetermined order number in the order of the components of the vector.

[0194] In the non-limiting example of Figure 3, the number L of presence probability indicators is equal to 10. 10 components of the vector V are reserved for the probability indicators, in the sense that their values ​​are likely to be those of the L probability indicators.

[0195] When A is greater than L, the other AL components of the vector may include values ​​of other parameters such as age, patient history, and / or risk factors.

[0196] Global classifier

[0197] The SC classification system comprises a global classifier GC configured to generate a set of global indicators IGFAk, IGFLAk, IGRSk of probabilities of presence of arrhythmias FA, FLA, and sinus rhythm noted RS, over the time window Fk, from an input vector noted Vk.

[0198] The global classifier GC is previously trained from training vectors generated from the training lead descriptors, by the classifier C, to discriminate V vectors from the different arrhythmias between them and with the sinus rhythm.

[0199] The test leads include test leads characteristic of sinus rhythm, and sets of test leads characteristic of the respective arrhythmias, each set of test leads being characteristic of one of the arrhythmias.

[0200] The global classifier GC is, for example, an artificial neural network.

[0201] The neural network is, for example, a multi-layer perceptron, a feedforward neural network. Alternatively, the global classifier is a forest of decision trees.

[0202] Generating reliable global indicators from indicators generated by different classifiers requires less training data than using a single classifier. This solution also offers better sensitivity and robustness.

[0203] Sequence of derivations

[0204] Advantageously, the ACQ acquisition system is configured to generate a sequence of sets of at least one derivation, i.e. ECGk with k= 1 to K, acquired during K successive time windows F, for example consecutive, of a global acquisition window FG of global duration TG. The global duration TG of the global acquisition window FG is advantageously between 20s and 30 days.

[0205] Advantageously, the successive time windows have the same duration.

[0206] The classifier C is configured to generate sequences of K sets of global indicators IGFAk, IGFLAk, IGRSk from the descriptors of the sets of at least one successive derivation Dki, Dk2. Each set of global indicators IGFAk, IGFLAk, IGRSk is generated from a single set of at least one derivation Dki, Dk2 of the sequence of time windows Fk. The temporal order of the sets of global indicators IGFAk, IGFLAk, IGRSk is the same as that of the sets of derivations Dki, Dk2 from which they are respectively generated.

[0207] Time Analyzer

[0208] Advantageously, the system S comprises a temporal analyzer TC configured to define a sequence of states Ek of the heart rate corresponding to the states Ek of the heart rate on the successive time windows Fk from the sequence of sets of global indicators IGFAk, IGFLAk, IGRSk generated for the successive time windows Fk.

[0209] Each Ek state is taken from arrhythmias and sinusoidal rhythm FA, FLA or RS.

[0210] The idea is to determine, from a sequence of K sets of global indicators IGFAk, IGFLAk, IGRSk, a sequence of states Ek of the heart rate in successive time windows Fk.

[0211] The TC temporal analyzer is configured to generate the states Ek of the respective time windows Fk from the global indicators generated for other time windows, for example all other time windows. The TC temporal analyzer makes it possible to obtain a sequence of elementary states consistent with each other and with the known probabilities, for example learned, of transition from one state to another from one time window to another. We know, for example, that when a rhythm is in flutter on a time window, the probability of remaining in flutter on the following time windows is very high. On the other hand, when the rhythm is atrial fibrillation, the patient can go into flutter, but with a low probability.The TC temporal analyzer uses, for example, a model such as a hidden Markov model also called HMM, an acronym for the Anglo-Saxon expression "Hidden Markov model" by implementing a Viterbi algorithm to generate the most probable state sequence from the sequence of global indicators that can be generated by the hidden Markov model.

[0212] The hidden Markov model used is parameterized.

[0213] In order to parameterize the hidden Markov model, the latter was previously subjected to a first training phase during which the hidden Markov model is trained from sequences of sets of global indicators IGFAk, IGFLAk, IGRSk labeled by a sequence of known states so as to generate a matrix of transition probabilities between the possible states.

[0214] The hidden Markov model was also trained, during a second training phase, to learn, for each possible state (FA, FLA and RS), to which observation (i.e. which set of global indicators IGFAk, IGFLAk, IGRSk) it corresponds. This learning phase is carried out from global training indicators generated, by the global classifier, from descriptors of one or more characteristic derivations of the given state. The second training phase allows to obtain a matrix of observations. The separation of these two training sessions makes it a simple learning process.

[0215] The probability of starting in each state is, for example, predetermined. For example, one can decide that the states are equiprobable or that the probabilities of starting in the respective states are the same. Alternatively, these probabilities are calculated from a set of derivation sequences whose state sequences, and therefore the first states, are known.

[0216] Alternatively, the TC temporal analyzer comprises, for example, a transformer or self-attentive model. Alternatively, the TC temporal analyzer comprises a recurrent neural network also called RNN in reference to the English expression "Recurrent neural network", for example, a recurrent network with short and long term memory or LSTM acronym of the English expression "Long short-term memory".

[0217] Selector Generally, the processing unit is configured to generate, for each arrhythmia of a plurality of FA, FLA arrhythmias, using a set of elementary classifiers, a set of indicators of probabilities of presence of the FA, FLA arrhythmia over a time window Fk using descriptors of a set of at least one derivation of an ECGk electrocardiogram of a patient acquired over the time window Fk.

