METHOD AND DEVICE FOR DETECTING REPRESENTATIVE CARDIAC ELECTRICAL ACTIVITY

Abstract

METHOD AND DEVICE FOR DETECTING REPRESENTATIVE CARDIAC ELECTRICAL ACTIVITY The invention relates to a method for detecting representative cardiac activity comprising the arrangement of a plurality of electrodes, the measurement of the value of electrophysiological descriptors, and the calculation (CALC) of a score. The invention also relates to a device for detecting representative cardiac activity. Figure for the abstract: Fig. 1

Description

Title of the invention: METHOD AND DEVICE FOR DETECTING REPRESENTATIVE CARDIAC ELECTRICAL ACTIVITY Field of the invention

[0001] The field of the invention relates to methods and devices for generating an electrophysiological parameter related to a cardiac activity of an individual. More particularly, the field of the invention relates to methods implemented by means of surface electrodes recording signals used to detect a representative cardiac electrical activity. State of the art

[0002] Currently, there are ways to analyze signals from surface electrodes in order to identify electrical activity of the myocardium. This analysis makes it possible to prevent certain cardiac pathologies by identifying singular electrical activities within the myocardium.

[0003] These means, even if they are very reliable, only focus on a limited number of indicators of cardiac activity derived from the analysis of cardiac electrical signals. For example, the QRS duration (ventricular depolarization time) is frequently used as an indicator of cardiac pathologies. This measurement is a very good indicator for detecting certain singular activities of the heart, but this measurement alone is only an overview of certain cardiac characteristics. It is for example possible, for a patient suffering from a given pathology, that the measurement of the QRS duration may be representative of an electrophysiological singularity. However, this measurement may prove insufficient to characterize certain electrophysiological activities of an individual, in particular an electrophysiological activity that can be used or corroborated with other variables to anticipate a cardiac risk.

[0004] Thus, by focusing on few parameters, it is common not to detect characteristic electrophysiological activities.

[0005] The currently known methods have the disadvantage, by concentrating on a very small number of indicators, of only providing a very limited view of a patient's cardiac activity.

[0006] To date, existing solutions aim to confirm or detect the presence of a singular electrical activity. However, this activity most often remains at a low level or drowned in noise or masked by a larger electrical pattern such as a QRS complex. No solution makes it possible, from a plurality of surface electrodes, to determine the characteristics of a source of a singular electrical activity or even to know a wide range of electrophysiological variables of a patient likely to form a corpus of singularities which can be used for medical purposes or for the prevention of a cardiac risk.

[0007] The invention therefore aims to propose a method for measuring the cardiac activity of a patient which overcomes the aforementioned drawbacks. Summary of the invention

[0008] According to one aspect, the invention relates to a method for generating an electrophysiological parameter which comprises: • selection of a subset of electrophysiological descriptors from a set of predefined electrophysiological descriptors according to an input parameter defining a measurement context, each electrophysiological descriptor of the subset being associated with at least one channel, one type of signal, one signal marker and one statistical calculation method; • arrangement of a plurality of surface electrodes on the body of a patient; • recording of a plurality of cardiac electrical activities defining said pathways, each pathway being obtained by recordings from at least two electrodes; • estimation of all the electrophysiological descriptors of the subset, each electrophysiological descriptor being calculated from the statistical modality which is applied to the signal marker of the acquired signal according to the type of signal on a selected channel associated with said electrophysiological descriptor; • comparison of the value of all the electrophysiological descriptors with at least one threshold value specific to all the electrophysiological descriptors, said at least one threshold value being defined by a statistical distribution of said descriptors of a set of healthy patients; • calculation of a score defining an electrophysiological parameter based on exceeding at least one threshold value defined by the statistical distribution.

[0009] An advantage of the invention is to propose a method taking into account a set of different electrophysiological descriptors to characterize cardiac activity. The possibility of selecting the measurement context by taking descriptors which concern areas on the patient's body, types of signals analyzed, signal markers and different statistical modalities makes it possible to make a composite measurement of the patient's cardiac activity. Composing the score according to the exceedance of a threshold for a plurality of descriptors with different modalities makes it possible to effectively detect cardiac activities that may be characteristic. Finally, this method makes it possible to form a corpus of electrophysiological variables making it possible to characterize a singular activity of an individual's cardiac activity.

[0010] For example, for a patient suffering from a cardiac pathology, it is possible that the measured indicator (for example the duration of the QRS) is normal, while it is considered to be a characteristic measurement of this pathology. In the case of the invention, as a plurality of descriptors is used, even if one of these gives a value which is within the norm, the other descriptors used can alert us to the presence of a characteristic cardiac activity. There is therefore much better efficiency in the detection of said characteristic activity, compared to known methods.

[0011] According to one embodiment, at least one descriptor of the subset is associated with a statistical modality different from that of a second descriptor of the subset and a signal marker different from that of the second descriptor. This arrangement makes it possible to obtain a subset which comprises several descriptors carrying physiological information which is different from one another.

[0012] The various steps of the method of the invention can be implemented by calculation means such as computers. These can be those of an electronic card of dedicated equipment or those of a computer or a remote data server.

[0013] According to one embodiment, the comparison step is performed by comparing the value of the set of descriptors with the threshold value defined by a statistical distribution of said descriptors of the set of healthy patients of dimension equal to the number of descriptors of the subset. This arrangement makes it possible to define a relevant threshold value as a function of a set of healthy patients.

[0014] According to one embodiment, the comparison step is performed by comparing the value of each electrophysiological descriptor with at least one threshold value specific to said electrophysiological descriptor, said at least one threshold value being defined by a statistical distribution of said descriptor of a set of healthy patients; and the step of calculating the score defining the electrophysiological parameter is performed by incrementing said score each time an electrophysiological descriptor crosses the at least one threshold value specific to it. This characteristic makes it possible to define the calculation of the score by comparing the values ​​with a control population. The calculated score is therefore representative of the cardiac activity of the patient.

[0015] According to one embodiment, for at least one electrophysiological descriptor, the at least one channel comes from a predefined area of ​​the patient's body among a set of predefined areas. This arrangement makes it possible to take into account the location of the measurements on the patient's body, the areas being predefined.

[0016] According to one embodiment, the subset comprises at least one second geographic descriptor which is associated with several channels and several geographic groups, each geographic group being formed by a central channel and the at least four channels close to the central channel, the value of the electrophysiological descriptor being determined: • by comparing the value, for each geographical group, of the measurement of each channel according to the type of signal and the signal marker selected with at least one geographical threshold value specific to said electrophysiological descriptor and to said channel; and • by counting the number of geographical groups for which the value of at least three channels exceeds the geographical threshold value specific to it.

[0017] This arrangement makes it possible to have a second type of descriptors which take into account geographical groups. The addition of this other type of descriptors makes it possible to take into account the concentration of singular measured values ​​and enriches the subset of selected descriptors.

