METHOD AND DEVICE FOR DETECTING A REPRESENTATIVE CARDIAC ELECTRICAL ACTIVITY

Abstract

A method for detecting a representative cardiac activity includes the arrangement of a plurality of electrodes, the measurement of the value of electrophysiological descriptors, and the calculation (CALC) of a score.

Claims

1. A method for generating an electrophysiological parameter, the method comprising: selecting a subset of first electrophysiological descriptors from a set of first predefined electrophysiological descriptors, according to an input parameters defining a measurement context, each first electrophysiological descriptor of the subset being associated with at least one channel, a signal type, a signal marker and a statistical modality for calculation; at least one first descriptor of the subset being associated with a statistical modality different to that of another first descriptor of the subset and a signal marker different to that of the other first descriptor; arranging a plurality of surface electrodes on a patient's body; recording of a plurality of cardiac electrical activities defining said channels, each channel being obtained by the recordings of at least two electrodes; estimating the set of electrophysiological descriptors of the subset, each electrophysiological descriptor being calculated from the statistical modality that is applied to the signal marker of the acquired signal according to the signal type on a selected channel associated with said electrophysiological descriptor; comparing the value of the set of electrophysiological descriptors with at least one threshold value specific to the set of electrophysiological descriptors, said at least one threshold value being defined by a statistical distribution of said descriptors of a set of patients of a reference group, and calculating a score defining an electrophysiological parameter as a function of the exceeding of the at least one threshold value.

2. The method for generating an electrophysiological parameter according to claim 1, wherein the comparing 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 patients of the reference group of dimension equal to the number of descriptors of the subset.

3. The method for generating an electrophysiological parameter according to claim 1, wherein: the comparing 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 patients of the reference group, and calculating the score defining the electrophysiological parameter is performed by incrementing said score each time an electrophysiological descriptor exceeds the at least one threshold value specific to it.

4. The method for generating an electrophysiological parameter according to claim 1, wherein the subset comprises at least one geographical descriptor which is associated with several channels and several geographical groups, each geographical group being formed by a central channel and the at least four channels adjacent 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 signal type and the signal marker selected with at least one geographical threshold value specific to said electrophysiological descriptor and said channel; and by counting the number of geographical groups for which the value of at least three channels exceeds the geographical threshold value that is specific to it.

5. The method according to claim 1, wherein the input parameter defining the measurement context defines a subset of characteristic descriptors: of a given cardiac pathology and/or; of a patient profile comprising at least one age and/or gender/sex.

6. The method according to claim 1, wherein 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 wherein 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 an entire torso of the patient.

7. The method according to claim 1, wherein for each electrophysiological descriptor, the signal type analyzed is chosen from: a unipolar signal taken between an electrode of 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 leading from the feet to the head of the patient with respect 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 going from one arm to the other of the patient with respect to the other electrode; and a Laplacian signal estimated by subtracting from the potential of a central electrode the average tension of the eight electrodes directly adjacent to said central electrode.

8. The method according to claim 1, wherein for at least one electrophysiological descriptor, the signal marker is the measurement of the voltage of an averaged signal.

9. The method according to claim 1, wherein for at least one electrophysiological descriptor, the marker is the measurement, on the averaged and filtered signal between 40 and 250 Hertz, of the duration of depolarization of the ventricles or fragmentation of the signal during depolarization of the ventricles.

10. The method according to claim 1, wherein for at least one electrophysiological descriptor, the signal marker 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. The method according to claim 1, wherein for at least one electrophysiological descriptor, the signal marker is the measurement on the continuous wavelet decomposition of the signal of the number of chains of local maxima.

12. The method according to claim 1, wherein for at least one descriptor, the signal marker is: either the measurement on the wavelet taken between 256 and 512 Hertz of the signal: i. of the Kurtosis; or ii. of the number of areas with reduced amplitudes; or the measurement on the wavelet taken between 128 and 256 Hertz of the signal: iii. of the Kurtosis; iv. of the number of areas of reduced amplitude; or v. of the RMS (Root Mean Square); or the measurement on the wavelet taken between 64 and 128 Hertz of the RMS (Root Mean Square).

13. The method according to claim 1, wherein for at least one electrophysiological descriptor, the statistical modality is chosen from: the fifth percentile minimum of the measured values of the signal on each electrode of the predefined area; the ninety fifth percentile maximum of the measured values of the signal on each electrode of the predefined area; the average of the measured values of the signal on each electrode of the predefined area; the standard deviation of the measured values of the signal on each electrode of the predefined area; the median of the measured values of the signal on each electrode of the predefined area; and the interquartile of the measured values of the signal on each electrode of the predefined area.

14. A device for generating an electrophysiological parameter, the device comprising: 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 channel; a means of measuring the signal of each channel; a calculation means configured to: 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, a signal type, a signal marker and a statistical modality for calculation; at least one descriptor of the subset being associated with a statistical modality different to that of a second descriptor of the subset and a signal marker different to that of the second descriptor; record a plurality of cardiac electrical activities defining said channels, each channel being obtained by the recordings of at least two electrodes; estimate the set of electrophysiological descriptors of the subset, each electrophysiological descriptor being calculated from the statistical modality that is applied to the signal marker of the acquired signal according to the signal type on a selected channel associated with said electrophysiological descriptor; compare the value of the set of electrophysiological descriptors with at least one threshold value specific to the set of 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 as a function of the exceeding of the at least one threshold value defined by the statistical distribution.

