ACTIVE IMPLANTABLE MEDICAL DEVICE FOR THE TREATMENT OF HEART FAILURE WITH VAGUS NERVE STIMULATION

Abstract

An active implantable medical device includes a VNS pulse bursts generator for stimulation of the vagus nerve according to several selectable configurations. The device may further include a sensor of the current activity level of the patient. The generator is controlled on the activity signal via a classifier determining the class of the current level of activity among a plurality of classes of activity. A controller selects a configuration of VNS therapy depending on the class of activity thus determined. Limits of the activity classes are dynamically changeable by a calibration module that conducts a historical analysis of the successive current activity levels over a predetermined analysis period. The calibration module can prepare a histogram of the historical analysis, and can define the limits of the activity classes depending on the outcome of the historical analysis and the histogram.

Claims

1. An active implantable medical device, comprising: a pulse generator for stimulation of a vagus nerve; an interface configured to receive an indication of activity level; a control circuit coupled to the interface and the pulse generator, the control circuit configured to determine a class of a current level of activity among a plurality of classes of activity and selects a configuration of pulse therapy for the generator depending on the determined class of activity; wherein the control circuit is further configured to dynamically change the thresholds of activity that define the plurality of classes of activity and to conduct a historical analysis of successive current activity levels over an analysis period.

2. The active implantable medical device of claim 1, wherein the control circuit is further configured to prepare a histogram of the historical analysis and define the thresholds of the activity classes depending on an outcome of the historical analysis and the histogram.

3. The active implantable medical device of claim 2, wherein the control circuit is further configured to re-partition the calculated histogram into the plurality of classes of activity based on recent history to generate the thresholds.

4. The active implantable medical device of claim 3, wherein the re-partitioning of the histogram comprises defining limits between successive activity classes so that a cumulative sum of sampled activity levels in each class is a predetermined fraction of a cumulative sum of sampled activity levels of all classes.

5. The active implantable medical device of claim 4, wherein said predetermined fraction is a fraction identical for all classes.

6. The active implantable medical device of claim 3, wherein the control circuit is further configured to determine a minimum value and a maximum value of said activity levels sampled during the analysis period and partition the histogram between said minimum and maximum values.

7. The active implantable medical device of claim 3, wherein the control circuit is further configured to determine a nominal value of rest and a nominal value of effort according to said activity levels sampled during the analysis period, and wherein the control circuit is further configured to partition the histogram between the determined nominal rest and effort values.

8. The active implantable medical device of claim 7, wherein the nominal rest value and the nominal effort value are defined based on a predetermined percentage of a cumulative sum of the sampled levels of all classes.

9. A method for configuring a vagus nerve stimulation generator, comprising: using sensor input to collect samples of activity level; comparing, using a processor of the vagus nerve stimulation generator, the samples relative to a series of thresholds defining a plurality of activity classes; operating a pulse generator of the vagus nerve stimulation generator for stimulation of the vagus nerve based on the comparison; using the processor, periodically re-calibrating the thresholds that define the plurality of activity classes by evaluating a set of historical activity level samples.

10. The method of claim 9, further comprising: using the processor, generating a histogram of the set of historical activity level samples and setting the thresholds to achieve a predetermined distribution within the histogram.

11. The method of claim 10, further comprising: using the processor, re-partitioning the histogram into the plurality of classes of activity based on recent history to generate the thresholds.

12. The method of claim 11, wherein the re-partitioning of the histogram comprises defining limits between successive activity classes so that a cumulative sum of the samples of activity levels in each class is a predetermined fraction of a cumulative sum of the samples of activity levels of all classes.

13. The method of claim 12, wherein said predetermined fraction is a fraction identical for all classes.

14. The method of claim 10, wherein the re-partitioning of the histogram comprises determining a minimum value and a maximum value of the samples of activity levels and partitioning the histogram between the minimum and maximum values.

15. The method of claim 10, wherein the re-partitioning of the histogram comprises determining a nominal value of rest and a nominal value of effort according to the samples of activity levels and partitioning the histogram between the determined nominal rest and effort values.

16. The method of claim 15, wherein the nominal rest value and the nominal effort value are defined based on a predetermined percentage of a cumulative sum of the samples of activity levels of all classes.