[0018] According to one embodiment, the input parameter defining the measurement context defines a subset of descriptors characteristic of a given cardiac pathology. This characteristic makes it possible to adapt the subset of descriptors to a cardiac pathology that one wishes to analyze.

[0019] According to one embodiment, the input parameter defining the measurement context defines a subset of descriptors characteristic of a patient profile comprising at least one age and / or one gender / sex. Taking these characteristics into account makes it possible to increase the precision of the generated electrophysiological parameter.

[0020] According to one embodiment, each first electrophysiological descriptor of the subset is associated with at least one channel of a predefined area of ​​the patient's body from a set of predefined areas and in that for each electrophysiological descriptor, the predefined area on the patient's body is chosen from: • An upper right area of ​​the torso; • An upper left area of ​​the torso; • A lower right area of ​​the torso; • A lower left area of ​​the torso; and • The patient’s entire torso.

[0021] This arrangement makes it possible to have descriptors that take into account the geographical location on the patient's body of the measurements taken. Thus, we have a variety of descriptors that take into account several types of information.

[0022] According to one embodiment, the plurality of electrodes comprises at least fifteen electrodes, preferably twenty-five. This arrangement makes it possible to obtain a mesh of measuring electrodes that is sufficiently fine to obtain the electrophysiological parameter.

[0023] According to one embodiment, for each electrophysiological descriptor, the type of signal analyzed is chosen from: • A unipolar signal taken between an electrode in the chosen body area and a reference electrode; • A vertical bipolar signal taken between two electrodes of the given area, one of the two electrodes being offset along a vertical line relative to the other electrode; • A horizontal bipolar signal taken between two electrodes of the given area, one of the two electrodes being offset along a horizontal line relative to the other electrode; and • A Laplacian signal estimated by subtracting from the potential of a central electrode the average of the voltage of the eight electrodes directly near said central electrode.

[0024] This arrangement makes it possible to take into account several types of signals and therefore to have descriptors which take into account several types of signal measurement. Thus, the descriptors take into account several types of information and more faithfully reflect the state of said patient.

[0025] According to one embodiment, at least one reference electrode is arranged on a lower limb or an upper limb of the patient. This arrangement allows the measurement of a reference potential.

[0026] According to one embodiment, a plurality of reference electrodes is arranged on the surface of the patient's lower or upper limb(s). This arrangement makes it possible to obtain a reference potential with great precision.

[0027] According to one embodiment, for at least one descriptor, the signal marker is the measurement of the voltage of an averaged signal. The averaging of the signal makes it possible to obtain a stable measurement of said voltage.

[0028] According to one embodiment, for at least one descriptor, the signal marker is the measurement, on the signal averaged and filtered between 40 and 250 Hertz, of the duration of depolarization of the ventricles or of the fragmentation of the signal during the depolarization of the ventricles. The duration of depolarization of the ventricles is a very representative measurement of cardiac activity.

[0029] According to one embodiment, for at least one descriptor, the signal marker is the measurement on the discrete wavelet decomposition of the signal: • Energy of the sum of wavelets; • Kurtosis; • Fisher's asymmetry coefficient; or • The number of local minima.

[0030] This arrangement allows the consideration of different types of signals, all carrying different information, which enriches the selected subset.

[0031] According to one embodiment, for at least one descriptor, the signal marker is the measurement on the continuous wavelet decomposition of the signal of the number of chains of local maxima. This measurement is a measurement which makes it possible to account for a singular cardiac activity.

[0032] According to one embodiment, for at least one descriptor, the signal marker is the measurement on the wavelet taken between 256 and 512 hertz of the signal: • From Kurtozis; or • The number of reduced amplitude zones.

[0033] These measurements make it possible to account for measurements on the curve such as its flattening.

[0034] According to one embodiment, for at least one descriptor, the signal marker is the measurement on the wavelet taken between 128 and 256 hertz of the signal: • From Kurtozis; • The number of reduced amplitude zones; or • RMS (Root Mean Square).

[0035] These measurements make it possible to account for measurements on the curve such as its flattening.

[0036] According to one embodiment, for at least one descriptor, the signal marker is the measurement on the wavelet taken between 64 and 128 hertz of the RMS (Root Mean Square). This measurement makes it possible to report the patient's cardiac activity.

[0037] According to one embodiment, for each first descriptor, the statistical modality is chosen from: • The minimum at the fifth percentile of the measured signal values ​​on each electrode of the predefined area; • The maximum at the ninety-fifth percentile of the measured signal values ​​on each electrode of the predefined area; • The average of the measured signal values ​​on each electrode of the predefined area; • The standard deviation of the measured signal values ​​on each electrode of the predefined area; • The median of the measured signal values ​​on each electrode of the predefined area; and • The interquartile range of the measured signal values ​​on each electrode in the predefined area.

[0038] This arrangement makes it possible to select a statistical modality which makes it possible to process the multiple measurements carried out while choosing the statistical modality which is relevant, depending on whether one wishes to analyze maxima or minima for example. These modalities also make it possible to avoid aberrant extreme measurements.

[0039] According to one embodiment, the estimation of the electrophysiological descriptors is carried out during a low breathing phase of the patient. This arrangement makes it possible to carry out the measurement during a breathing phase which does not disturb the measurement.

[0040] According to one embodiment, a plethysmography belt is placed on the patient to estimate the patient's breathing phases.

[0041] According to one embodiment, the method of the invention may comprise a prior step aimed at selecting a subset of electrophysiological descriptors characteristic of cardiac electrical activity.

[0042] For example, this preliminary step can be carried out by a method of selecting a subset of first electrophysiological descriptors characteristic of a characteristic cardiac electrical activity from a set of predefined first descriptors. Each first electrophysiological descriptor is associated with at least one channel, a signal type, a signal marker and a statistical calculation modality. The method comprises a recording of a plurality of electrical activities defining said channels and comprises: • Estimation for a first set of patients not affected by a predefined condition, of values ​​of each descriptor of the set of predefined descriptors; • Estimation for a second set of patients affected by the predefined condition, of values ​​of each descriptor of the set of predefined descriptors; • Generation of a first characteristic vector of the state of each patient of the first and second sets of patients in which each component corresponds to a state relative to the predefined state; • Generation of a descriptor vector for each descriptor in a metric space in which each component corresponds to the value of the descriptor for each patient; • First quantification for each descriptor of a first proximity factor between the values ​​of the components of the first characteristic vector and the values ​​of the components of the descriptor vector; • First selection and inclusion in the subset of at least one descriptor having optimal quantified proximity factor values; • Second quantification: • a second proximity factor between the values ​​of the components of each descriptor not selected during the first selection step and the values ​​of the components of the descriptor vector(s) selected during the first selection step; and • a third proximity factor between the values ​​of the components of each descriptor not selected during the first selection step and the values ​​of the components of the characteristic vector; • Second selection and inclusion in the subset of at least one new descriptor based on the value of the second proximity factor and the value of the third proximity factor quantified during the second quantification step.