15. The device for generating an electrophysiological parameter according to claim 14, further comprising a device for detecting a patient's respiration phases.

16. A device for generating an electrophysiological parameter, comprising a plurality of electrodes, a receiver of signals measured by the electrodes, a memory for recording the measured data and a calculator making it possible to perform operations and processings on the measured data, said device comprising means configured to implement the method of claim 1.

17. The device according to claim 15, wherein the device for detecting a patient's respiration phases is a plethysmography belt.

Description

BRIEF DESCRIPTION OF THE FIGURES

[0104] Other characteristics and advantages of the invention will become clearer upon reading the following detailed description, in reference to the appended figures, that illustrate:

[0105] FIG. 1: a schematic flowchart of the method according to the invention;

[0106] FIG. 2: a flowchart presenting the selection of a descriptor;

[0107] FIG. 3: a front view of the torso of a patient on which is arranged a plurality of measuring electrodes to implement the method according to the invention;

[0108] FIG. 4: a view of a plurality of measuring electrodes in a predefined area of the patient's body;

[0109] FIG. 5: two curves illustrating the method for calculating an asymmetry index;

[0110] FIG. 6: a graph illustrating the method for calculating a Kurtosis index;

[0111] FIG. 7: a view of a curve illustrating the method for calculating the number of areas of reduced amplitude.

DESCRIPTION OF THE INVENTION

[0112] According to a first aspect, the invention relates to a method for generating an electrophysiological parameter. The term electrophysiological parameter is taken to mean a datum derived from an electrical measurement which is characteristic of a patient's physiological activity.

[0113] 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 previously. 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.

[0114] The method according to the invention will be described in support of FIG. 1, which is a schematic flowchart of the method according to the invention.

[0115] According to a first aspect, the invention relates to a method for generating an electrophysiological parameter. A first step in this method is a step of selecting a subset of first descriptors {D.sub.k} from a set of predefined descriptors {D.sub.N}. Electrophysiological descriptor D.sub.i is taken to mean a context of measurement of an electrical datum associated with an activity detected on the surface of a patient. Each first electrophysiological descriptor D.sub.i is associated with the recording of at least one channel V.sub.i. The recording of a channel V.sub.i 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.sub.i is associated with at least one channel V.sub.i on a predefined area Z.sub.i on the patient's body. Predefined area Z.sub.i is taken to mean the area of the patient's body on which are deposited the measuring electrode(s) EL, the electrical activities of which are recorded to obtain the channel(s) V.sub.i. Thus, the surface of a patient's body may be segmented into different functional and/or geometrical and/or physiological areas. These areas may therefore correspond to geographical areas on the patient's body, to functional areas in relation to the patient's cardiac activity, or to areas corresponding to the patient's physiology and/or physiology.

[0116] Each first descriptor D.sub.i is associated with a signal type T.sub.i measured on the selected channels V.sub.i. For example, signal type T.sub.i is taken to mean the measurement of a tension between two electrodes. The different types of signals T.sub.i that can be selected will be described later.

[0117] Each first descriptor D.sub.i is associated with a signal marker M.sub.i. Signal marker M.sub.i is taken to mean the characteristic of the signal type T.sub.i that will be measured. A set of signal markers M.sub.i used in the context of the invention comprises the measurement of frequency and energy characteristics of the signal. The different signal markers M.sub.i that may be selected are described below.

[0118] Each first descriptor D.sub.i is associated with a statistical modality MS.sub.i. Statistical modality MS.sub.i is taken to mean a statistical measurement modality applied to the measurements made on the signals. For example, a statistical modality MS.sub.i may be the calculation of the average of the signal marker M.sub.i measured on several electrodes. The different statistical modalities MS.sub.i that may be selected will be described later.

[0119] Each first electrophysiological descriptor D.sub.i is therefore defined by both a selection of the predefined area Z.sub.i of the patient's body, the signal type T.sub.i, the signal marker M.sub.i and the statistical modality MS.sub.i.

[0120] In the method according to the invention, an input parameter Inp is acquired. The input parameter Inp defines a measurement context. Measurement context is taken to mean the number of descriptors D.sub.i that will be selected as well as, for each descriptor D.sub.i, the combination of the predefined area Z.sub.i, the signal type T.sub.i, the signal marker M.sub.i and the statistical modality MS.sub.i that are selected.

[0121] According to a first example, the input parameter is generated from a patient profile, for example defined by his or her age, gender, medical history, and possibly other parameters.

[0122] According to a second example, the input parameter is generated from a selection of a predefined pathology. The pathology may be associated in a database or memory with a set of descriptors. This case may be interesting to associate the measured electrical activity with said selected pathology.

[0123] According to a third example, the input parameter is defined by selecting a set of descriptors. This selection may be made by an operator from a user interface.

[0124] According to a fourth example, the input parameter is defined by selecting a set of electrodes. This selection may be made by an operator from a user interface.

[0125] According to a fifth example, the input parameter is defined by a set point emitted by a computer-implemented device or method. The set point may correspond to a set of descriptors selected according to an algorithm configured according to different parameters. Such an algorithm is for example described in the application FR2103047 filed on 25 Mar. 2021, the title of which is METHOD FOR SELECTING ELECTROPHYSIOLOGICAL DESCRIPTORS.