[0218] The set of elementary classifiers comprises, for example, at least one of the elementary classifiers of Figure 2 and, possibly, at least one other elementary classifier of the system S.

[0219] It is important to note that the indicators generated by the elementary classifiers are not always relevant, their consideration by the global classifier leading to a degradation in the reliability of the global indicators.

[0220] This is, for example, the case when atrial fibrillation is coupled with a disorder of electrical impulse conduction that modulates the RR interval. The analysis of the RR interval is not relevant for characterizing atrial fibrillation in this case, since taking into account the indicators generated by the RR analyzer classifier degrades the reliability of the indicators generated by the global classifier. The reliability of the indicators generated by the morphological classifier is also degraded by the regularity of the RR intervals. In this case, only the P wave analyzer classifier and the morphological classifier are relevant for generating the indicators.

[0221] The same applies when a lead does not allow reliable indicators to be obtained, for example because it is noisy or presents artifacts, for example due to muscle movement or electrode detachment, the indicators generated from this lead, in particular by the P wave analyzer classifier from combinations of parts of this lead will not be reliable.

[0222] In order to improve the quality of the global indicators generated by the global classifier, the system advantageously comprises a selector INT, SE making it possible to select and / or deselect at least one of the indicators generated by the elementary classifiers so that only a selection of these indicators, i.e. only a selection taken from among these indicators, is taken into account by the global classifier GC, to generate the global indicators IGFAk, IGFLAk, IGRSk relating to the time window Fk considered.

[0223] In other words, only each indicator of the selection is (are) taken into account by the global classifier GC to generate the global indicators IGFAk, IGFLAk, IGRSk relating to the time window Fk considered.

[0224] For this purpose, at least one of the indicators, for example each indicator, is advantageously capable of being alternately in a selected state and in a deselected state.

[0225] The indicator selection includes each indicator in the selected state. It may include no indicators when all indicators are in the deselected state or include one or more selected indicators.

[0226] The generator GV of the input vector V is configured to generate an input vector V comprising; among the indicators generated by the elementary classifiers for arrhythmias, only each indicator of the selection of indicators.

[0227] Advantageously, the vector generator GV uses, among the presence probability indicators generated or likely to be generated for the time window by the sets of elementary classifiers dedicated to the different arrhythmias, only each presence probability indicator of the selection, to generate the input vector V.

[0228] The value of the other components of the input vector V likely to take the values ​​of the other indicators, i.e. reserved for the other indicators, is advantageously fixed at the same predetermined value. This value is, for example, equal to or is representative of a probability of 1 / 2.

[0229] For example, in the case of 10 presence probability indicators, if the indicator selection includes two indicators, the values ​​of the other 8 components of the vector reserved for the presence probability indicators are fixed to the same predetermined value, for example .

[0230] This helps to limit the impact of non-selected indicators on the quality of the overall indicators.

[0231] In a first embodiment described above, the data processing unit is configured, regardless of the number of indicators in the selection of indicators, to generate, for each arrhythmia of the plurality of arrhythmias, using the set of elementary classifiers, the set of indicators of probabilities of presence of the arrhythmia over the time window, using the descriptors of the set of at least one derivation of the patient's electrocardiogram acquired over the time window.

[0232] The processing unit is also configured to generate an input vector Vk whose components include, among the indicators of the sets of presence probability indicators generated for the arrhythmias over the time window, only each indicator of the selection.

[0233] The sets of presence probability indicators generated by the processing unit for arrhythmias over the time window include each indicator from the indicator selection.

[0234] If the selection is not complete, in the sense that it does not include all the presence probability indicators likely to be generated by the elementary classifiers for a time window, the presence probability indicators generated by the processing unit include each indicator of the selection as well as each presence probability indicator, generated for the time window, not selected.

[0235] In a second embodiment, the data processing unit is configured to implement the following steps:

[0236] ■ When a selection from the probability indicators of sets of presence probability indicators consists of the sets of presence probability indicators, generating, for each arrhythmia of the plurality of arrhythmias, using a set of elementary classifiers, one of the sets of presence probability indicators of the arrhythmia over a time window from the descriptors of a set of at least one derivation of an electrocardiogram of a patient acquired over the time window, so as to generate the presence probability indicators of the sets of presence probability indicators,

[0237] ■ Generate, among the probability of presence indicators of the probability of presence indicator sets, only each probability of presence indicator of the selection, when the selection includes an incomplete part of the probability of presence indicators of the indicator sets and is non-empty.

[0238] The data processing unit is configured to use for this purpose at least one classifier from the set of elementary classifiers which uses at least one descriptor of at least one derivation from the set of derivations of the patient's electrocardiogram acquired over the time window.

[0239] In other words, we only use each elementary classifier capable of generating, that is to say configured to generate, an indicator of the selection. This embodiment is therefore economical in terms of calculations.

[0240] Advantageously, in the latter embodiment, when the selection is zero, no indicator is generated for the time window.