[0043] According to one aspect, the invention relates to a device or system comprising means for implementing the method of the invention. The means may comprise computers, memories, electronic cards, electrodes and electrode supports. The device or system of the invention may comprise computers or servers when computing resources are necessary. The invention is described below so that the characteristics described may relate to the method of the invention or to the device or system of the invention.

[0044] The invention also relates to a device for generating an electrophysiological parameter which comprises: • a plurality of surface electrodes configured to be deposited on a patient's body and to measure an electrical potential of the surface of the patient's body, each surface electrode defining a pathway; • a means of measuring the signal of each channel; • a calculation means configured for: • select a subset of descriptors from a set of predefined descriptors according to an input parameter defining a measurement context, each electrophysiological descriptor of the subset being associated with at least one channel, one signal type, one signal marker and one statistical calculation modality; at least one descriptor of the subset being associated with a statistical modality different from that of a second descriptor of the subset and a signal marker different from that of the second descriptor; • recording a plurality of cardiac electrical activities defining said pathways, each pathway being obtained by recordings from at least two electrodes; • estimate all the electrophysiological descriptors of the subset, each electrophysiological descriptor being calculated from the statistical modality which is applied to the signal marker of the acquired signal according to the type of signal on a selected channel associated with said electrophysiological descriptor; • compare the value of all the electrophysiological descriptors with at least one threshold value specific to all the electrophysiological descriptors, said at least one threshold value being defined by a statistical distribution of said descriptors of a set of patients; • calculate a score defining an electrophysiological parameter based on exceeding at least one threshold value defined by the statistical distribution.

[0045] According to one embodiment, the electrodes are arranged on the surface of the patient using adhesive strips. This arrangement allows for convenient and rapid arrangement of the adhesive strips on the patient.

[0046] According to one embodiment, the device comprises a device for detecting the breathing phases of a patient, preferably a plethysmography belt. This arrangement makes it possible to carry out the measurement during a breathing phase which does not disturb the measurement.

[0047] According to one embodiment, the device is capable of implementing the method according to the invention. Brief description of the figures

[0048] Other characteristics and advantages of the invention will emerge on reading the detailed description which follows, with reference to the appended figures, which illustrate:

[0049] [Fig-1]: a schematic flowchart of the method according to the invention;

[0050] [Fig.2]: a flowchart showing the selection of a descriptor;

[0051] [Fig.3]: a front view of a patient's torso on which a plurality of measuring electrodes for implementing the method according to the invention;

[0052] [Fig.4]: a view of a plurality of measuring electrodes in a predefined area of ​​the patient's body;

[0053] [Fig.5]: two curves illustrating the method of calculating an asymmetry index;

[0054] [Fig.6]: a graph illustrating the method of calculating a Kurtosis index;

[0055] [Fig.7]: a view of a curve illustrating the method of calculating the number of zones of reduced amplitude. Description of the invention

[0056] According to a first aspect, the invention relates to a method for generating an electrophysiological parameter. By "electrophysiological parameter" is meant data from an electrical measurement which is characteristic of a physiological activity of a patient.

[0057] The invention also relates to a device for generating an electrophysiological parameter. The device according to the invention is capable of implementing the method mentioned above. Subsequently, the elements described in this description will be applicable both to the method according to the invention and to the device according to the invention.

[0058] The method according to the invention will be described with the support of [Fig.l], which is a schematic flowchart of the method according to the invention.

[0059] According to a first aspect, the invention relates to a method for generating an electrophysiological parameter. A first step of this method is a step of selecting a subset of first descriptors {Dk} from a set of predefined descriptors {DN}. By "electrophysiological descriptor D;" is meant a context for measuring electrical data associated with an activity detected on the surface of a patient. Each first electrophysiological descriptor D; is associated with the recording of at least one channel V;. The recording of a channel V; corresponds to the recording of the electrical activity captured by at least one electrode EL arranged on the surface of a patient's body. Each first descriptor D; is associated with at least one channel V; on a predefined area Z; on the patient's body.By "predefined zone Z;" is meant the area of ​​the patient's body on which the measuring electrode(s) EL are placed whose electrical activities are recorded to obtain the V; pathway(s). Thus, the surface of a patient's body can be segmented into different functional and / or geometric and / or physiological zones. These zones can therefore correspond to geographical zones on the patient's body, to functional zones in relation to the patient's cardiac activity, or to zones corresponding to the patient's physiology and / or physiology.

[0060] Each first descriptor D; is associated with a type of measured signal T; on the selected channels V]. By type of signal T; we mean for example the measurement of a voltage between two electrodes. The different types of signals T; which can be selected will be described later.

[0061] Each first descriptor D; is associated with a signal marker M;. By "signal marker M;", we mean the characteristic of the signal type T; which will be measured. A set of signal markers M; used in the context of the invention comprises the measurement of frequency and energy characteristics of the signal. The different signal markers Mi that can be selected are described below.

[0062] Each first descriptor D; is associated with a statistical modality MS;. By “statistical modality MS;”, we mean a statistical measurement modality applied to the measurements carried out on the signals. A statistical modality MS; can for example be the calculation of the average of the signal marker M; measured on several electrodes. The different statistical modalities MS; which can be selected will be described later.

[0063] Each first electrophysiological descriptor D; is therefore defined both by a selection of the predefined zone Zi of the patient's body, the signal type T;, the signal marker M; and the statistical modality MS;.

[0064] In the method according to the invention an input parameter Inp is acquired. The input parameter Inp defines a measurement context. By "measurement context" is meant the number of descriptors D; which will be selected as well as, for each descriptor Di, the combination of the predefined zone Z, the signal type T;, the signal marker M; and the statistical modality MS; which are selected.

[0065] At least two first descriptors D;, Dj of the subset {Dk} are associated with a different statistical modality MS;. In other words, two of the first descriptors D;, Dj are not associated with the same statistical modality MS;.

[0066] At least two first descriptors D;, Dj of the subset {Dk} are associated with a different signal marker M;. In other words, two of the first descriptors Di, Dj are not associated with the same signal marker M;.

[0067] A plurality of EL surface electrodes are arranged on the patient's body. Thus, at least two electrodes are deposited on the patient's skin. These EL electrodes are configured to collect an electrical potential at the surface of the patient's skin.

[0068] An ENR recording of a plurality of cardiac electrical activities defining said V; pathways is carried out. During this step, the recording of all available pathways is preferably carried out. In other words, the electrical activity of each electrode of the plurality of electrodes arranged on the patient's body is recorded. For the purposes of the recordings, the invention can be implemented in particular by means of a memory allowing the recording of the data acquired and / or the data processed by one of the steps of the method of the invention.