[0126] According to an example, the input parameter is defined by a combination of these different alternatives.

[0127] According to one embodiment, at least two first descriptors D.sub.i, D.sub.j of the subset {D.sub.k} are associated with a different statistical modality MS.sub.i. In other words, two of the first descriptors D.sub.i, D.sub.j are not associated with the same statistical modality MS.sub.i.

[0128] At least two first descriptors D.sub.i, D.sub.j of the subset {D.sub.k} are associated with a different signal marker M.sub.i. In other words, two of the first descriptors D.sub.i, D.sub.j are not associated with the same signal marker M.sub.i.

[0129] A plurality of surface electrodes EL is arranged on the patient's body. Thus, at least two electrodes are placed on the patient's skin. These electrodes EL are configured to collect an electrical potential on the surface of the patient's skin.

[0130] A recording ENR of a plurality of cardiac electrical activities defining said channels V.sub.i is performed. During this step, it is preferable to record all the available channels. 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 may be implemented notably by means of a memory making it possible to record the data acquired and/or the data processed by one of the steps of the method of the invention.

[0131] An estimation EST of the set of electrophysiological descriptors D.sub.i of the subset of descriptors {D.sub.k} is made. In this step of the method, each value of each descriptor D.sub.i of the subset {D.sub.k} is calculated. The calculation is performed for each descriptor D.sub.i in the predefined area Z.sub.i, with the signal type T.sub.i, on the signal marker M.sub.i, and according to the statistical modality MS.sub.i that are associated with it.

[0132] The calculated values of each descriptor D.sub.i are then compared to a threshold value V.sub.threshold. Each threshold value V.sub.threshold is characteristic of the electrophysiological descriptor D.sub.i in question. Thus, there is a threshold value V.sub.threshold specific to each descriptor D.sub.i.

[0133] A score is then calculated from the number of descriptors D.sub.i of which the value has exceeded the threshold value V.sub.threshold specific to it. Exceeding the threshold value V.sub.threshold is taken to mean exceeding by a higher value or by a lower value. The direction in which the threshold is exceeded is specific to each descriptor D.sub.i. Each time the threshold value V.sub.threshold is exceeded by a descriptor D.sub.i, the score is incremented.

[0134] Finally, a score is obtained that defines an electrophysiological parameter.

[0135] The method according to the invention thus makes it possible to obtain an electrophysiological parameter that takes into account the calculation of the value of several descriptors D.sub.i which have measurement and calculation modalities that are very different. Thus, the electrophysiological parameter does not take into account only one parameter to calculate the score defining the electrophysiological parameter. Since each descriptor is a bearer of information on the cardiac activity of the patient that is specific thereto, the method according to the invention makes it possible to generate the electrophysiological parameter the most representative of the patient's cardiac activity.

[0136] Alternatively, the comparison step may be performed taking into account the statistical distribution of the set of descriptors in a space having a dimension equal to the number of descriptors D.sub.i of the subset {D.sub.k}. According to this alternative, the set of descriptors is compared to a threshold value derived from the multi-dimensional distribution.

[0137] Electrophysiological Descriptors

[0138] As described previously, each electrophysiological descriptor D.sub.i is associated with at least one channel V.sub.i of a predefined area Z.sub.i of the patient's body, a signal type T.sub.i, a signal marker M.sub.i, and a statistical modality for calculation MS.sub.i. These four elements will be described in detail below. The selection of a descriptor D.sub.i from the set of descriptors {D.sub.N} is illustrated in FIG. 2, which is a flowchart showing the method for selecting a descriptor D.sub.i. To select a descriptor D.sub.i, a selection is made of a predefined area Z.sub.i from a set of predefined areas Z.sub.i. A selection is made of a signal type T.sub.i from a set of signal types T.sub.i. A selection is made of a signal marker M.sub.i from a set of signal markers M.sub.i. A selection is made of a statistical modality MS.sub.i from a set of statistical modalities MS.sub.i.

[0139] Patient's Body Area

[0140] As shown in FIG. 3, a plurality of electrodes EL are placed on the surface of the patient's torso. The number of electrodes in the example shown may vary, for example the number of electrodes may be much lower than that shown, or much higher. The plurality of electrodes EL covers a large part of the patient's torso. As can be seen, this plurality of electrodes is separated into four distinct areas on it. A first part of the electrodes EL is located on an upper right area Z.sub.i of the patient's torso. A second part of the electrodes EL is located on an upper left area Z.sub.2 of the patient's torso. A third part of the electrodes EL is located on a lower right area Z.sub.3 of the patient's torso. A fourth part of the electrodes EL is located on a lower left area Z.sub.4 of the patient's torso. Finally, the plurality of electrodes EL is comprised in an area comprising the totality Z.sub.5 of the patient's torso.

[0141] The demarcation between the areas situated to the left of the torso and those situated to the right of the torso is a vertical line passing through the center of the torso, or substantially through the center of the torso.

[0142] The demarcation between the areas situated on the lower torso and those situated on the upper torso is a horizontal line through the center of the torso.

[0143] Each area comprises a predefined number of electrodes EL. The number of electrodes arranged per area may be of the order of thirty or so. For example, there may be thirty electrodes EL per area.