[0241] Therefore, generally speaking, the data processing unit is configured to implement the following steps:

[0242] ■ when a selection of indicators from sets of presence probability indicators consists of the presence probability indicators of the sets of presence probability indicators, generating, for each arrhythmia of the plurality of arrhythmias, using a set of elementary classifiers, one of the sets of presence probability indicators of the arrhythmia over a time window using descriptors of a set of at least one derivation of an electrocardiogram of a patient acquired over the time window, so as to generate the indicators of the sets of presence probability indicators,

[0243] ■ when the selection includes an incomplete part of the presence probability indicators of the indicator sets and is non-empty, generating each indicator of the selection using at least one of the elementary classifiers using at least one descriptor of at least one derivation of the set of at least one derivation,

[0244] ■ generate an input vector Vk, the components of the input vector Vk comprising, among the indicators of the sets of presence probability indicators, only each presence probability indicator of the selection,

[0245] ■ generating, using a global classifier GC, global indicators of the probability of presence of arrhythmias in the time window Fk from the input vector Vk, the system comprising a selector making it possible to select and / or deselect at least one of the indicators of probability of presence so that the selection is composed of each indicator of probability of presence selected.

[0246] The selection of indicators is made from the presence probability indicators of the sets of presence probability indicators likely to be generated for the time window, for the arrhythmias of the set of arrhythmias using, for each arrhythmia of the set of arrhythmias, using the set of elementary classifiers dedicated to the arrhythmia, using descriptors of a set of at least one derivation of an electrocardiogram of a patient acquired over the time window.

[0247] By indicator or indicator of probability of presence capable of being generated by an elementary classifier, it is meant that this elementary classifier is configured to generate this indicator of probability of presence from the descriptors of a set of at least one derivation of an electrocardiogram of a patient acquired over a time window.

[0248] In other words, the selection of indicators comprises only each selected presence probability indicator taken from the sets of presence probability indicators that the sets of elementary classifiers dedicated to the arrhythmias of the set of arrhythmias are configured to generate using descriptors of a set of at least one derivation of an electrocardiogram of a patient acquired over the time window considered.

[0249] The selected state is, for example, the default state of each indicator. The INT, SE selector then advantageously allows at least one of the indicators to be de-selected. Advantageously, but not necessarily, the INT, SE selector also allows the indicator to be selected. This allows the indicator to be selected and de-selected. Alternatively, the deselected state is, for example, the default state of each indicator. The selector then advantageously allows at least one of the indicators to be selected. Advantageously, but not necessarily, the INT, SE selector also allows the indicator to be de-selected.

[0250] In a particular embodiment, the selector INT, SE makes it possible to individually select and / or deselect each indicator from a subset of at least one of the indicators generated or, more generally, likely to be generated by the elementary classifiers. It makes it possible, for example, to individually select the different indicators generated or, more generally, likely to be generated by the elementary classifiers. In another embodiment, the selector makes it possible to jointly select and / or deselect several indicators.

[0251] The INT, SE selector allows, for example, to jointly select and / or deselect indicators, by selecting and / or deselecting at least one elementary classifier and / or by selecting and / or deselecting at least one lead and / or by selecting and / or deselecting at least one arrhythmia and / or by selecting and / or deselecting at least one group of elementary classifiers.

[0252] For example, each elementary classifier, each lead and each arrhythmia are able to be in a selected state and in a deselected state.

[0253] The selected state is, for example, the default state. Alternatively, the deselected state is the default state.

[0254] According to an exemplary embodiment, the selector comprises a first selector making it possible to individually deselect and / or select the derivation(s) Dkj of the time window Fk so that each indicator generated or, more generally, capable of being generated, from the derivation deselected, by a predetermined set of elementary classifiers, is in the deselectable state. This avoids the consideration of the derivation deselected by the global classifier GC to generate the global indicators relating to the time window Fk.

[0255] The predetermined set of elementary classifiers is, for example, made up of all elementary classifiers. Thus, deselection of a derivation results in deselection of each indicator generated from the derivation.

[0256] Alternatively, the predetermined set of elementary classifiers comprises a subset of elementary classifiers.

[0257] In the example of Figure 3, the system comprises at least one elementary classifier, namely MO_FA, MO_FLA, PW_FLA, PW_FA configured to generate several indicators from respective derivations, each indicator being generated from a single derivation Dki, Dk2. The predetermined set comprises, for example, only these classifiers. This makes it possible to avoid deactivating the RR analyzer classifier when it uses this derivation.

[0258] Advantageously, as in the example of Figure 3, the classifiers of the sets of classifiers trained for the different arrhythmias are classified into groups. The classifiers of the same group use the same descriptors of the same derivation(s) to generate their indicators. For example, the morphological classifiers MO_FA and MO_FLA belong to the same group, just as the RR analyzer classifiers belong to the same second group and the P wave analyzer classifiers belong to the same third group.

[0259] According to an alternative or complementary embodiment, the selector INT, SE comprises a second selector making it possible to individually select and / or deselect groups of classifiers from the sets of elementary classifiers so that each indicator generated or, more generally, capable of being generated from the deselected group is in the deselected state.

[0260] According to an alternative or complementary embodiment, the selector INT, SE comprises a third selector making it possible to individually select and / or deselect the elementary classifiers of the sets of elementary classifiers dedicated to the respective arrhythmias so that each indicator generated or more generally, likely to be generated, from each deselected classifier is in the deselected state.