[0069] An EST estimation of the set of electrophysiological descriptors D; of the subset of descriptors {Dk} is performed. In this step of the method, each value of each descriptor D; of the subset {Dk} is calculated. The calculation is carried out for each descriptor D; in the predefined zone Zi5 with the signal type T,, on the signal marker M;, and according to the statistical modality MS; which are associated with it.

[0070] The calculated values ​​of each descriptor D; are then compared to a threshold value Vseuii. Each threshold value Vseuii is characteristic of the electrophysiological descriptor D; in question. Thus, there is a threshold value Vseuii specific to each descriptor D;.

[0071] A score is then calculated from the number of descriptors D; whose value has exceeded the threshold value Vseuii specific to it. By exceeding the threshold value Vseuii we mean an exceedance by a higher value or by a lower value. The direction of exceeding the threshold is specific to each descriptor D;. Each time the threshold value Vseuii is exceeded by a descriptor D,, the score is incremented.

[0072] Finally, we obtain a score which defines an electrophysiological parameter.

[0073] The method according to the invention therefore makes it possible to obtain an electrophysiological parameter which takes into account the calculation of the value of several descriptors D; which have measurement and calculation methods which are very different. Thus, the electrophysiological parameter does not take into account only one parameter to calculate the score defining the electrophysiological parameter. Each descriptor carrying information on the cardiac activity of the patient which is specific to it, the method according to the invention makes it possible to generate the electrophysiological parameter which is most representative of the cardiac activity of the patient.

[0074] Alternatively, the comparison step can be performed by taking into account the statistical distribution of the set of descriptors in a space having a dimension equal to the number of descriptors D; of the subset {Dk}. According to this alternative, the set of descriptors is compared to a threshold value from the multi-dimensional distribution. Electrophysiological descriptors

[0075] As described previously, each electrophysiological descriptor D; is associated with at least one channel V; of a predefined area Z; of the patient's body, a signal type T;, a signal marker M;, and a statistical calculation modality MS;. We will describe these four elements in detail below. The selection of a descriptor D; from the set of descriptors {DN} is illustrated by [Fig.2], which is a flowchart showing the process of selecting a descriptor D;. To select a descriptor D;, a selection is made of a predefined area Zik from a set of predefined areas Z. A selection is made of a signal type Tik from a set of signal types Tj. A selection is made of a signal marker Mik from a set of signal markers M;. A selection is made of a statistical modality MSik from a set of statistical modalities MS;. Patient's body area

[0076] As shown in [Fig. 3], a plurality of EL electrodes is deposited on the surface of the patient's torso. The number of electrodes in the example shown may vary; for example, it may be much lower than the number shown, or much higher. The plurality of EL electrodes covers a large portion of the patient's torso. As can be seen, this plurality of electrodes is separated into four distinct zones thereon. A first portion of the EL electrodes is located on an upper right zone Zi of the patient's torso. A second portion of the EL electrodes is located on an upper left zone Z2 of the patient's torso. A third portion of the EL electrodes is located on a lower right zone Z3 of the patient's torso. A portion of the EL electrodes is located on a lower left zone Z4 of the patient's torso. Finally, the plurality of EL electrodes is included in an area comprising the entire Z5 of the patient's torso.

[0077] The demarcation between the areas located on the left of the torso and those located on the right of the torso is a vertical line passing through the center of the torso, or substantially through the center of the torso.

[0078] The demarcation between the areas located at the bottom of the torso and those located at the top of the torso is a horizontal line passing through the center of the torso.

[0079] Each zone has a predefined number of EL electrodes. The number of electrodes arranged per zone may be around thirty. For example, there may be thirty EL electrodes per zone.

[0080] Advantageously, each zone comprises the same number of electrodes EL. Of course, the zone comprising the entire torso Z5 comprises a different number of electrodes than the others, because this zone Z5 comprises the meeting of the electrodes of all the other zones Zb Z2, Z3 and Z4.

[0081] Subsequently, when we speak of the selection of the electrodes of a zone, we will speak of the selection of one or more EL electrodes of said zone.

[0082] Alternatively, a smaller number of EL electrodes may be provided, for example nine EL electrodes per zone Zb Z2, Z3, and Z4. Thus, in this case, zone Z5 comprising the entire torso of the patient comprises thirty-six EL electrodes.

[0083] Each channel V; is obtained by recording the electrical activity of at least two EL electrodes. These at least two electrodes may be two electrodes from one or more areas of the patient's torso. These at least two electrodes may also be one electrode from one area of ​​the patient's torso and one reference electrode. Geographic groups

[0084] According to one embodiment, the subset {Dk} comprises at least one second geographic electrophysiological descriptor D;. The at least one geographic descriptor D; is associated with at least one channel V; and with several geographic groups. A geographic group is formed by an electrode EL and the four electrodes EL which are located directly near it. A geographic group is shown in [Fig. 3]. This geographic group comprises a central electrode ELb as well as the electrode located directly above it. It also comprises the electrode located directly below the central electrode ELb the electrode located directly to the left of the central electrode ELi and the electrode located directly to the left of the central electrode ELp These four electrodes are shown hatched in [Fig. 3]. For the second geographic descriptors, the measurement of the value according to the signal type and the signal marker is carried out on all available geographic groupings of the plurality of electrodes arranged on the patient's body.Other electrode arrangements can be considered. In particular, one can select the central electrode and the four electrodes located at the top left, top right, bottom left and bottom right of the central electrode ELp. These are the electrodes appearing without a pattern in [Fig. 3]. One can also provide more electrodes, for example nine electrodes in the geographical group. These are the nine EL electrodes in [Fig. 3] for example.

[0085] For each geographic grouping, the value obtained for each EL electrode of said grouping is compared to at least one geographic threshold value. The at least one geographic threshold value is obtained from a statistical distribution of the value of the EL electrode considered from a set of healthy patients. When at least three electrode values ​​of a geographic group exceed their geographic threshold value, then the geographic group is considered significant. Finally, the value of the descriptor is the number of significant geographic groups counted. Alternatively, a geographic group can be considered significant from two electrodes exceeding their geographic threshold value, or with four electrodes.

[0086] It can be noted that in the case of a second geographical descriptor, the statistical modality is not taken into account, the value of the descriptor being the number of geographical groupings detected.

[0087] Advantageously, the subset {Dk} of descriptors comprises at least one first descriptor D; and at least one second geographic descriptor. Additionally, the subset {Dk} comprises several first descriptors D;. According to this variant, the subset {Dk} comprises a second geographic descriptor per signal marker used in the first descriptors Di of the subset {Dk}. Signal type

[0088] Each electrophysiological descriptor D; is associated with a signal type Tj. In this section, we will rely on [Fig.4], which is a schematic representation of nine contiguous EL electrodes on the patient's body. In this figure, each circle represents an EL electrode. The dotted areas represent the different EL electrodes selected in the different signal types.