[0144] Advantageously, each area comprises the same number of electrodes EL. Obviously, the area comprising the entire torso Z.sub.5 comprises a different number of electrodes than the others, because this area Z.sub.5 comprises the reunion of the electrodes of all the other areas Z.sub.1, Z.sub.2, Z.sub.3 and Z.sub.4.

[0145] Hereafter, when mention is made of the selection of electrodes in an area, the selection of one or more electrodes EL in said area will be meant.

[0146] Alternatively, a lower number of electrodes EL may be provided, for example nine electrodes EL per area Z.sub.1, Z.sub.2, Z.sub.3, and Z.sub.4. Thus, in this case, the area Z.sub.5 comprising the entire torso of the patient comprises thirty six electrodes EL.

[0147] Each channel V.sub.i is obtained by recording the electrical activity of at least two electrodes EL. 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 an electrode from an area of the patient's torso and a reference electrode.

[0148] Geographical Groups

[0149] According to one embodiment, the subset {D.sub.N} comprises at least one second geographical electrophysiological descriptor D.sub.i. The at least one geographical descriptor D.sub.i is associated with at least one channel V.sub.i and with several geographical groups. A geographical group is formed by an electrode EL and the four electrodes EL that are situated directly next to it. A geographical group is shown in FIG. 3. This geographical group comprises a central electrode EL.sub.1 and the electrode situated directly above it. It also comprises the electrode situated directly below the central electrode EL.sub.1, the electrode situated directly to the left of the central electrode EL.sub.1, and the electrode situated directly to the right of the central electrode EL.sub.1. These four electrodes are shown hatched in FIG. 3. For the second geographical descriptors, the measurement of the value according to the signal type and the signal marker is performed on all the available geographical groups of the plurality of electrodes arranged on the patient's body. Other provisions of electrodes may be envisaged. It is notably possible to select the central electrode and the four electrodes situated at the top left, top right, bottom left and bottom right of the central electrode EL.sub.1. These are the electrodes that appear without a pattern in FIG. 3. More electrodes may also be provided, for example nine electrodes in the geographical group. These are the nine electrodes EL in FIG. 3 for example.

[0150] For each geographical group, the value obtained for each electrode EL of said group is compared with at least one geographical threshold value. The at least one geographical threshold value is obtained from a statistical distribution of the value of the considered electrode EL of a set of healthy patients. When at least three electrode values of a geographical group exceed their geographical threshold value, then the geographical group is considered as significant. Finally, the value of the descriptor is the number of significant geographical groups counted. Alternatively, a geographical group may be considered as signifying from two electrodes exceeding their geographical threshold value, or instead with four electrodes.

[0151] It may 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 groups detected.

[0152] Advantageously, the subset {D.sub.k} of descriptors comprises at least one first descriptor D.sub.i and at least one second geographical descriptor. Additionally, the subset {D.sub.k} comprises several first descriptors D.sub.i. According to this alternative, the subset {D.sub.k} comprises a second geographical descriptor per signal marker used in the first descriptors D.sub.i of the subset {D.sub.k}.

[0153] Signal Type

[0154] Each electrophysiological descriptor D.sub.i is associated with a signal type T.sub.i. In this section, reference will be made to FIG. 4, which is a schematic representation of nine contiguous electrodes EL on the patient's body. In this figure, each circle represents an electrode EL. The dotted areas represent the different electrodes EL selected in the different signal types.

[0155] The signal type T.sub.i may preferably be chosen between four different signal types.

[0156] A first signal type T.sub.i is a unipolar signal. A unipolar signal is a signal taken between an electrode EL of the predefined area Z.sub.i and a reference electrode. In other words, the unipolar signal type is the tension measured between the electrode of the predefined area and the reference electrode. Reference electrode is taken to mean an electrode that is not situated in one of the areas of the patient's torso defined previously. For example, a reference electrode may be an electrode placed on a lower limb or an upper limb of a patient.

[0157] A second signal type T.sub.i is a vertical bipolar signal. A vertical bipolar signal is a signal taken between an electrode in the predefined area and the electrode situated directly below it on the patient's torso. In other words, the signal type acquired is the tension between the two electrodes EL. A vertical bipolar signal is taken between two electrodes of the predefined area Z.sub.i. These two electrodes form a vertical bipole B.sub.v.

[0158] A third signal type T.sub.i is a horizontal bipolar signal. A horizontal bipolar signal is a signal taken between an electrode in the predefined area and an electrode situated directly next to it along a horizontal line on the patient's torso. In other words, the signal type acquired is the tension between the two electrodes EL. The horizontal bipolar signal is taken between two electrodes of the predefined area Z.sub.i. These two electrodes form a horizontal bipole B.sub.h.

[0159] A vertical axis is taken to mean an axis defined by a line parallel to the axis of the body taken in its largest dimension. A horizontal axis is taken to mean an axis defined in a plane perpendicular to the vertical axis and tangential to the surface of the human body.

[0160] According to one embodiment, a frame of reference linked to the human body may be defined in such a way as to define the vertical and horizontal axes notably with respect to the morphology of the human body or other reference axes.

[0161] According to one embodiment, the invention may be defined with respect to other reference axes than the vertical axis and the horizontal axis insofar as the positions of the electrodes may be defined in the position indicator related to the human body.