[0261] According to another additional or alternative example, the selector INT, SE comprises a fourth selector for individually selecting and / or deselecting arrhythmias such that each indicator generated, or more generally, likely to be generated for each deselected arrhythmia is deselected.

[0262] The different selectors can be the same or different.

[0263] Advantageously, as visible in Figure 1, the processing system S comprises a user interface INT.

[0264] The user interface 120 allows a user to enter data or commands so as to be able to interact with the programs according to the invention.

[0265] The INT user interface includes, for example, an INTS interface and output and an INTE input interface.

[0266] The input interface includes, for example, a keyboard or a pointing interface, such as a mouse, a light pen, a touchpad, a remote control, a voice recognition device, a haptic device.

[0267] The INTS output interface is designed to output information to a user, sensorily or electrically, such as visually or audibly. The output interface includes, for example, a graphical interface.

[0268] The INTS output interface can be the INTE input device, for example, in the case of a touchscreen tablet.

[0269] Advantageously, the INT user interface allows a user to select and / or deselect at least one of the elementary indicators. In other words, the selector function is performed by the INT user interface.

[0270] The user interface INT allows the user to interact with the classifier C via a control module CO, which is a functional brick of the system S, for the generation of the vector V.

[0271] The INTS output interface is, for example, configured to present to the user a list of indicators that can be selected and / or deselected and / or a list of one or more derivations that can be selected and / or deselected and / or a list of groups of individual classifiers that can be selected and / or deselected and / or at least one list of elementary classifiers that can be selected and / or deselected.

[0272] The INTE input interface allows receiving at least one selection or deselection command from the user, of an indicator and / or a derivation and / or a group of classifiers and / or an individual classifier taken from the displayed list(s).

[0273] The user interface INT interacts with the vector generator GV, via the control module CO, to generate the input vector V corresponding to the user's command(s). The control module CO is configured to generate, from the user's selection and / or deselection commands, a command, intended for the vector generator GV so that it generates the input vector in accordance with the user's commands.

[0274] Advantageously, the INT user interface is configured to enable selective display, on a screen of the INTS output interface, of at least one indicator and / or at least one global indicator and / or the sequence of states and / or at least one of the derivations and / or at least one of the descriptors used by at least one elementary classifier. This helps the user to define the indicators that he wishes to select and those that he wishes to deselect.

[0275] Alternatively or in addition, the selector includes an automatic SE selector, which is a functional building block of the S system, configured to:

[0276] ■ Check whether a condition for selecting or deselection of at least one indicator is met,

[0277] ■ automatically select or deselect said at least one indicator when the selection or deselect condition is met.

[0278] For example, the automatic selector SE comprises a characteristic analyzer of the derivation(s) of the time window configured to check whether the characteristics verify a condition of selection or deselection of at least one indicator is respected and a command generator, which can be implemented by the control module C, configured to generate a selection or deselection command intended for the vector generator GV so that it generates the input vector V.

[0279] The selection or deselection condition is, for example, a composite condition. For example, the characteristic analyzer is configured to check, for each derivation, whether a set of at least one quality criterion is met, comprising, for example, a first criterion according to which a signal-to-noise ratio is greater than a predetermined threshold (making it possible to check the quality of the derivation), and / or a second criterion according to which the absolute value of the average amplitude of the derivation is greater than a predetermined threshold (making it possible to check that the signal is indeed detected).

[0280] The SE automatic selector is configured to deselect each branch that does not meet the quality criterion.

[0281] The automatic selector SE interacts with the vector generator GV, via the control module CO, to generate the input vector V corresponding to the user's command(s). The control module CO is configured to generate, from the selection and / or deselection commands of the automatic selector SE, a command, intended for the vector generator GV so that it generates the input vector in accordance with the command of the automatic selector and possibly that of the user.

[0282] The first, second, and third selectors may be implemented by the same means, for example, the INT user interface or the automatic selector, or by separate means. For example, one of the selectors is implemented by the INT user interface and the others by the automatic selector.

[0283] Alternatively, the system is without a selector.

[0284] The various functional building blocks mentioned above, which may be artificial neural networks, may, more generally, be learning functions, some of whose parameters may be fixed by learning during a training phase. Once the training phase is complete, the parameters are fixed and the building blocks are said to be in the trained state.

[0285] These functional bricks are, for example, in the trained state, in the system S.

[0286] Process

[0287] The invention also relates to a method for characterizing a heart rhythm. The system S and the system SYS are configured to implement the method according to the invention. The steps of an example of the method according to the invention are shown in Figure 5.

[0288] Advantageously, but not necessarily, the method according to the invention comprises a step of acquiring the set of at least one derivation Dkj over the time window Fk. This step is implemented by the acquisition system ACQ.

[0289] The method also comprises a step 15 of generating descriptors from the set of at least one derivation Dki.

[0290] This step is advantageously implemented using the GD descriptor generator.

[0291] Alternatively, the generation of the descriptors is carried out prior to the method according to the invention. The method then begins with a step of receiving these descriptors by the system S.

