Methods for quantifying and estimating response levels to hypoglossal nerve stimulation - Patents.com

Abstract

One of the objectives of the present disclosure is to respond to the shortcomings of the prior art and provide a method that allows short-term analysis of the physiological data of sleep apnea patients treated with neurostimulation, so as to quantify the effectiveness of the treatment applied in different stimulation forms and / or the patient condition.To do so, the method presented herein can be, in particular, a computer-implemented method.This method can include: d) detecting ON time and OFF time in a defined time segment of the physiological data, where each ON time corresponds to the moment when the subject is stimulated, and each OFF time corresponds to the moment when the subject is not stimulated; e) detecting at least one respiratory cycle.

Description

[Technical field]

[0001] Technical Field The subject matter of the present disclosure described hereinafter refers to a method for quantifying a response level to neural stimulation. In particular, the subject matter refers to a method for quantifying a response level to neural stimulation in the treatment of obstructive sleep apnea (OSA). [Background technology]

[0002] background Neuromodulation, e.g., electrical stimulation of nerves, is known in the prior art as a reliable and effective type of medical treatment. It presents an opportunity to address many physiological conditions and disorders by interacting with the body's own natural neural processes. Neuromodulation involves the inhibition (e.g., blocking), stimulation, modification, regulation, or therapeutic alteration of activity (electrical or chemical) in the central, peripheral, or autonomic nervous systems. By modulating the activity of the nervous system, several different goals can be achieved. For example, motor neurons can be stimulated at the appropriate time to cause muscle contraction. Additionally, sensory neurons can be blocked to reduce pain or stimulated to provide a signal to a subject. In yet other examples, modulation of the autonomic nervous system can be used to regulate various involuntary physiological parameters (e.g., heart rate and blood pressure). Neuromodulation may offer an opportunity to treat several diseases or physiological conditions. Various devices and techniques are used in an attempt to provide optimal stimulation of the tissue of interest.

[0003] Within the meaning of this disclosure, the terms "stimulation," "modulation," "neurostimulation," and "neuromodulation" are used synonymously, unless something else becomes apparent from any given context.

[0004] Typically, neurostimulators deliver therapy in the form of electrical pulses and include one or more electrodes in proximity to a target location (e.g., a particular nerve or section thereof). The electrical stimulation is programmable and adjustable through various parameters (e.g., electrode polarity, voltage, current amplitude, pulse frequency, pulse width, etc.) that define the electrical stimulation therapy to be delivered to a user in need of treatment. Such parameters may be programmed or programmable to deliver the desired stimulation and end result desired from the stimulation therapy.

[0005] One of the conditions for which neuromodulation can be applied is obstructive sleep apnea (OSA), a breathing disorder characterized by recurrent episodes of partial or complete obstruction of the upper airway during sleep. One of the causes of OSA is the inability of the tongue muscle to withstand the negative inspiratory pressure in the pharynx due to sleep-related loss of muscle tone. As the tongue is pulled backward, it obstructs the upper airway, decreasing ventilation and lowering lung and blood oxygen levels. For example, stimulation of the hypoglossal nerve and / or cervical nerve trap contracts the tongue muscle, e.g., the genioglossus muscle, thereby keeping the airway open and unobstructed. Because the genioglossus muscle is responsible for the forward movement of the tongue and the stiffening of the anterior pharyngeal wall.

[0006] Polysomnography (PSG) is regularly performed to detect, monitor and test the occurrence of sleep apnea events, and to test the effectiveness of neurostimulation, and in particular hypoglossal nerve stimulation or cervical nerve trap neurostimulation (HGNS and ACS), as a method of treatment of sleep apnea. PSG allows the monitoring of many body functions, such as brain activity (EEG), eye movement (EOG), muscle activity or skeletal muscle activation (EMG), thermocouples, oxygen saturation and cardiac rhythm (ECG). Furthermore, according to the present disclosure, PSG data further includes airflow data and respiratory band signals. The aforementioned data can be regularly captured during any standard PSG-based method.

[0007] prior art Studies on the efficacy of sleep apnea treatments, or "Functional capacity evaluations" (FSE), are known in the prior art. They often rely on reductions in the apnea-hypopnea index (AHI) or oxygen desaturation index (ODI) as indices of treatment efficacy. However, these techniques require long-term PSG data collection (where long-term can mean more than two nights of sleep study, one night pre-treatment as a baseline and one post-treatment). Once long-term PSG data is collected, the AHI / ODI scoring is compared to evaluate the efficacy of the treatment. However, the results obtained in this way cannot be used to guide titration or prediction of treatment response at the start of the night / treatment. Moreover, the results tend to be highly variable.