[0089] The signal type T; can preferably be chosen from four different signal types.

[0090] A first type of signal T; is a unipolar signal. A unipolar signal is a signal taken between an electrode EL of the predefined area Z; and a reference electrode. In other words, the unipolar signal type is the voltage measured between the electrode of the predefined area and the reference electrode. By "reference electrode" is meant an electrode which is not located in one of the areas of the patient's torso defined previously. For example, a reference electrode can be an electrode placed on a lower limb or an upper limb of a patient.

[0091] A second type of signal T; is a vertical bipolar signal. A vertical bipolar signal is a signal taken between an electrode of the predefined area and the electrode located directly below it on the patient's torso. In other words, the type of signal acquired is the voltage between the two electrodes EL. A vertical bipolar signal is taken between two electrodes of the predefined area Zj. These two electrodes form a vertical bipole Bv.

[0092] A third type of signal T; is a horizontal bipolar signal. A horizontal bipolar signal is a signal taken between an electrode of the predefined area and an electrode located directly next to it along a horizontal line on the patient's torso. In other words, the type of signal acquired is the voltage between the two electrodes EL. The horizontal bipolar signal is taken between two electrodes of the predefined area Z;. These two electrodes form a horizontal bipole Bh.

[0093] A fourth type of signal T; is a Laplacian signal. A Laplacian signal is estimated by subtracting from the potential of a central electrode ELi the average of the potentials of the eight electrodes which are directly near said central electrode. In other words, the Laplacian signal is a compound voltage between the central electrode ELi and a set of electrodes EL peripheral to the central electrode EL,. These nine electrodes form a Laplacian electrode ELkp. Signal markers

[0094] Each electrophysiological descriptor is associated with a signal marker M;. A signal marker M; is a measurement modality of a physical quantity associated with the types of signals measured by the EL electrodes.

[0095] The signal marker M; associated with a descriptor D; is preferably chosen from among fourteen signal markers M;. These signal markers Mi are described below.

[0096] A first signal marker M; corresponds to the measurement of an averaged electrical signal. By averaged electrical signal, we mean the calculation, carried out on the measured voltage, of the average between the maximum peak and the minimum peak of the QRS. This measurement of the duration of the QRS is generally fairly representative of cardiac activity.

[0097] We will now present two signal markers which are measured on the filtered signal between 40 and 150 hertz. The signal is filtered using a band-pass filter. Advantageously, the band-pass filter is a bidirectional Butterworth filter. A bidirectional Butterworth filter has the advantage of limiting oscillations due to filtering, which makes the calculation of values ​​for certain signal markers M; more accurate.

[0098] A signal marker M; on the filtered signal is the duration of the QRS on the filtered signal. To measure this quantity, a marker is placed at the start of the QRS and a second marker is placed at the end of the QRS. The time separating the two markers is measured. This operation can be carried out automatically using a QRS start and end detection algorithm. Alternatively, this duration can be measured manually by an operator on an interface. It is also possible to provide automatic measurement of the QRS duration and manual control of said measurement by the operator on the interface.

[0099] According to one embodiment, the duration of the QRS is detected by moving a sliding window measuring the energy of the filtered signal. When an energy threshold is exceeded, a marker is placed which marks the start of the window. The QRS end marker is placed in the same way.

[0100] Another signal marker M; is the measurement of the fragmentation of the averaged signal filtered between 40 hertz and 250 hertz. According to this marker M;, the number of QRS peaks on the filtered signal is measured. By peak, we mean a local maximum of the curve of the filtered signal. The number of peaks is measured on the section of the curve corresponding to the QRS. The start and end marks of the QRS are set in the same way as for the previous marker M;, which as a reminder is the duration marker of the QRS on the filtered signal.

[0101] The following markers M; are calculated on the wavelet decomposition of the signal. For these markers M;, one can use the continuous wavelet decomposition or the discrete wavelet decomposition.

[0102] The four markers M; presented below are calculated on the discrete wavelet decomposition.

[0103] A first marker M; is the calculation of the energy on the discrete wavelet decomposition of the signal. Concretely, the energy is calculated on the sum of the coefficients on several levels. Typically, the sum of the coefficients is carried out between 64 hertz and 1024 hertz, i.e. on the four levels of this frequency band. According to one embodiment, the measured energy is normalized with respect to the duration of the QRS. Alternatively or additionally, the energy is normalized with respect to the maximum amplitude of the signal.

[0104] A second marker Mi calculated on the discrete wavelet transform is the measurement of the index called Kurtosis Sku. By "Kurtosis" we mean an index allowing to estimate the spread of a given curve. [Fig.6] illustrates several measurements of the spread of the curve on three example curves. For a flat curve P, the Kurtosis index is negative. For a slender curve E, the index is positive. Thus, the more the curve is spread, the more the Kurtosis is negative. When the curve is narrow, the Kurtosis is positive. The Kurtosis of a curve representing a normal distribution N is equal to zero. Concretely, the Kurtosis Sku is calculated on the sum of the coefficients on several levels of the discrete wavelet decomposition. Typically, the sum of the coefficients is carried out between 64 hertz and 1024 hertz, that is to say on the four levels of this frequency band.

[0105] A third marker M; calculated on the discrete wavelet transform is the measure of the Fischer asymmetry coefficient. This coefficient can also be called "Skewness". This coefficient makes it possible to estimate the asymmetry of a given curve. [Fig.5] illustrates two measures of asymmetry on two curves given as examples. Curve 1 is a curve tending to the left and curve 2 is a curve tending to the right. The Fischer asymmetry coefficient has a positive value when the curve tends to the left. This is the case for curve 1. The Fischer asymmetry coefficient has a negative value when the curve tends to the right. This is the case for [Fig.2]. Concretely, the Fischer asymmetry coefficient is calculated on the sum of the coefficients on several levels of the discrete wavelet decomposition.Typically, the sum of the coefficients is carried out between 64 hertz and 1024 hertz, that is to say on the four levels of this frequency band.

[0106] A fourth marker M; calculated on the discrete wavelet transform is the measurement of the number of chains of local minima of said decomposition. Concretely, a chain of local minima is the presence on several levels of discrete wavelet decomposition of the same minimum. By measuring the number of minima which are found in each level of decomposition, we measure the number of chains of local minima. Typically, the measurement is carried out between 64 hertz and 1024 hertz, i.e. on the four levels of this frequency band. We are therefore looking for the minima repeating in the bands from 64 hertz to 128 hertz, then from 128 hertz to 256hz, then from 256 hertz to 512 hertz and finally 512 hertz to 1024 hertz. Alternatively, the measurement of the number of chains of local minima can be carried out on the continuous wavelet decomposition.