[0162] A fourth signal type T.sub.i is a Laplacian signal. A Laplacian signal is estimated by subtracting from the potential of a central electrode EL.sub.1 the average of the potentials of the eight electrodes that are directly adjacent to said central electrode. In other words, the Laplacian signal is a tension composed between the central electrode EL.sub.1 and a set of electrodes EL peripheral to the central electrode EL.sub.1. These nine electrodes form a Laplacian electrode EL.sub.lap.

[0163] Signal markers Each electrophysiological descriptor D.sub.i is associated with a signal marker M.sub.i. A signal marker M.sub.i is a modality of measuring a physical quantity associated with the types of signals measured by the electrodes EL.

[0164] The signal marker M.sub.i associated with a descriptor D.sub.i is preferably chosen from fourteen signal markers M.sub.i. These signal markers M.sub.i are described below.

[0165] A first signal marker M.sub.i corresponds to the measurement of an averaged electrical signal. Averaged electrical signal is taken to mean the calculation, made on the measured tension, of the average between the maximum peak and the minimum peak of the QRS. This measurement of QRS duration is generally quite representative of a cardiac activity.

[0166] Two signal markers which are measured on the filtered signal between 40 and 150 Hertz will now be described. The signal is filtered using a bandpass filter. Advantageously, the bandpass 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.sub.i more accurate.

[0167] A signal marker M.sub.i on the filtered signal is the QRS duration on the filtered signal. To measure this value, a position indicator is placed at the start of the QRS and a second mark is placed at the end of the QRS. The time between the two marks is measured. This operation may be performed automatically thanks to a QRS start and end detection algorithm. Alternatively, this duration may be measured manually by an operator on an interface. An automatic measurement of the QRS duration and a manual check of said measurement by the operator on the interface may also be provided.

[0168] According to one embodiment, the QRS duration is detected by moving a sliding window measuring the energy of the filtered signal. When an energy threshold is exceeded, a position indicator is placed that marks the start of the window. The QRS end position indicator is placed in the same way.

[0169] Another signal marker M.sub.i is the measurement of the fragmentation of the filtered averaged signal between 40 Hertz and 250 Hertz. According to this marker M.sub.i, the number of QRS peaks on the filtered signal is measured. Peak is taken to mean a local maximum of the filtered signal curve. 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.sub.i, which as a reminder is the QRS duration marker on the filtered signal.

[0170] The following markers M.sub.i are calculated on the decomposition into wavelets of the signal. For these markers M.sub.i, either continuous wavelet decomposition or discrete wavelet decomposition may be used.

[0171] The four markers M.sub.i shown below are calculated on the discrete wavelet decomposition.

[0172] A first marker M.sub.i is the calculation of the energy on the discrete wavelet decomposition of the signal. Specifically, the energy is calculated on the sum of the coefficients over several levels. Typically, the sum of the coefficients is made 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 QRS duration. Alternatively or additionally, the energy is normalized with respect to the maximum signal amplitude.

[0173] A second marker M.sub.i calculated on the discrete wavelet transform is the measurement of the so-called Kurtosis index S.sub.ku. Kurtosis is taken to mean an index making it possible to estimate the spread of a given curve. FIG. 6 illustrates several measurements of curve spread on three exemplary 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 negative the Kurtosis. When the curve is narrow, the Kurtosis is positive. The Kurtosis of a curve representing a normal distribution N is equal to zero. Specifically, the Kurtosis S.sub.ku is calculated on the sum of the coefficients over several levels of the discrete wavelet decomposition. Typically, the sum of the coefficients is made between 64 Hertz and 1024 Hertz, i.e. on the four levels of this frequency band.

[0174] A third marker M.sub.i calculated on the discrete wavelet transform is the measurement of the Fischer asymmetry coefficient. This coefficient may also be called Skewness. This coefficient makes it possible to estimate the asymmetry of a given curve. FIG. 5 illustrates two asymmetry measurements 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 in FIG. 2. Specifically, the Fischer asymmetry coefficient is calculated on the sum of the coefficients over several levels of the discrete wavelet decomposition. Typically, the sum of the coefficients is made between 64 Hertz and 1024 Hertz, i.e. on the four levels of this frequency band.

[0175] A fourth marker M.sub.i calculated on the discrete wavelet transform is the measurement of the number of local minima chains of said decomposition. Specifically, a local minima chain is the presence on several discrete wavelet decomposition levels of a same minimum. By measuring the number of minima that are found in each decomposition level, the number of local minima chains is measured. Typically, the measurement is made between 64 Hertz and 1024 Hertz, i.e. on the four levels of this frequency band. The minima repeating in the 64 Hertz to 128 Hertz bands, then 128 Hertz to 256 Hertz, then 256 Hertz to 512 Hertz and finally 512 Hertz to 1024 Hertz are therefore sought. Alternatively, the measurement of the number of local minima chains may be carried out on the continuous wavelet decomposition.