[0008] Another well-known method for measuring the effectiveness of HGNS treatment relies on visual observation of tongue protrusion or any other anatomical changes that may reflect HGNS-induced airway opening. These methods are qualitative rather than quantitative and therefore have the disadvantage of being unreliable.

[0009] Other tests may be of an invasive nature, including procedures such as drug-induced sleep endoscopy (DISE). This method is used at the beginning of treatment to guide dose setting and identify therapeutic HGNS and / or ACS settings. However, the drawbacks of this method are many: it is highly invasive, with the risk of respiratory blockage or cessation and allergic reactions. Secondly, since it is drug-induced, the results may not reflect the actual therapeutic level required in normal sleep conditions. Finally, natural sleep is not accurately reproduced, especially with regard to body position, which can significantly affect the therapeutic response.

[0010] Further approaches known from the prior art include ultrasound techniques (Korotun et al., 2020, https: / / doi.org / 10.1093 / sleep / zsaa056.645; Al-Sherif et al., 2020, doi: 10.21037 / jtd-cus-2020-001), tomography techniques (Xiao et al., 2020, doi: 10.1177 / 0194599820901499) or baseline overnight sleep studies (Schwartz et al., 2011, doi: 10.1164 / rccm.201109-1614OC). [Prior art documents] [Non-patent literature]

[0011] [Non-Patent Document 1] Korotun et al., 2020, https: / / doi.org / 10.1093 / sleep / zsaa056.645 [Non-Patent Document 2] Al-Sherif et al., 2020, doi: 10.21037 / jtd-cus-2020-001 [Non-Patent Document 3] Xiao et al., 2020, doi: 10.1177 / 0194599820901499 [Non-Patent Document 4] Schwartz et al., 2011, doi: 10.1164 / rccm.201109-1614OC Summary of the Invention [Means for solving the problem]

[0012] Summary of the Invention One of the objectives of the present disclosure is to address the shortcomings of the prior art and to provide a method that allows for short-term analysis of physiological data of sleep apnea patients treated with neurostimulation to quantify the effectiveness of treatment applied in different stimulation configurations and / or patient status. To do so, the method presented herein may be, inter alia, a computer-implemented method.

[0013] According to one aspect of the present disclosure, there is provided a computer-implemented method for quantifying a level of response to neurostimulation, the method comprising the steps of: a) transferring the stimulation parameter data to a data processing unit; b) transferring at least a subgroup of physiological data corresponding to the subject to said data processing unit; c) identifying at least one physiological signal, e.g., an airflow signal, of said physiological data corresponding to an effect of treatment on airway management of said subject; d) detecting ON and OFF time points in defined time segments of the physiological data, where each ON time point corresponds to a moment when the subject is stimulated and each OFF time point corresponds to a moment when the subject is not stimulated; e) detecting at least one respiratory cycle (also referred to in this disclosure as a "respiratory cycle"); f) for each detected respiratory cycle, analyzing the airflow segment and checking whether it is synchronized with an ON time or with an OFF time; g) sorting each airflow segment into one of two groups based on its respectively checked synchronization with the ON / OFF time points, said two groups being an ON group containing airflow signals synchronized with the ON time points and an OFF group containing airflow signals synchronized with the OFF time points; h) determining separately for each of said two groups the airflow curves from all airflow time points of each respective group for their relative occurrence within said respiratory cycle; i) calculating an air volume for each of the two air flows; j) determining the effect of the stimulation based on the ratio of the two volumes; A method is provided that includes:

[0014] Neurostimulation in accordance with the present disclosure may include stimulation or modulation of a nerve or nerves corresponding to the upper airway of a subject or patient, i.e., a nerve or nerves having an effect on the genioglossus muscle, particularly for the treatment of obstructive sleep apnea. In this regard, neurostimulation may particularly refer to stimulation of the hypoglossal nerve or the cervical fascia nerve.

[0015] Advantageously, only a subgroup of physiological data is required to carry out the method of the present disclosure. The subgroup may, for example, include one or more sub-sets of physiological data corresponding to a subject, for example, only PSG or even only a specific trajectory of the PSG data. This subgroup of physiological data may then be transferred to a processing unit.