[0292] Step 15 includes a step 151 of detecting and time-stamping the beats, i.e. to determine the times of occurrence or TOC, acronym for the Anglo-Saxon expression, of the consecutive R waves of at least one of the derivations.

[0293] Step 15 of generating the descriptors also comprises the generation 160 of statistical indicators characterizing the distribution of the RR intervals of the patient during the acquisition window Fk from at least one derivation of the set of at least one derivation Dki.

[0294] This step is advantageously implemented by running the GIS statistical indicator generator.

[0295] Step 160 advantageously comprises the generation 161 of a series RR, SRRk for the time window Fk, from at least one derivation of the set of derivations, for example the derivation Dki.

[0296] This step includes, for example, constructing an RR series by calculating the RR intervals between consecutive beats, from the timestamps of consecutive R waves.

[0297] It is advantageously implemented by running the GRR series generator.

[0298] Step 160 advantageously comprises the generation 162, from the RR series, of at least one statistical indicator Gk characterizing the statistical distribution of the R-Rk(h) intervals of the RR series, SRRk. This step is advantageously implemented by executing the RR series generator GRR.

[0299] This step advantageously includes the parameterization of a Gaussian mixture model and the calculation of at least one elementary statistical indicator from at least one parameter of the Gaussian mixture model parameter.

[0300] Step 161 advantageously comprises the generation of the sequence of reports between consecutive RR intervals.

[0301] It then advantageously comprises, for each arrhythmia, a step 164a, 164b of generating indicators IFAk, respectively IFLAk, of probabilities of presence of the arrhythmia from the sequence of ratios between RR intervals.

[0302] These steps are advantageously implemented by running the RR, RA_FA, RA_FLA ratio classifiers.

[0303] Step 15 advantageously comprises a step 152 of combination, for example of averaging, during which, for each derivation, at least a portion of a subset of the beats present on the derivation Dki, respectively Dk2, and detected by the R wave detector is combined, for example averaged.

[0304] In this combination one can combine whole beats or portions of beats as explained previously.

[0305] This step is implemented by running the MO combiner.

[0306] Step 15 advantageously comprises a portion extraction step 153, during which a portion Pki, Pk2 is extracted from one of the combinations or averages Mki, Mk2, obtained from one of the derivations Dki, Dk2. This part comprises a portion preceding the R wave of the combination obtained.

[0307] It can correspond to the entire combination obtained or to an incomplete part of the combination obtained.

[0308] For example, this part includes only a part of the combination or average preceding the R wave and without the R wave.

[0309] This portion is advantageously likely to include the P wave in the case of sinus rhythm.

[0310] This portion, for example, a part of predetermined duration preceding the R wave as explained previously. The method then comprises a step 20 of generating indicators of probabilities of presence of arrhythmias of a predetermined set of arrhythmias, during the acquisition window Fk. from descriptors of the set of at least one derivation Dki. This step is implemented using, that is to say by executing the classifier C.

[0311] This step 20 comprises, the generation, during a step 21, for each arrhythmia of the plurality of arrhythmias, of a set of indicators of probabilities of presence of the arrhythmia over the time window, using a set of elementary classifiers using the descriptors. The elementary classifiers use distinct respective descriptors.

[0312] In a particular embodiment, step 21 comprises the following steps, implemented for each arrhythmia:

[0313] ■ generating, during a step 210_FA, respectively 210_FLA, a first set of indicators IMFAki, IMFAk2, respectively IMFLAki, IMFLAk2 of probability of presence of the arrhythmia FA; respectively FLA, in the time window Fk using a first classifier, which is a morphological classifier MO_FA, respectively MO_FLA, using values ​​from the set of at least one derivation Dki, Dk2,

[0314] ■ generating, during a step 211_FA, respectively 211_FLA, a second set of indicators of probability of presence of the arrhythmia IPFAi, IPFA2; respectively IPFLA1, IPFLA2, in the time window Fk, using a second classifier, called P wave analyzer classifier PW_FA; respectively PW_FLA, using the first portions including a part preceding the R wave, of combinations of second portions of beats of the set of at least one derivation Dki, Dk2,

[0315] ■ generating, during a step 212_FA, respectively 212_FLA, a third set of indicators IRRFAk; respectively IRRFLAk, of probabilities of presence of the arrhythmia in the time window Fk, using a third classifier called RR interval analyzer classifier, RR_FA; respectively RR_FLA, from the set of statistical indicators representative of the distribution of R_R intervals of the electrocardiogram ECGk.

[0316] Step 212_FA, respectively 212_FLA is advantageously implemented by executing the RR interval analyzer classifier, RR_FA; respectively RR_FLA from a vector composed of at least one statistical indicator generated by the GIS generator, for example the indicator Gk characterizing the R_R distribution and the probability indicator generated for the arrhythmia considered IFAk, IFLAk.

[0317] In the case of multiple derivations, at least one classifier uses both derivations as described above. It then generates, during its execution, different indicators for the two derivations. Advantageously, it processes the derivations consecutively, so as to successively generate the IMFAki and IMFAk2 indicators. This is for example the case of the first and second classifiers.

[0318] The method then advantageously comprises a step 40 of generating an input vector Vk, by executing the input vector generator itself using the sets of probability indicators generated during step 21 for the time window Fk.