[0176] Another signal marker M.sub.i that may be chosen is the measurement on the continuous wavelet decomposition of the number of local maxima chains. In concrete terms, a local maxima chain is the presence on several discrete wavelet decomposition levels of a same maximum. By measuring the number of maxima that are found in each decomposition level, the number of local maxima chains is measured. Typically, the measurement is made between 64 Hertz and 1024 Hertz, i.e. on the four levels of this frequency band. The maxima repeating in the 64 Hertz to 128 Hertz bands, then 128 Hertz to 256 Hertz, then 256 Hertz to 512 Hertz and finally 512 Hertz to 1024 Hertz are therefore sought. Alternatively, the number of local maxima chains may be measured on the discrete wavelet decomposition.

[0177] The following two signal makers M.sub.i are measured on the wavelet of the signal comprised in the frequency band ranging from 256 Hertz to 512 Hertz of the signal. The first concerns the measurement of the Kurtosis index S.sub.ku on this wavelet. Kurtosis S.sub.ku is taken to mean the same indicator as described previously in the application. The second signal marker M.sub.i measured on this wavelet is the measurement of the number of reduced amplitude areas RED of the wavelet. To calculate the number of reduced amplitude areas, the upper and lower signal envelopes are created. Thus, the number of areas of reduced amplitude is calculated on the signal envelopes. This is illustrated by FIG. 7 which shows a signal and the reduced amplitude areas RED detected.

[0178] The following three signal markers M.sub.i are measured on the wavelet of the signal comprised in the frequency band ranging from 128 Hertz to 256 Hertz of the signal. The first concerns the measurement of the Kurtosis index S.sub.ku on this wavelet. Kurtosis S.sub.ku is taken to mean the same indicator as that described previously in the application. The second signal marker M.sub.i measured on this wavelet is the measurement of the number of areas of reduced amplitude of the wavelet. The number of areas of reduced amplitude is calculated in the same way as for the marker concerning the wavelet of the signal in the frequency range 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 128 Hertz to 256 Hertz. RMS, or Root Mean Square, is taken to mean the measurement of the effective amplitude of the signal.

[0179] Finally, a signal marker M.sub.i that may be selected is measured on the wavelet of the signal comprised in the frequency band ranging from 64 to 128 Hertz. This signal marker M.sub.i relates to the measurement of the RMS (Root Mean Square) effective amplitude of the signal.

[0180] Other signal markers M.sub.i may be used beyond the fourteen signal markers M.sub.i described. For example, signal markers M.sub.i may be used which are combinations of signal markers M.sub.i already described.

[0181] Statistical modality Each first electrophysiological descriptor D.sub.i is associated with a statistical modality MS.sub.i. Statistical modality MS.sub.i is taken to mean a modality for processing the different quantities measured in order to calculate a value for each descriptor D.sub.i.

[0182] It may be recalled that for each descriptor D.sub.i an area of the body of the patient Z.sub.i, a signal type T.sub.i (therefore the way in which the individual signals of each electrode EL are captured and used), and a signal marker M.sub.i are selected. It should be specified that once these three choices have been made, the signal marker M.sub.i for the selected signal type is measured for each electrode of the body area of the patient Z.sub.i available in said selected area Z.sub.i. Thus, for each descriptor D.sub.i, a plurality of measurements of the value of the signal marker M.sub.i is obtained. Using the statistical modality MS.sub.i makes it possible to transform this plurality of values into a final value for the selected descriptor D.sub.i For each descriptor D.sub.i, a statistical modality MS.sub.i may be selected from a set of available statistical modalities MS.sub.i.

[0183] A first statistical modality is the fifth percentile minimum of the measured values. It will be recalled that, for the values, all the values measured on each electrode in the predefined area of the body of the patient Z.sub.i are taken. For this statistical modality, the five percent of the lowest values are removed from all the measured values and the minimum value is selected from the remaining values. This statistical modality has the advantage, by removing the five percent of the lowest values, of eliminating outliers that could distort the representativeness of the measurement.

[0184] A second statistical modality that can be selected is the 95th percentile maximum. For this statistical modality, the five percent of the highest values are taken from all the selected values. The highest value of the remaining values is then selected. This statistical modality makes it possible not to take into account, for a measurement of a maximum value, the outliers which could appear in the highest values measured. In this way, there is an upper limit representative of all the measured values.

[0185] A third statistical modality MS.sub.i is the average of the measured values. The average is a conventional indicator and representative of a distribution.

[0186] A fourth statistical modality MS.sub.i is the standard deviation calculated on the set of measured values. The standard deviation is a representative value of the dispersion of the values. In our case, the dispersion may be a significant value, a large variance in the measurements being able to be the sign of a disorder in the patient's cardiac activity.

[0187] A fifth statistical modality MS.sub.i that may be selected is the median. The median value of a set of values is the value making it possible to separate all of the values into two sets of the same size. This value provides a teaching which may vary from that given by the average value, because the median makes it possible not to give too much importance to outliers close to the maximum and minimum of the values measured.

[0188] A sixth statistical modality MS.sub.i is the value of the interquartile. To calculate this value, the value of the 25th percentile and the value of the 75th percentile are calculated. The value of the interquartile 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.

[0189] Calculation of a Score

[0190] Once the subset {D.sub.k} of the electrophysiological descriptors is selected, a calculation of the value of each descriptor of the subset {D.sub.k} is performed.

[0191] In order to be able to use the value of each descriptor D.sub.i, a threshold value V.sub.threshold is available for each of them. Each threshold value V.sub.threshold for each descriptor D.sub.i of the subset {D.sub.k} may advantageously be defined by the input parameter Inp.