[0016] The physiological signal of step c) can be any type of electrical or non-electrical signal. Preferably, the physiological signal comprises a PSG signal. However, the physiological signal can also be a live image or sound.

[0017] The airflow curve determined in step h) may preferably be a mean airflow curve. However, it is also possible that a mean airflow curve, a maximum airflow curve or a minimum airflow curve is determined. Similarly, the air volume calculated in step i) may preferably be a mean air volume. However, it is also possible that a mean air volume, a maximum air volume or a minimum air volume is calculated.

[0018] Further, the method includes the steps of: k) determining a therapy "hit rate" based on the percentage of respiratory inspirations (or portions thereof) that are synchronized with ON times within the defined time segment; l) determining the subject's average respiratory rate within said defined time segment; may include.

[0019] All relevant parameters of the neurostimulation to be evaluated may, for example, be stored as a data file, for example a data file of type xls (more commonly referred to as an Excel sheet). The data processing unit used in the computer-implemented method shown may then be programmed to automatically retrieve the respective parameters of interest from the xls data file. These parameters may include, for example, pulse frequency, pulse duration, amplitude, slope within train duration, train length, train interval of stimulation, etc. Furthermore, stimulation parameter data may include physiological parameters of the subject and the body / head position and / or sleep stage of the subject.

[0020] The physiological data may preferably be PSG data, which may include, for example, brain activity (EEG), eye movements (EOG), muscle activity or skeletal muscle activation (EMG), thermocouples, oxygen saturation, cardiac rhythm (ECG), airflow data and respiratory band signals. The physiological data and / or the PSG data are stored as data files of type edf. As in the case of the stimulation parameter input file, the data processing unit may be programmed to automatically search for the respective signal of interest contained in the PSG data file.

[0021] According to an advantageous embodiment, the method comprises the following steps: b') overlapping said transferred physiological data and said transferred stimulation parameter data; This allows the user to divide the physiological data into time segments of fixed stimulus shape and subject state.

[0022] The data processing unit may be part of a processor configured to perform all the logical steps of the method described herein. Thus, the processor may include any electronic circuit that may be configured to perform logical operations on at least one input variable. The processor may include one or more integrated circuits, microchips, microcontrollers, and microprocessors, which may be all or part of a central processing unit (CPU), a digital signal processing unit (DSP), a field programmable gate array (FPGA), or any other circuit known to those skilled in the art that may be suitable for executing instructions or performing logical operations. According to a preferred embodiment, the processor is part of a computer or a portable computing device.

[0023] The ON and OFF time points are defined as the timings within the EMG signal of the PSG data that are stimulation on or off, respectively. This process has the advantage of allowing quantification of the response level to nerve stimulation of the hypoglossal or cervical nerve.

[0024] The method presented above can preferably be performed with a technician / FCE tool used to analyze and measure the effectiveness of neurostimulation therapy, which in particular allows detection of the actual period of the subject's neurostimulation based on the EMG signal. Using the EMG signal, the airflow signal can be analyzed breath by breath to, among other things, quantify the baseline airflow without HGNS and / or ACS, the airflow synchronized with HGNS and / or ACS, and subsequently the actual relative change in the subject's airflow as a result of the applied therapy.

[0025] Further to the above, the method presented above allows quantifying the number of breaths and / or inspirations (or the average portion of inspirations) that are synchronized with a stimulation event, or more precisely, that are synchronized with a stimulation event during a previous stimulation. The ratio of the number of breaths and / or inspirations that are synchronized with HGNS and / or ACS to the total number of breaths (i.e., the number of breaths and / or inspirations that are synchronized with HGNS, plus the number of breaths and / or inspirations that are not synchronized with HGNS) within a defined time segment is referred to as the "hit rate". The combination of the above data provides a reliable method for determining the effectiveness of a given HGNS and / or ACS treatment. Thus, the method is very useful for estimating the response to the applied HGNS and / or ACS treatment, thus allowing better and more accurate titration of stimulation settings.

[0026] Moreover, the method presented above allows HGNS and / or ACS response assessment for any part of sleep, and more precisely for short segments, from one night after implantation, based solely on non-invasive passive data collection of EMG, airflow and respiration band signals, which can all be derived from PSG data, resulting in rapid results.

[0027] Furthermore, the effectiveness of the analyzed treatment can be assessed directly based on the effect of the treatment on the subject's airflow, rather than only indirectly through quantification of potential reductions in sleep apnea-related symptoms (e.g., AHI, ODI, etc.).