17. A controller comprising one or more circuits configured to: control pulse therapy produced by a pulse generator; receive an indication of activity level via an activity sensor; determine a class of a current level of activity among a plurality of classes of activity; select a configuration of the pulse therapy for the generator based on the determined class of activity; and dynamically change the thresholds of activity that define the plurality of classes of activity by conducting a historical analysis of successive activity levels over an analysis period.

18. The controller of claim 17, wherein the one or more circuits are configured to prepare a histogram of the historical analysis and define the thresholds of the activity classes depending on an outcome of the historical analysis and the histogram.

19. The controller of claim 18, wherein the one or more circuits are configured to re-partition the calculated histogram into the plurality of classes of activity based on recent history to generate the thresholds.

20. The controller of claim 19, wherein the one or more circuits are configured to re-partition the calculated histogram by defining limits between successive activity classes so that a cumulative sum of sampled activity levels in each class is a predetermined fraction of a cumulative sum of sampled activity levels of all classes.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0013] FIG. 1 is an illustration providing an overview of the device of the invention, showing the generator, leads, the myocardium and the vagus nerve.

[0014] FIG. 2 is a schematic block corresponding to the generator of the device.

[0015] FIG. 3 is a 24-hour histogram of patient activity detected by an accelerometer sensor. This Figure illustrates the partitioning of this histogram into four classes corresponding to four different configurations of VNS therapy.

[0016] FIG. 4 shows the profile of a histogram established over 24 hours of the mean values of patient activity collected using a physiological minute ventilation sensor.

[0017] FIG. 5 is derived from the histogram in FIG. 4 after accumulation of the collected values and defines definition of activity levels of rest and of exercise.

[0018] FIG. 6 is a diagram showing the long-term evolution of the activity levels of rest and of exercise as daily determined as illustrated in FIG. 5.

DETAILED DESCRIPTION

[0019] Such a pacemaker includes a programmable microprocessor provided with circuits for processing and delivering stimulation pulses to implantable electrodes. It is possible to transmit to it (e.g., via telemetry electronics) software that is stored in memory and executed to implement the functions of the invention that are described below. The methods and modules of the present specification may be implemented by appropriate programming of the control software of a VNS stimulator. In particular, the methods may be implemented by software (e.g., by appropriate computer code algorithms stored in memory and executed by a microcontroller or a digital signal processor). For the sake of clarity, the various processing applied will be broken down and diagrammed by a number of different functional blocks in the form of interconnected circuits, however this representation is only illustrative. Other embodiments may fall within the scope of the appended claims.

[0020] In FIG. 1, the reference 10 designates the housing of an implantable generator for vagus nerve stimulation. The stimulation is delivered by a lead 12 bearing at its distal end an electrode implanted on the vagus nerve 14. The generator 10 applies produced pulse trains that are used to stimulate the nerve. To allow delivery of VNS pulses in synchronism with the heartbeat, the generator 10 also has a cardiac lead 16 with an electrode 18 provided at its distal end for collecting the electrical activity of the myocardium 20. For example, the cardiac lead 16 and electrode 18 may be used to collect endocardial electrogram signals, which are then used to drive the generator 10. The goal of such synchronization may be to deliver stimulation pulses as a function of the heart rate and at the most appropriate moment of the cardiac depolarization wave.

[0021] FIG. 2 schematically illustrates the main functions implemented within the housing 10 as part of the invention. This housing includes a pulse generator 22 of VNS delivered to the vagus nerve via the lead 12 at the output. Regarding inputs, the generator 22 is controlled, firstly, by the EGM electrogram signal delivered by the lead 16 and, secondly, by an activity signal delivered by a sensor 24.

[0022] The sensor 24 may be a motion sensor such as an accelerometer sensor, or G sensor. Other types of sensors may be used, for example (as will be described below in connection with FIGS. 4 to 6) a physiological sensor such as a minute ventilation sensor or MV sensor, providing an indication of the patient activity level according to metabolic needs notably measured from the rhythm and from the respiratory volume.

[0023] The activity sensor 24 is used for controlling or modulating the VNS therapy according to the detected current level of activity, for example by selection between several energy levels of the VNS stimulation pulses. This may include, for some patients, stimulating with decreasing energy gradually as the activity increases, so as not to prevent the heart rate from accelerating due to the effort produced by the patient.