[0192] Each value of each descriptor D.sub.i of the subset {D} is compared with the threshold value V.sub.threshold of said descriptor D.sub.i. Threshold value V.sub.threshold is taken to mean a lower or upper threshold value. Thus, depending on the threshold value V.sub.threshold, the threshold may be exceeded either if the value of the descriptor is lower than the threshold value, or if the value of the descriptor is greater than the threshold value V.sub.threshold.

[0193] Alternatively or additionally, a descriptor D.sub.i comprises several different threshold values V.sub.threshold. For example, a descriptor D.sub.i may comprise an upper threshold value V.sub.threshold and a lower threshold value V.sub.threshold. Thus, the threshold is exceeded if the value of the descriptor is between the lower threshold value V.sub.threshold and the upper threshold value V.sub.threshold. Similarly, for a descriptor, the threshold may be exceeded when the value of said descriptor is lower than the lower value V.sub.threshold or when the value is greater than the upper value V.sub.threshold. Similarly, a descriptor D.sub.i may comprise three or more threshold values V.sub.threshold which define value ranges corresponding to the exceeding for the value of the descriptor D.sub.i.

[0194] According to one embodiment, the threshold value V.sub.threshold is defined by the scientific literature relating to representative cardiac data.

[0195] For example, for a descriptor D.sub.i that relates to the upper right zone Z.sub.i of the patient's torso, according to a type of unipolar signal T.sub.i, on which the signal marker M.sub.i is the duration of depolarization of the ventricles (QRS), with a statistical modality STi that is the average of the values measured on each electrode of the predefined area Z.sub.i, the threshold value may be set at 120 milliseconds. Thus, in this case, it is considered that the threshold value of the descriptor is exceeded for a duration greater than 120 milliseconds. In this example, the score retained is 0 if the value is less than 120 ms and the score is 2 if the value is greater than 120 ms. According to one example, different thresholds may be defined for a descriptor D.sub.i associated with the marker and the statistical modality. These different thresholds allow different scores to be assigned, for example an intermediate threshold of 80 ms allows a first score of 0 to be defined if the value is less than 80 ms, a score of 1 if the value is between 80 ms and 120 ms and a score of 2 if the value is greater than 120 ms.

[0196] According to another example, for a descriptor D.sub.i which concerns the entire torso of the patient Z.sub.5, according to a unipolar signal type T.sub.i, on which the signal marker M.sub.i is the absolute amplitude between 40 and 250 Hz of the last 40 milliseconds of the QRS (measurement which corresponds to the measurement of late potentials of the QRS), with a statistical modality which is the average of the values measured on each electrode of the predefined area Z.sub.i, the threshold value can be set at 20 microvolts. Thus, in this case, it is considered that the threshold value of the descriptor is exceeded for a measured tension value which is less than 20 microvolts. In this example, a score of 0 may be assigned to the value above 20 microvolts and a score of 1 may be assigned to the value below 20 microvolts.

[0197] More broadly, many examples of threshold values are described in the publication entitled Caract?risation et d?tection des micro-potentiels anormaux associ?s aux arythmies ventriculaires l?tales, par analyse des signaux ?lectrocardiographiques haute-?esolution enregistr?s ? la surface du torse (Characterization and detection of abnormal micro-potentials associated with lethal ventricular arrhythmias, by analysis of high-resolution electrocardiographic signals recorded on the surface of the torso) (Nolwenn TAN et al.).

[0198] According to different embodiments, the thresholds may correspond to physiological signs characterizing an electrical activity, a physical effort or a medical history. According to one example, the threshold values may be chosen so as to identify an index of the presence of a given cardiac condition or singular cardiac electrical activity.

[0199] The comparison between the value of each descriptor D.sub.i of the subset {D.sub.k} and the respective threshold value V.sub.threshold of each descriptor D.sub.i is performed. 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 the value of one of the descriptors D.sub.i of the subset {D.sub.k} exceeds the threshold value V.sub.threshold. The number of times the threshold value V.sub.threshold is exceeded makes it possible to establish the score. The score calculated according to the invention has the interest 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.

[0200] According to one aspect, the score is equal to the number of values of descriptors D.sub.i exceeding the threshold value V.sub.threshold associated with said descriptor D.sub.i. A score established in this manner is very representative of a cardiac activity. If the subset {D.sub.k} is chosen correctly, the score is very representative of the patient's cardiac activity.

[0201] According to one aspect, the score is compared to a threshold score value. Said threshold score value is specific to the predefined condition ET.sub.1.

[0202] Device for Generating the Electrophysiological Parameter

[0203] 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.

[0204] 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.sub.i.

[0205] According to one embodiment, the device comprises adhesive strips comprising the surface electrodes. According to this aspect, the adhesive strips are intended to be applied to the surface of the patient's body.

[0206] Advantageously, each adhesive strip comprises several surface electrodes. This provision makes it easier to install the electrodes on the patient, the installation of a strip comprising several electrodes being simpler than installing the electrodes one by one.

[0207] According to one aspect, the device comprises a vest or jacket comprising the plurality of measuring electrodes EL. The vest is intended to be put on by the patient. This provision enables a rapid installation of the device on the patient. According to one example, the device comprises at least 14 electrodes.