[0319] Advantageously, the input vector Vk is generated using the selection of indicators generated in step 21 for the time window Fk.

[0320] The method then advantageously comprises a step 50 of generating global indicators IGFAk, IGFLAk, of the probability of presence of arrhythmias in the time window Fk from the input vector Vk. This step is implemented by executing the global classifier GC.

[0321] Advantageously, the global indicators include an IGRSk indicator of the probability of presence of sinus rhythm in the time window Fk.

[0322] Advantageously, the preceding steps are repeated so as to generate sequences of K sets of global indicators IGFAk, IGFLAk, IGRSk from descriptors of sets of at least one successive derivation Dki, Dk2.

[0323] In other words, the method comprises the acquisition of a sequence of sets of at least one derivation, i.e. ECGk with k= 1 to K, acquired during K successive time windows F, for example consecutive, of a global acquisition window FG of global duration TG, the generation of descriptors from the set of at least one derivation acquired for each of these windows.

[0324] Alternatively, the method only comprises receiving descriptor sets respectively associated with sets of at least one derivation acquired successively, i.e. acquired over successive time windows Fk, and implementing steps 21, 40 and 50, for each of these sets of descriptors. Each set of global indicators IGFAk, IGFLAk, IGRSk is generated from a single set of at least one derivation Dki, Dk2 of the sequence of time windows Fk. The temporal order of the sets of global indicators IGFAk, IGFLAk, IGRSk is the same as that of the sets of derivations Dki, Dk2 from which they are respectively generated.

[0325] Advantageously, the method comprises a step 60 of temporal analysis, to define a sequence of states Ek of the heart rate corresponding to the states Ek of the heart rate on the successive time windows Fk from the sequence of sets of global indicators IGFAk, IGFLAk, IGRSk generated for the successive time windows Fk.

[0326] Advantageously, this step is implemented by running the TC time analyzer from the sets of global indicators IGFAk, IGFLAk, IGRSk generated for the successive time windows.

[0327] Advantageously, the method comprises, for at least one time window Fk, a step 30 of selecting and / or deselection of at least one of the indicators generated during step 21 for the corresponding time window Fk, so that the selection of indicators is composed of each selected indicator. The vector Vk is generated, during step 40, from the selection of indicators.

[0328] This step 30 is implemented by the selector.

[0329] In the example in the figure, the selection and / or deselection step is implemented after step 21.

[0330] Alternatively, the selection and / or deselection step is implemented before the step of generating the presence probability indicators by the elementary classifiers.

[0331] Advantageously, in the variant in which only each indicator of probability of presence of an arrhythmia of the selection is generated, a selection and / or deselection step is advantageously implemented before the step of generating the indicators.

[0332] Step 30 may be a selection step, prior to the generation of the presence probability indicators by the elementary classifiers, implemented only once for a plurality of time windows.

[0333] Step 30 is, for example, implemented by means of the human-machine interface and / or by means of the automatic selector.

[0334] In the case of the presence of the selection and / or deselection step 30, for a time window, the method may comprise the step 40 of generating the input vector using the vector generator using each indicator in the state selected prior to the selection / deselection as well as the step 50 of generating the global indicators from the input vector and the step 60 of temporal analysis from the global indicators generated for the time windows including the window Fk. The method advantageously comprises updating the input vector, the global indicators and the set of states by repeating the steps 40, 50 and 60 from the selection of states obtained following the selection step 30.

[0335] The training of the training functions used in the method according to the invention is advantageously implemented prior to the method according to the invention. It is advantageously supervised, at least for the classifiers.

[0336] Advantageously, the input data of at least one classifier, for example of each of the classifiers, are calibrated prior to their use by the classifier so that they are included in predetermined value intervals. The system advantageously comprises calibration means configured for these functions. And the method advantageously but not necessarily comprises these steps.

[0337] For example, the SYS system comprises means for calibrating the derivations so that the values ​​of the derivations used by the morphological classifier are calibrated values ​​of these derivations, i.e. values ​​included in a predetermined interval. The analog converter can, for example, perform this function. Advantageously, the training data are calibrated in the same way.

[0338] Material

[0339] From a hardware point of view, the processing system S can be seen as a computer interacting with a computer program product.

[0340] The system S comprises at least one computer, for example, a microcomputer, a computer network, an electronic component, a tablet, a smartphone or a personal digital assistant (PDA).

[0341] The MEM memory assembly comprises, for example, a computer-readable medium. The computer-readable medium is a tangible device readable by the reader of the computing unit, capable of storing electronic instructions and of being coupled to communication means of the system S.

[0342] In other words, the computer-readable medium is a tangible medium. In other words, it is not a transient signal in itself, such as radio waves or other freely propagating electromagnetic waves, such as light pulses or electronic signals. Such a computer-readable storage medium is, for example, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof.

[0343] For example, the readable medium is an optical disk, a magneto-optical disk, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a random access memory (RAM), a magnetic card or an optical card. The memory assembly can include an operating system and load the programs according to the invention. It includes registers adapted to record parameter variables created and modified during the execution of the aforementioned programs. A computer program comprising software instructions is then stored on the readable medium.