[0028] According to a preferred embodiment of the computer-implemented method for quantifying a level of response to neurostimulation disclosed herein, said method comprises the steps of: c'') defining at least one time segment of both transferred data, where step c'') is preferably performed before step c'); It may further include:

[0029] Thus, the method allows for accurate quantification of the efficacy of HGNS and / or ACS within a desired time range of PSG data from a subject, which can be selected to be very small, thus giving the method a distinct temporal advantage over conventional PSG-based methods.

[0030] As a result, the FSE can be based on objective, quantitative, and reliable measurements of treatment parameters rather than visual scoring of sleep apnea symptoms, which can be highly subjective.

[0031] The method may be further characterized in that the detected ON time and OFF time are each detected based on the EMG signal of the PSG data. Furthermore, the at least one respiratory cycle may also be detected based on the respiratory band signal of the PSG data. According to a specific embodiment, at least 70 respiratory cycles may be detected based on the respiratory band signal of the physiological data. It is also possible that the detection of the respiratory cycle may be based on other signals of the physiological data, such as signals obtained by a motion sensor or an acoustic sensor.

[0032] The determined mean airflow of the ON group in step g) may correspond in particular to the mean airflow induced by nerve stimulation, for example by stimulation of the nerves of the hypoglossal nerve or the cervical fasciculus.Furthermore, the determined mean airflow of the OFF group in step g) may correspond to the mean airflow without nerve stimulation.

[0033] It is also possible that the baseline determined in step h) can be provided, said baseline corresponding to the mean airflow determined within the OFF group. Using the OFF group as the baseline is beneficial because it allows for the determination of the impact of the treatment without the need for a baseline PSG before the stimulation procedure.

[0034] Additionally, the calculation of the average volume of air inspired in synchronization with neural stimulation may be based on the area under the graphed inspiratory portion of the average airflow induced by neural stimulation (A), and the calculation of the average airflow without said neural stimulation may also be based on the area under the graphed inspiratory portion of the average airflow without stimulation of the respective nerve (B).

[0035] In a preferred embodiment, the method may further comprise the calculation of a stimulation hit rate, which is determined in particular by the following steps: l) determining the percentage of the inspiratory phase, relative to the respiratory cycle, that is synchronized with each of said neural stimulations; m) calculating an overall hit rate as a percentage of respiratory cycles greater than a predefined threshold compared to respiratory cycles less than said predefined threshold for those inspiration phases synchronized with said respective neural stimulation; may include.

[0036] For example, if the hit rate=80%, this means that 80% of a particular respiratory volume were higher than the predefined threshold for their inspiratory phase synchronized with HGNS and / or ACS. Similarly, 20% were lower than the predefined threshold for their inspiratory phase synchronized with HGNS and / or ACS.

[0037] Preferably, the percentage of the inspiration phase synchronized with each of the neural stimulations may be determined based on the detected ON and / or OFF time points.

[0038] According to an advantageous embodiment of the method, at least one of the following intermediate and / or final results may be graphically represented: -At the time of stimulation ON / OFF -Respiration cycle -Airflow signal -air current -Air volume -Stimulus effects - Intake portion of the mean airflow - the volume of air inspired by each nerve stimulation - volume of air inspired without stimulation of each nerve - The percentage of inspiration phases synchronized with each nerve stimulation -Average breathing rate - Hit rate.

[0039] The airflow curve determined in step h) may preferably be a mean airflow curve. However, it is also possible that a mean airflow curve, a maximum airflow curve or a minimum airflow curve is determined. Similarly, the air volume calculated in step i) may preferably be a mean air volume. However, it is also possible that a mean air volume, a maximum air volume or a minimum air volume is calculated.

[0040] In particular, the results can be displayed on the screen of a computer or a portable electronic device (e.g., a smartphone). In a preferred embodiment, the results can be displayed using a specific program that includes a graphical user interface (GUI).

[0041] According to a preferred embodiment of the computer-implemented method, the stimulation parameter data and / or the physiological data may be continuously obtained in real time during the method. In particular, the method allows for the selection of whether real-time data (i.e., data obtained during a currently ongoing stimulation procedure) or stored data (i.e., data obtained during a previous stimulation treatment) is used.