[0107] Another signal marker M; that can be chosen is the measurement on the continuous wavelet decomposition of the number of chains of local maxima. Concretely, a chain of local maxima is the presence on several levels of discrete wavelet decomposition of the same maximum. By measuring the number of maxima that are found in each level of decomposition, we measure the number of chains of local maxima. Typically, the measurement is carried out between 64 hertz and 1024 hertz, that is to say on the four levels of this frequency band. We therefore look for maxima repeating in the bands from 64 hertz to 128 hertz, then from 128 hertz to 256hz, then from 256 hertz to 512 hertz and finally 512 hertz to 1024 hertz. Alternatively, the measurement of the number of chains of local maxima can be carried out on the discrete wavelet decomposition.

[0108] The following two signal markers Mi are measured on the wavelet of the signal included in the frequency band from 256 hertz to 512 hertz of the signal. The first concerns the measurement of the Kurtosis Sku index on this wavelet. By "Kurtosis Sku" is meant the same indicator as that described previously in the application. The second signal marker M; measured on this wavelet is the measurement of the number of reduced amplitude zones RED of the wavelet. To calculate the number of reduced amplitude zones, the upper and lower envelopes of the signal are created. Thus, the number of reduced amplitude zones is calculated on the envelopes of the signal. This is illustrated by [Fig.7] which shows a signal and the detected reduced amplitude zones RED.

[0109] The following three signal markers M; are measured on the wavelet of the signal included in the frequency band from 128 hertz to 256 hertz of the signal. The first concerns the measurement of the Kurtosis Sku index on this wavelet. By Kurtosis Sku is meant the same indicator as that described previously in the application. The second signal marker M; measured on this wavelet is the measurement of the number of reduced amplitude zones of the wavelet. The number of reduced amplitude zones is calculated in the same way as for the marker concerning the wavelet of the signal included in the frequency band from 256 hertz to 512 hertz of the signal. The third signal marker concerns the measurement of the RMS of the wavelet in the frequency band from 128 Hertz to 256 Hertz. By RMS, or "Root Mean Square", is meant the measurement of the effective amplitude of the signal.

[0110] Finally, a signal marker M; which can be selected is measured on the wavelet of the signal included in the frequency band from 64 to 128 hertz. This signal marker Mi concerns the measurement of the effective amplitude RMS (“Root Mean Square”) of the signal.

[0111] Other M; signal markers may be used beyond the fourteen M; signal markers described. For example, M; signal markers may be used which are combinations of M; signal markers already described. Statistical modality

[0112] Each first electrophysiological descriptor D; is associated with a statistical modality MS;. By statistical modality MS; we mean a modality for processing the different measured quantities in order to calculate a value for each descriptor D;.

[0113] It is recalled that for each descriptor D; we select an area of ​​the patient's body Z, a signal type T; (therefore the way in which we capture and use the individual signals of each electrode EL), and a signal marker M;. It should be specified that when these three choices have been made, we measure the signal marker Mi for the type of signal selected for each electrode of the area of ​​the patient's body Z; available in said selected area Z. Thus, for each descriptor D;, we obtain a plurality of measurements of the value of the signal marker M;. The use of the statistical modality MS; makes it possible to transform this plurality of values ​​into a final value for the selected descriptor D;.

[0114] For each descriptor D;, one can select a statistical modality MS; from a set of available statistical modalities MS;.

[0115] A first statistical modality is the minimum at the fifth percentile of the measured values. It is recalled that the values ​​are taken as the set of values ​​measured on each electrode of the predefined area of ​​the patient's body Z- For this statistical modality, the lowest five percent of values ​​are removed from the set of measured values ​​and the minimum value is selected from the remaining values. This statistical modality has the advantage, by removing the lowest five percent of values, of removing aberrant values ​​which could distort the representativeness of the measurement.

[0116] A second statistical modality that can be selected is the maximum at the 95th percentile. For this statistical modality, the five percent of the highest values ​​are taken from all the selected values. The highest value is then selected from the remaining values. This statistical modality makes it possible to not take into account, for a measurement of a maximum value, the aberrant values ​​that could appear in the highest measured values. In this way, we have an upper bound representative of all the measured values.

[0117] A third statistical modality MS; is the average of the measured values. The average is a classic indicator and representative of a distribution.

[0118] A fourth statistical modality MS; is the standard deviation calculated on the set of measured values. The standard deviation is a value representative of the dispersion of the values. In our case, the dispersion can be a significant value, a large variance in the measurements carried out being able to be the sign of a disorder in the patient's cardiac activity.

[0119] A fifth statistical modality MS; which can be selected is the median. The median value of a set of values ​​is the value allowing the set of values ​​to be separated into two sets of the same size. This value provides information which may vary from that given by the average value, because the median allows not to give too much importance to aberrant values ​​close to the maximum and minimum of the measured values.

[0120] A sixth statistical modality MS; is the interquartile value. To calculate this value, we calculate the value of the 25th percentile and the value of the 75th percentile. The interquartile value represents the difference between the value of the 75th percentile and the value of the 25th percentile. The interquartile is an interesting statistical value to look at to characterize the distribution of the measured values. Calculating a score

[0121] Once the subset {Dk} of electrophysiological descriptors is selected, a calculation of the value of each descriptor of the subset {Dk} is performed.

[0122] In order to be able to use the value of each descriptor D;, we have for each of them a threshold value Vseuii- Each threshold value Vseuii for each descriptor Di of the subset {Dk} can advantageously be defined by the input parameter Inp.

[0123] Each value of each descriptor D; of the subset {Dk} is compared to the threshold value Vseuii of said descriptor D;. By threshold value Vseuii, we mean a lower threshold value or an upper threshold value. Thus, depending on the threshold value Vseuii, the threshold can be exceeded either if the value of the descriptor is lower than the threshold value, or if the value of the descriptor is higher than the threshold value Vseuii.

[0124] Alternatively or additionally, a descriptor D; comprises several different threshold values ​​VSeuii. For example, a descriptor D; may comprise a higher threshold value VSeuii and a lower threshold value Vseuii. Thus, the threshold is exceeded if the value of the descriptor is between the lower threshold value Vseuii and the upper threshold value Vseuii. In the same way, for a descriptor, the threshold may be exceeded when the value of said descriptor is lower than the lower threshold value Vseuii or when the value is greater than the upper threshold value Vseuii. Similarly, a descriptor D; can have three or more threshold values ​​VSeuii which define value ranges corresponding to the exceedance for the value of the descriptor D;.