[0344] The data processing unit TR comprises at least one electronic circuit designed to manipulate and / or transform data represented by electronic or physical quantities in registers of the evaluation system and / or memories into other similar data corresponding to physical data in the memories of registers or other types of display devices, transmission devices or storage devices.

[0345] The data processing unit TR comprises, for example, memories, for storing data, operatively coupled to the data processing circuit and a reader adapted to read the computer-readable medium.

[0346] Each function of the system S is performed by causing the data processing unit TR to read a predetermined program from hardware such as the memory assembly MEM so that the data processing circuit performs calculations, controls communications and reads and / or writes data to the memory assembly MEM.

[0347] The process is performed on a single computer or on a system distributed among multiple computers (particularly through the use of cloud computing).

[0348] The data processing unit comprises at least one of the elements listed below: a set of one or more processors (for example a central processing unit (CPU), a graphics processing unit (GPU), a microcontroller and a digital signal processor (DSP)) capable of interpreting instructions in the form of a computer program a programmable logic circuit, such as an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a programmable logic device (PLD) programmable logic arrays (PLA), a system-on-chip (SOC), and / or an electronic card in which steps of the method according to the invention are implemented in hardware elements.

[0349] The program product may include the computer-readable recording medium.

[0350] Alternatively, the program instructions are obtained from an external source and downloaded via a network. This is particularly the case for applications. In this case, the computer program product comprises a computer-readable data carrier on which the program instructions are stored or a data carrier signal on which the program instructions are coded. The invention relates to a computer program product comprising the computer-readable medium containing instructions which, when executed by the processing circuit, cause the system S to implement the steps of the method according to the invention, i.e. to execute the functional building blocks of the system according to the invention.

[0351] The form of the program instructions is, for example, a source code form, a computer-executable form, or any intermediate form between a source code and a computer-executable form, such as the form resulting from the conversion of the source code via an interpreter, an assembler, a compiler, a linker, or a locator. Alternatively, the program instructions are microcode, firmware instructions, state definition data, integrated circuit configuration data (e.g., VHDL), or object code. The program instructions are written in any combination of one or more programming languages, for example, an object-oriented programming language (C++, JAVA, Python), a procedural programming language (e.g., C language).

[0352] The communication means allow communication between the elements of the system S and possibly between at least one element of the system and a device external to the system S. The communication means can establish a physical link between elements of the system and / or between an element of the system and a device external to the system and / or a remote communication link (wireless) between elements of the system and / or between an element of the system and a device external to the system.

[0353] The means of communication advantageously transmit data between the ACQ acquisition system and the system S. It allows, for example, communication between the box and the classifier C in the associated embodiment.