[0042] In addition to the above, the method further comprises the steps of: k) the neurostimulation protocol is adjusted based on the effect of the stimulation determined in step j); It may further include:

[0043] The adjustment of the stimulation protocol can be performed manually or automatically. In this manner, the method can also be used in a closed loop manner, where the results obtained with the method are used directly to adjust the stimulation of the subject. Thus, intelligent or interactive methods can be demonstrated in accordance with the present disclosure.

[0044] The method presented herein can be used in the early stages of HGNS and / or ACS treatment, for example, to facilitate the dose setting of HGNS and / or ACS setting.Furthermore, the method can be used to estimate the treatment response at the site during the dose setting.Alternatively or in addition, the method can retrospectively use any PSG data recorded from HGNS and / or ACS patients (e.g., for research activities).

[0045] The invention is not limited to one of the embodiments described herein, but can be modified in many other ways.

[0046] All features and advantages disclosed by the claims, the specification and the drawings, including structural details, spatial arrangements and methodological steps, may be essential to the present invention, either by themselves or in various combinations with one another.

[0047] BRIEF DESCRIPTION OF THE EXEMPLARY EMBODIMENTS The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate several examples of the subject matter of the present disclosure. The drawings show: [Brief description of the drawings]

[0048] [Figure 1a] A flow chart including steps of a method according to a preferred embodiment; [Figure 1b] 1 is a flow chart including steps of a method according to another embodiment; [Diagram 2] Steps in Fig. 1a, b with examples of intermediate results; [Diagram 3]Steps in Fig. 1a, b with an example of the final result. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0049] DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS FIG. 1a shows a flow chart including steps S1 to S7 of a method according to a preferred embodiment. The method shown in FIG. 1a includes a first step S1: transferring physiological data corresponding to a subject (in the case of the embodiment shown in FIG. 1a, PSG data) and stimulation parameter data to a data processing unit (e.g., a computer). The parameter data may preferably be saved as an .xls file. The data processing unit used in the computer-implemented method shown above may then be programmed to automatically retrieve each parameter of interest from the data file. The physiological data may preferably be saved as an .edf file. An edf file is a standardized digital format for any PSG test. Similarly, the data processing unit may be programmed to automatically retrieve each signal of interest contained in the physiological data.

[0050] After both data are transferred to the data processing unit, the processor automatically detects the stimulation train (i.e., the ON / OFF time points) and the respiratory cycle in the transferred data according to a second step S2. In this way, the ON and OFF time points in the physiological data are detected. The ON and OFF time points are defined as the timings in the EMG signal of the PSG data when the stimulation is on or off, respectively.

[0051] As shown in Figures 1a and 2, S2 may be followed by a third step S3. According to step S3, the detected stimulation train (i.e., the ON / OFF time points) is superimposed onto the airflow signal retrieved from the physiological data. Preferably, this step is also performed automatically by the processor of the computer used to perform the method performed therein. After superimposing the stimulation train onto the airflow signal, the superimposed data may be color-coded for improved visualization, as shown in Figure 2.

[0052] After S3, a fourth step S4 is performed: lining up all detected breaths within a respiratory cycle in a defined time segment.

[0053] After the ON and OFF time points have been detected and the detected breaths have been aligned together, step S5 follows: for each breath, separate a portion of the airflow segment into either an ON group or an OFF group, where the ON group contains airflow signals that are synchronized with the ON time points and the OFF group contains airflow signals that are synchronized with the OFF time points.

[0054] Based on the two groups, step S6 is performed during which mean airflow curves are calculated separately for each of the two groups. These curves correspond to the airflow signal of an average single breathing cycle synchronized with either the ON time or the OFF time (depending on which curve is calculated for the ON group or the OFF group) and include inspiration portions 1a, 1b (see FIG. 2). Based on the area under each inspiration portion of the mean airflow curves 1a, 1b, the respective average air volumes inspired either synchronized with neural stimulation or not synchronized with neural stimulation can be calculated.

[0055] Finally, step S7 may be performed: determining the percentage of detected breaths that were synchronized with neural stimulation (hit rate).

[0056] The method of Figure 1b differs from the method of Figure 1a in that Figure 1b describes a closed-loop method. In particular, the transferred data used in the method of Figure 1b is obtained in real time, i.e., during or between subject sessions or patient stimulation (see step S1).

[0057] Once the method produces results, for example in the form of hit rates, the stimulation parameters used to stimulate the subject can be adjusted based on those results. This can occur either manually or automatically.

[0058] 2 and 3 show intermediate results (FIG. 2) and final results (FIG. 3), preferably as displayed on a suitable device.