[0024] For other heart failure patients who have poorly controlled spontaneous heart rate despite the treatment with beta-blockers, the therapeutic goal may be, conversely, to decrease heart rate during exercise, so to increase the energy level of the VNS therapy with activity.

[0025] This modulation of the VNS therapy results from the comparison of the level of activity measured by the sensor 24 to a series of successive thresholds, these thresholds corresponding to limits of a set of classes of the patient activity. Advantageously provided by the present invention, these different classes and their limits are no longer defined in a fixed and undifferentiated manner, but so as to better adapt to suit the patient and to be dynamically changeable over time. For this purpose, the device includes a calibration circuit 26 (i.e., a calibration module comprising executable computer code stored in memory) to establish and recalculate the thresholds of the different classes of activity. The generator 22 can use these thresholds to select the appropriate VNS therapy.

[0026] This calibration circuit 26 may operates from activity measurements delivered by the sensor 24 according to systems and methods as described below. Successive samples of the patient's activity are collected over a predetermined time (e.g., over the last 24 hours). Each sample may be an average (or some other aggregate) of the level of activity measured by the sensor over a period of a few seconds, so as to smooth the instantaneous variations of the accelerometric signal. In other embodiments, a median or a non-smoothed signal are used.

[0027] The calibration circuit 26 then sets up a histogram of the set of values thus measured and stored for 24 hours. An example of this histogram is illustrated in FIG. 3, with the A activity level corresponding to each sample value in abscissa, and the number of occurrences of each of these values in ordinate.

[0028] The activity A varies between zero level (immobility of the patient, for example during periods of rest or sleep) and a maximum level of activity Amax, which can vary significantly from one day to another depending on the maximal exercise produced by the patient during the day in question. The envelope of this histogram is a curve C related to a specific activity profile, specific to the patient and to the considered period of 24 hours.

[0029] The calibration circuit then partitions the histogram into a plurality of classes or slots, for example four slots T1-T4, each corresponding to a different configuration of VNS therapy that may be selected by the generator 22 (e.g., therapies for each activity level differing in their stimulation energy level, which may gradually decrease, for example, when the current activity of the patient increases).

[0030] The partition may be performed according to a predetermined relation. For example, in the illustrated example, the successive classes T1-T4 are defined so that the cumulative sum of sampled levels in each class is a predetermined fraction, e.g. an identical fraction of 25%, of the cumulated sum of sampled levels of all classes. In other words, the boundaries between the successive classes T1-T4 are selected so that the area under the curve C is the same for each of the classes, and equal to 25% of the total area under the curve C.

[0031] The thresholds defining the boundaries between the classes T1-T4 thus determined are stored by the generator 22, which can then select a particular VNS therapy depending on the current level of activity of the patient.

[0032] Calibration as described above is preferably repeated at regular intervals, for example every 24 hours or every 48 hours, in order to incorporate a possible evolution of the condition of the patient, positive or negative.

[0033] Other modes of dynamic definition of the different classes can be considered. Thus, as illustrated in FIGS. 4 to 6, in the case wherein a physiological sensor of the minute ventilation MV type is used to measure the activity, it is necessary to previously determine the activity values at rest A.sub.rest and maximal exercise activity A.sub.effort.

[0034] Indeed, as can be seen in FIG. 4, the envelope curve C of the histogram obtained from the sampled measurements produced by the MV sensor (homologous histogram to that obtained with a G sensor in FIG. 3) does not vary monotonically but has a peak corresponding to the activity at rest.

[0035] To determine the position of this peak, the calibration circuit calculates a cumulated sum of the values of the histogram, giving a profile such as that illustrated in FIG. 5, with a cumulated value ranging between zero and EA (total of all sampled values of the histogram).

[0036] The level of activity at rest A.sub.rest may be defined, for example, as corresponding to 10% of A and the activity level at effort A.sub.effort as that corresponding to 90% of A. Partitioning successive classes is then operated in the manner described above, between the two extreme ends of A.sub.rest and A.sub.effort.

[0037] This technique may advantageously take into account the highly variable dynamics AA from one day to another between A.sub.rest and A.sub.effort as shown in FIG. 6, which represents on a period of 30 days the evolution of the levels of activity A.sub.effort and at rest A.sub.rest as daily determined in the manner described above.