[0125] The comparison between the value of each descriptor D; of the subset {Dk} and the respective threshold value Vseuii of each descriptor D; is carried out. The result of these comparisons makes it possible to establish a score. The established score defines an electrophysiological parameter. More precisely, the score takes into account the number of times that the value of one of the descriptors D; of the subset {Dk} exceeds the threshold value VSeuii. The number of times the threshold values ​​Vseuii are exceeded makes it possible to establish the score. The score calculated according to the invention has the advantage of taking into account the value of a set of different physiological data, which makes it very representative compared to conventional measurements taking into account only a limited number of parameters.

[0126] According to one aspect, the score is equal to the number of descriptor values ​​D; exceeding the threshold value Vseuii associated with said descriptor D;. A score established in this way is very representative of cardiac activity. If the subset {Dk} is well chosen, the score is very representative of the patient's cardiac activity.

[0127] According to one aspect, the score is compared to a score threshold value. Said score threshold value is specific to the predefined state ETh

[0128] Device for generating the electrophysiological parameter

[0129] The invention also relates to a device for generating the electrophysiological parameter. The characteristics described above concerning the method according to the invention also apply to the device according to the invention. The characteristics described below for the device also apply to the method according to the invention.

[0130] The device for generating an electrophysiological parameter comprises a plurality of electrodes which are arranged on the surface of the patient's body. Each surface electrode defines a channel V;.

[0131] According to one embodiment, the device comprises adhesive strips comprising the surface electrodes. According to this aspect, the adhesive strips are intended to be stuck to the surface of the patient's body. Advantageously, each adhesive strip comprises several surface electrodes. This arrangement makes it easier to install the electrodes on the patient, the installation of a strip comprising several electrodes being simpler than that of the electrodes one by one.

[0132] According to one aspect, the device comprises a vest or jacket comprising the plurality of EL measuring electrodes. The vest is intended to be put on by the patient. This arrangement allows for rapid installation of the device on the patient. In one example, the device has at least 14 electrodes.

[0133] The device comprises a means for measuring the signal of each channel V;. More precisely, the measuring means is configured to measure the electrical potential of each of the channels V;. The measuring means may for example be an acquisition card. The acquisition card may comprise an input for collecting an electrical signal, and an analog-digital converter for digitizing the acquired signal. The digitized signal is then transmitted to a computer. The digitized signal may for example be transmitted to a computer which performs the processing steps on the signal.

[0134] The device comprises a calculation means. The calculation means records the measurements of the V channels; provided by the measurement means. The calculator then processes this data.

[0135] The calculator selects a subset {Dk} of descriptors D; from the set {DN} of descriptors D;. This selection is made according to the input parameter Inp.

[0136] The calculator then calculates the value of each descriptor D; of the subset {Dk}. This calculation is carried out from the measurements of the channels V;. The calculation is carried out in accordance with the predefined zone Zi5 at the signal type T;, at the signal marker MS; and at the statistical modality ST; selected for the descriptor D; in question.

[0137] The calculator then compares the value of the selected descriptors D; with the threshold value Vseuii relating to each descriptor D;.

[0138] The calculator then calculates a score from the comparisons of the estimated values ​​of the descriptors D; with their threshold value Vseuii. The score is calculated as described previously.

[0139] According to one embodiment, the device comprises a device for detecting the patient's breathing phases. Such a device detects when the patient is in the expiration phase or "flat" breathing phase. It also detects when the patient is in the inspiration phase. Breathing tends to disturb the measurements made at the EL electrodes. This is particularly the case during the inspiration phases during which the heartbeats and their measurement can be affected. Preferably, the measurement of the potential of each channel V; is carried out during the expiration phase. This arrangement makes it possible to avoid the disturbances caused by a measurement during inspiration phases. The device for detecting the breathing phases can be connected to the computer. Alternatively, it is connected to the means for measuring the signal of each channel V;.

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[0172] According to one embodiment, the breathing phase detection device is a plethysmography belt. The plethysmography belt is a practical means for performing this type of detection. Nomenclature D;: Electrophysiological descriptor {Dk}: Subset of electrophysiological descriptors {DN}: Set of electrophysiological descriptors Inp: Input parameter Vi: Way Z;: Predefined area of ​​the patient's body Zi: Upper right area of ​​the patient's torso Z2: Upper left area of ​​the patient's torso Z3: Lower right area of ​​the patient's torso Z4: Lower left area of ​​the patient's torso Z5: Area covering the patient's entire torso T; : Signal type Mj: Signal Marker MS; : Statistical method of calculation EL: Electrode EL p Central electrode ELkp: Laplacian electrode Bv: Vertical bipole Bh: Horizontal bipole DISPO: arrangement of a plurality of electrodes ENR: Recording of a plurality of electrical activities EST: Estimation of descriptors COMP: Comparison of the value of a descriptor with a threshold value VSeuii: Threshold value CALC: Calculating a score Sku: Kurtosis N: Curve representing a normal distribution P: Flat curve E: Slender curve 1: Curve tending towards the left 2: Curve tending towards the right RED: Reduced amplitude zone

Claims

1. Claims Device for generating an electrophysiological parameter characterized in that it comprises a plurality of electrodes (EL), a receiver of the signals measured by the electrodes, a memory for recording the measured data and a computer making it possible to carry out operations and processing on the measured data, said device comprising means configured to implement the following steps: • selection of a subset of first electrophysiological descriptors ({Dk}) from a set of predefined first electrophysiological descriptors ({Dn}) according to an input parameter (Inp) defining a measurement context, each first electrophysiological descriptor (D;) of the subset being associated with at least one channel (Vi), one signal type (Ti), one signal marker (Mi) and one statistical calculation modality (MSi); at least one first descriptor (Di) of the subset ({Dk}) being associated with a statistical modality (MSi) different from that of another first descriptor (D j) of the subset ({Dk}) and with a signal marker (Mi) different from that of the other first descriptor (Dj); • arrangement (DISPO) of a plurality of surface electrodes (EL) on the body of a patient; • recording (ENR) of a plurality of cardiac electrical activities defining said pathways, each pathway (Vi) being obtained by recordings from at least two electrodes (EL); • estimation (EST) of the set of electrophysiological descriptors ({Dk}) of the subset, each electrophysiological descriptor (Di) being calculated from the statistical modality (STi) which is applied to the signal marker (Mi) of the acquired signal according to the type of signal on a selected channel associated with said electrophysiological descriptor (Di); • comparison (COMP) of the value of all electrophysiological descriptors (Di) with at least one threshold value (Vseuii) specific to all electrophysiological descriptors, said at least one threshold value being characteristic of said electrophysiological descriptor; • calculation (CALC) of a score defining an electrophysiological parameter as a function of exceeding the at least one threshold value.

2. Device for generating an electrophysiological parameter according to the preceding claim, characterized in that the comparison (COMP) of the value of the set of descriptors with the threshold value (VSeuii) is defined by a statistical distribution of said descriptors (D;) of the set of healthy patients of dimension equal to the number of descriptors of the subset ({Dk}).