[0059] List of reference numbers Sn Step n

Claims

1. A computer-executed method for quantifying a response level to nerve stimulation, the method comprising the following steps: a) transferring stimulation parameter data to a data processing unit; b) transferring at least a subgroup of physiological data corresponding to a subject to the data processing unit; c) identifying at least one physiological signal of the physiological data corresponding to an effect of treatment on airway maintenance of the subject; d) detecting ON and OFF time points in a defined time segment of the physiological data, wherein each ON time point corresponds to the moment when the subject is stimulated and each OFF time point corresponds to the moment when the subject is not stimulated; e) detecting at least one respiratory cycle; f) for each detected respiratory cycle, analyzing an airflow segment and checking whether it is synchronized with an ON time point or an OFF time point; g) sorting each airflow segment into one of two groups based on its respective checked synchronization with the ON / OFF time points, the two groups being an ON group comprising airflow signals synchronized with the ON time point and an OFF group comprising airflow signals synchronized with the OFF time point; h) for each of the two groups, separately determining an airflow curve from all airflow segments of each respective group; i) calculating an air volume for each of the two airflows; j) determining an effect of the stimulation based on visualization of the two airflows and / or a ratio of the two volumes. A method comprising the above steps.

2. The method according to claim 1, characterized in that the physiological data comprises PSG data.

3. The method further comprises the following steps: c') superimposing the transferred physiological data and the transferred stimulation parameter data on each other, where step c') is preferably a step performed before step c), The method according to claim 1 or 2, characterized in that it further comprises . **Claim 4** The method comprises the following steps: c'') defining at least one time segment within both transferred data, where step c'') is preferably a step performed before step c'), The method according to claim 1, characterized in that it further comprises . **Claim 5** The method comprises the following steps: d') arranging all detected respiratory cycles together within the respiratory cycle in the defined time segment. The method according to any one of claims 1, characterized in that it further comprises . **Claim 6** The method according to claim 1, characterized in that the detected ON time point and OFF time point are each detected based on the EMG signal of the physiological data. **Claim 7** The method according to claim 1, characterized in that the at least one respiratory cycle is detected based on the respiratory band signal of the physiological data. **Claim 8** The determined average airflow of the ON group in step i) corresponds to the average airflow (A) induced by nerve stimulation, and the determined average airflow of the OFF group in step i) corresponds to the average airflow (B) without nerve stimulation, the method according to claim 1, characterized in that. **Claim 9** The baseline is determined in step i), and the baseline corresponds to the average airflow determined within the OFF group, the method according to claim 1, characterized in that. **Claim 10** The method comprises the following steps: k) calculating the volume of air inhaled with nerve stimulation and the average airflow without stimulation of the nerve; The method according to claim 1, characterized in that it further comprises.

11. The calculation of the volume of air inhaled with nerve stimulation is based on the area under the graphed inhalation portion of the airflow (A) induced by nerve stimulation; The calculation of the average airflow without stimulation of the nerve is based on the area under the graphed inhalation portion of the airflow (B) without stimulation of the nerve, according to the method of claim 10.

12. The method according to claim 1, characterized in that it further comprises calculating a stimulation hit rate.

13. The calculation of the stimulation hit rate comprises the following steps: l) for each respiratory cycle, determining the percentage of the inhalation phase synchronized with the nerve stimulation and predefining a desired threshold; m) calculating an overall hit rate as the percentage of respiratory cycles in which the inhalation phase synchronized with the nerve stimulation exceeds the predefined threshold compared to respiratory cycles below the predefined threshold, The method according to claim 12, characterized in that it comprises.

14. The percentage of the inhalation phase synchronized with the nerve stimulation is characterized in that it is determined based on the detected ON time point and / or OFF time point, according to the method of claim 13.

15. At least one of the following intermediate results and / or final results is Stimulation ON / OFF time points Respiratory cycles Airflow signal Average airflow Average air volume Effect of stimulation Inhalation portion of the airflow Volume of air inhaled with nerve stimulation Volume of air inhaled without nerve stimulation Hit rate Average respiratory rate The method according to claim 1, characterized in that it is graphically represented.

16. The method according to claim 1, characterized in that the stimulation parameter data and / or the physiological data are obtained continuously in real time.

17. The method according to claim 16, characterized in that the effect of the stimulation determined in step j) is for manually or automatically adjusting the nerve stimulation protocol.

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