[0208] The device comprises a means of measuring the signal of each channel V.sub.i. More precisely, the measurement means is configured to measure an electrical potential of each of the channels V.sub.i. For example, the measurement means may be an acquisition card. The acquisition card may comprise an input to collect an electrical signal, and an analog digital converter to digitize the acquired signal. The digitized signal is then transmitted to a calculator. For example, the digitized signal may be transmitted to a computer that performs the processing steps on the signal.

[0209] The device comprises a means of calculation. The calculation means records the measurements of channels V.sub.i supplied by the measurement means. The calculator then processes this data.

[0210] The calculator selects a subset {D.sub.k} of descriptors D.sub.i from the set {D.sub.N} of descriptors D.sub.i. This selection is made according to the input parameter Inp.

[0211] The calculator then calculates the value of each descriptor D.sub.i of the subset {D.sub.k}. This calculation is performed from measurements of channels V.sub.i. The calculation is performed according to the predefined area Z.sub.i, signal type T.sub.i, signal marker MS.sub.i and statistical modality ST.sub.i selected for the descriptor D.sub.i in question.

[0212] The calculator then compares the value of the selected descriptors D.sub.i with the threshold value V.sub.threshold relative to each descriptor D.sub.i.

[0213] The calculator then calculates a score based on comparisons of the estimated values of the descriptors D.sub.i with their threshold value V.sub.threshold. The score is calculated as described above.

[0214] According to one embodiment, the device comprises a device for detecting the respiration phases of the patient. Such a device detects when the patient is in the expiration phase or flat respiration phase. It also detects when the patient is in the inspiration phase. Respiration tends to interfere with the measurements carried out at the level of the electrodes EL. This is notably the case during inspiration phases during which heart beats and their measurement may be affected. Preferably, the measurement of the potential of each channel V.sub.i is performed during the expiration phase. This provision makes it possible to avoid disturbances caused by a measurement during inhalation phases. The device for detecting respiration phases may be connected to the calculator. Alternatively, it is connected to the means of measuring the signal of each channel V.sub.i.

[0215] According to one embodiment, the device for detecting respiration phases is a plethysmography belt. The plethysmography belt is a practical way to perform this type of detection.

[0216] An object of the invention is also a method for generating an electrophysiological parameter which comprises: [0217] 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, a signal type, a signal marker and a statistical modality for calculation; [0218] arrangement of a plurality of surface electrodes on a patient's body; [0219] recording of a plurality of cardiac electrical activities defining said channels, each channel being obtained by the recordings of at least two electrodes; [0220] estimation of the set of electrophysiological descriptors of the subset, each electrophysiological descriptor being calculated from the statistical modality that is applied to the signal marker of the acquired signal according to the signal type on a selected channel associated with said electrophysiological descriptor; [0221] comparison of the value of the set of electrophysiological descriptors with at least one threshold value specific to the set of electrophysiological descriptors, said at least one threshold value being defined by a statistical distribution of said descriptors of a set of patients of a reference group; [0222] display of a result of the comparison step and a classification of all or part of the results of the comparison step according to a sorting key.

[0223] According to one embodiment, the sorting key takes into account an increasing or decreasing order of incremental values. Alternatively or additionally, the sorting key takes into account the value of the differences recorded between the value of each descriptor and the at least one threshold value relative to said descriptor. Alternatively or additionally, the sorting key takes into account the descriptors, i.e. it classifies the descriptors and their value according to a predefined order. Alternatively or additionally, the sorting key takes into account the area on the torso of the patient considered in the choice of the descriptors. Alternatively or additionally, the sorting key takes into account the descriptors.

[0224] Optionally, the display step comprises a step of displaying a calculated score defining an electrophysiological parameter as a function of the exceeding of at least one threshold value defined by the statistical distribution.

Nomenclature

[0225] D.sub.i: Electrophysiological descriptor [0226] {D.sub.k}: Subset of electrophysiological descriptors [0227] {D.sub.N}: Set of electrophysiological descriptors [0228] Inp: Input parameter [0229] V.sub.i: Channel [0230] Z.sub.i: Predefined area of the patient's body [0231] Z.sub.1: Upper right area of the patient's torso [0232] Z.sub.2: Upper left area of the patient's torso [0233] Z.sub.3: Lower right area of the patient's torso [0234] Z.sub.4: Lower left area of the patient's torso [0235] Z.sub.5: Area covering the totality of the patient's torso [0236] T.sub.i: Signal type [0237] M.sub.i: Signal marker [0238] MS.sub.i: Statistical modality for calculation [0239] EL: Electrode [0240] EL.sub.1: Central electrode [0241] EL.sub.lap: Laplacian electrode [0242] By: Vertical bipole [0243] Bh: Horizontal bipole [0244] DISPO: arrangement of a plurality of electrodes [0245] ENR: Recording of a plurality of electrical activities [0246] EST: Estimation of descriptors [0247] COMP: Comparison of the value of a descriptor with a threshold value [0248] V.sub.threshold: Threshold value [0249] CALC: Calculation of a score [0250] S.sub.ku: Kurtosis [0251] N: Curve representing a normal distribution [0252] P: Flat curve [0253] E: Slender curve [0254] 1: Curve tending to the left [0255] 2: Curve tending to the right [0256] RED: Reduced amplitude range