3. Device for generating an electrophysiological parameter according to claim 1, characterized in that: • the comparison (COMP) is carried out by comparing the value of each electrophysiological descriptor with at least one threshold value (Vseuii) specific to said electrophysiological descriptor, said at least one threshold value being defined by a statistical distribution of said descriptor of a set of healthy patients; • the calculation (CALC) of the score defining the electrophysiological parameter is carried out by incrementing said score each time an electrophysiological descriptor (Di) crosses the at least one threshold value (Vseuii) specific to it.

4. Device for generating an electrophysiological parameter according to any one of the preceding claims, characterized in that the subset {Dk} comprises at least one geographical descriptor which is associated with several channels (V;) and several geographical groups, each geographical group being formed by a central channel (V;) and the at least four channels (V;) close to the central channel (Vi), the value of the electrophysiological descriptor (Di) being determined: by comparing the value, for each geographical group, of the measurement of each channel according to the type of signal and the signal marker selected with at least one geographic threshold value specific to said electrophysiological descriptor and to said channel (V;); and • by counting the number of geographic groups for which the value of at least three channels exceeds the geographic threshold value specific to it.

5. Device for generating an electrophysiological parameter according to any one of the preceding claims, characterized in that the input parameter (Inp) defining the measurement context defines a subset of characteristic descriptors ({Dk}): • of a given cardiac pathology and / or; • of a patient profile comprising at least one age and / or one gender / sex.

6. Device for generating an electrophysiological parameter according to any one of the preceding claims, characterized in that each first electrophysiological descriptor (D;) of the subset is associated with at least one channel (Vi) of a predefined zone (Zi) of the patient's body from among a set of predefined zones and in that for each electrophysiological descriptor (D;), the predefined zone (Zi) on the patient's body is chosen from: • An upper right zone (ZJ) of the torso; • An upper left zone (Z2) of the torso; • A lower right zone (Z3) of the torso; • A lower left zone (Z4) of the torso; and • The entire (Z5) of the patient's torso.

7. Device for generating an electrophysiological parameter according to any one of the preceding claims, characterized in that for each electrophysiological descriptor (D;), the type of signal (Ti) analyzed is chosen from: • A unipolar signal taken between an electrode of the chosen area of the body and a reference electrode; • A vertical bipolar signal taken between two electrodes of the given area, one of the two electrodes being offset along a vertical line relative to the other electrode; • A horizontal bipolar signal taken between two electrodes of the given area, one of the two electrodes being offset along a horizontal line relative to the other electrode; and • A Laplacian signal estimated by subtracting from the potential of a central electrode the average of the voltage of the eight electrodes directly near said central electrode.

8. Device for generating an electrophysiological parameter according to any one of the preceding claims, characterized in that for at least one electrophysiological descriptor (D;), the signal marker (Mi) is the measurement of the voltage of an averaged signal.

9. Device for generating an electrophysiological parameter according to any one of the preceding claims, characterized in that for at least one electrophysiological descriptor (D;), the marker (Mi) is the measurement, on the signal averaged and filtered between 40 and 250 Hertz, of the duration of depolarization of the ventricles (QRS) or of the fragmentation of the signal during the depolarization of the ventricles.

10. Device for generating an electrophysiological parameter according to any one of the preceding claims, characterized in that for at least one electrophysiological descriptor (D;), the signal marker (Mi) is the measurement on the discrete wavelet decomposition of the signal: • Of the energy of the sum of the wavelets; • Of the Kurtosis; • Of the Fisher asymmetry coefficient; or • Of the number of local minima.

11. Device for generating an electrophysiological parameter according to any one of the preceding claims, characterized in that for at least one electrophysiological descriptor (D;), the signal marker is the measurement on the continuous wavelet decomposition of the signal of the number of chains of local maxima.

12. Device for generating an electrophysiological parameter according to any one of the preceding claims, characterized in that for at least one descriptor (D;), the signal marker (Mi) is: • Either the measurement on the wavelet taken between 256 and 512 hertz of the signal: i. Of Kurtozis; or ii. The number of reduced amplitude zones; • Let the measurement on the wavelet taken between 128 and 256 hertz of the signal: iii. From Kurtozis; iv. The number of reduced amplitude zones; or v. The RMS (Root Mean Square); • Either the measurement on the wavelet taken between 64 and 128 hertz of the RMS (Root Mean Square).

13. Device for generating an electrophysiological parameter according to any one of the preceding claims, characterized in that for at least one electrophysiological descriptor (D;), the statistical modality (STi) is chosen from: • The minimum at the fifth percentile of the measured signal values on each electrode of the predefined zone (Zi); • The maximum at the ninety-fifth percentile of the measured signal values on each electrode of the predefined area (Zi); • The average of the measured signal values on each electrode of the predefined zone (Zi); • The standard deviation of the measured signal values on each electrode of the predefined area (Zi); • The median of the measured signal values on each electrode of the predefined area; and • The interquartile of the measured signal values on each electrode of the predefined area (Zi).

14. Device for generating an electrophysiological parameter characterized in that it comprises: • a plurality of surface electrodes configured to be deposited on the body of a patient and to measure an electrical potential of the surface of the patient's body, each surface electrode defining a path (Vi); • a means of measuring the signal of each channel (Vi); • a calculation means configured for: selecting a subset of descriptors ({Dk}) from a set of predefined descriptors ({DN}) according to an input parameter (Inp) defining a measurement context, each electrophysiological descriptor (D;) of the subset being associated with at least one channel (Vi), one signal type (Ti), one signal marker (Mi) and one statistical calculation modality (MSi); at least one descriptor (Di) of the subset ({Dk}) being associated with a statistical modality (MSi) different from that of a second descriptor (Dj) of the subset ({Dk}) and a signal marker (Mi) different from that of the second descriptor (Dj); recording a plurality of cardiac electrical activities defining said pathways, each pathway (Vi) being obtained by recordings from at least two electrodes; estimating the set of electrophysiological descriptors ({Dk}) of the subset, each electrophysiological descriptor (Di) being calculated from the statistical modality (STi) which is applied to the signal marker (Mi) of the acquired signal according to the type of signal on a selected channel associated with said electrophysiological descriptor (Di); comparing the value of the set of electrophysiological descriptors (Di) with at least one threshold value (Vseuii) specific to the set of electrophysiological descriptors, said at least one threshold value being defined by a statistical distribution of said descriptors (D;) of a set of patients; calculate a score defining an electrophysiological parameter based on exceeding at least one threshold value defined by the statistical distribution.

15. Device for generating an electrophysiological parameter according to claim 14, characterized in that it comprises a device for detecting the breathing phases of a patient, preferably a plethysmography belt.

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