Claims

CLAIMS 1. System for characterizing a heart rhythm comprising a data processing unit configured to implement, by computer, the following steps: ■ when a selection from among presence probability indicators of sets of presence probability indicators consists of the presence probability indicators sets of presence probability indicators, generating, for each arrhythmia of the plurality of arrhythmias, using a set of elementary classifiers, a set of presence probability indicators of the arrhythmia over a time window using descriptors of a set of at least one derivation of an electrocardiogram of a patient acquired over the time window, so as to generate the indicators of the presence probability indicator sets, ■ when the selection includes an incomplete part of the indicators of the indicator sets and is non-empty, generating each indicator of the selection using at least one of the elementary classifiers using at least one descriptor of at least one derivation of the set of at least one derivation, ■ generate an input vector (Vk), the components of the input vector (Vk) comprising, among the indicators of the sets of presence probability indicators, only each presence probability indicator of the selection, ■ generating, using a global classifier (GC), global indicators of the probability of presence of arrhythmias in the time window (Fk) from the input vector (Vk), the system comprising a selector (INT, SE) making it possible to select and / or deselect at least one of the indicators of probability of presence so that the selection is composed of each indicator of probability of presence selected. System for characterizing a heart rhythm according to claim 1, the data processing unit being configured to implement, by computer, the following steps: ■ for each arrhythmia of the plurality of arrhythmias, generating, using the set of elementary classifiers, the set of indicators of probabilities of presence of the arrhythmia over the time window, using the descriptors of the set of at least one derivation of the patient's electrocardiogram acquired over the time window, ■ generate the input vector, the components of the input vector comprising, among the indicators of the sets of presence probability indicators, only each presence probability indicator of the selection, ■ generating, using the global classifier, global indicators of the probability of presence of arrhythmias in the time window from the input vector. System for characterizing a heart rhythm according to claim 1, in which the data processing unit is configured to generate, among the indicators of probability of presence of the sets of indicators of probability of presence, only each indicator of probability of presence of the selection when the selection comprises an incomplete part of the indicators of probability of presence of the sets of indicators of probability and is non-empty. System according to any one of the preceding claims, in which the value of each component of the input vector (Vk) associated with a deselected indicator is fixed at the same predetermined value.A system according to any preceding claim, wherein the selector allows each indicator of a subset of at least one of the indicators to be individually selected and / or de-selected. System according to any one of the preceding claims, wherein the set of at least one lead comprises several leads and wherein the selector comprises a first selector for deselecting and / or selecting the leads individually, such that each first indicator of the sets of indicators generated or capable of being, using a respective elementary classifier of a predetermined subset of the sets of elementary classifiers, from a deselected lead is deselected. System according to the preceding claim, wherein the predetermined subset is composed of each elementary classifier configured to generate first indicators of probabilities of presence of arrhythmias from first descriptors of the set of at least one lead, each first indicator being generated from a single lead.System according to any one of the preceding claims, in which the selector comprises a second selector for individually selecting and / or deselecting groups of elementary classifiers from the sets of elementary classifiers such that each indicator capable of being generated using a deselected group is in the deselected state, the groups of elementary classifiers using respective descriptors distinct from the set of at least one derivation, the elementary classifiers of the same group using the same descriptors. System according to any one of the preceding claims, in which the selector comprises a third selector for individually selecting and / or deselecting the elementary classifiers from the sets of classifiers such that each indicator capable of being generated using a selected elementary classifier is in the deselected state. System according to any one of the preceding claims, comprising a user interface (INT) allowing a user to select and / or deselect at least one of the elementary indicators. System according to any one of the preceding claims, wherein the selector comprises an automatic selector (SE) configured to: ■ check whether a condition for selecting or deselection of at least one indicator is met, ■ automatically selecting or respectively deselecting said at least one indicator when the selection or respectively deselecting condition is met. System according to any one of the preceding claims, wherein the generation of the set of indicators of probability of presence of the arrhythmia comprising: ■ generate a first set of indicators (IMFAki, IMFAk2) of probability of presence of the arrhythmia in the time window using a first classifier using values ​​from the set of at least one derivation (Dki, Dk2), ■ generating a second set of indicators of probability of presence of the arrhythmia in the time window, using a second classifier, from first portions, preceding the R wave, of combinations of second portions of beats of the set of at least one derivation, ■ generate a third set of indicators of probabilities of presence of the arrhythmia in the time window using a third classifier using a set of statistical indicators representative of the distribution of R_R intervals of the electrocardiogram. System according to any one of the preceding claims, in which the data processing unit is configured to define a sequence of states (Ek) of the individual's heart rhythm, taken from arrhythmias and sinus rhythm, from sets of global indicators generated for successive time windows. System according to the preceding claim, in which the definition of the sequence of states uses a Viterbi algorithm configured to determine the most probable sequence of states likely to be obtained by a hidden Markov model from the sets of global indicators.System according to any one of the preceding claims comprising an acquisition device (ACQ) comprising electrodes configured to acquire the set of at least one lead of the electrocardiogram and a processing sub-unit configured to use a descriptor generator to generate the descriptors of the set of at least one lead. System according to any one of the preceding claims, wherein the arrhythmias are atrial fibrillation and atrial flutter. System according to any one of the preceding claims, wherein each of the elementary classifiers and the global classifier is a trained classifier. Method for characterizing a heart rhythm comprising:. ■ when a selection from among presence probability indicators of sets of presence probability indicators is made up of the indicators of the sets of presence probability indicators, generating, for each arrhythmia of the plurality of arrhythmias, using a set of elementary classifiers, a set of presence probability indicators of the arrhythmia over a time window using descriptors of a set of at least one derivation of an electrocardiogram of a patient acquired over the window temporal, so as to generate the indicators of the sets of indicators of probability of presence, ■ when the selection includes an incomplete part of the presence probability indicators of the presence probability indicators of the sets of indicators and is non-empty, generating each indicator of the selection using at least one of the elementary classifiers using at least one descriptor of at least one derivation of the set of at least one derivation, ■ generate an input vector (Vk), the components of the input vector (Vk) comprising, among the indicators of the sets of presence probability indicators, only each presence probability indicator of the selection, ■ generate, using a global classifier (GC), global indicators of the probability of the presence of arrhythmias in the time window (Fk) from the input vector (Vk), ■ generating, using a global classifier (GC), global indicators of probability of presence of arrhythmias in the time window (Fk) from the input vector (Vk), selecting or deselecting at least one of the indicators of probability of presence so that the selection is composed of each indicator of probability of presence selected. assigned according to the preceding claim comprising: ■ For each arrhythmia of the plurality of arrhythmias, generating, using the set of elementary classifiers, the set of indicators of probabilities of presence of the arrhythmia over the time window, using the descriptors of the set of at least one derivation of the patient's electrocardiogram acquired over the time window, ■ generate the input vector, the components of the input vector comprising, among the indicators of the sets of presence probability indicators, only each presence probability indicator of the selection, ■ generate, using the global classifier, global indicators of the probability of the presence of arrhythmias in the time window from the input vector, ■ selecting or deselecting at least one of the presence probability indicators such that the selection is composed of each selected presence probability indicator. The method of claim 18, wherein generating, among the presence probability indicators of the presence probability indicator sets, only each presence probability indicator of the selection when the selection comprises an incomplete portion of the presence probability indicators of the presence probability indicator sets and is non-empty.A computer program product comprising a readable information medium, on which is stored a computer program comprising program instructions, the computer program being loadable onto a data processing unit and adapted to cause the implementation of steps of the method according to the preceding claim, when the computer program is implemented on the data processing unit. A readable information medium comprising program instructions forming a computer program, the computer program being loadable onto a data processing unit and adapted to cause the implementation of steps of the method according to any one of claims 18 to 20 when the computer program is implemented on the data processing unit.

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