METHOD OF AUTOMATICALLY CONTROLLING A RESPIRATION SYSTEM AND A CORRESPONDING RESPIRATOR

• US 20090159082A1
• Filed: 11/05/2008
• Published: 06/25/2009
• Est. Priority Date: 12/21/2007
• Status: Active Grant

• Current Assignee: Dragerwerk AG & Company KGaA (FPS Vermögensverwaltung GmbH)
• Original Assignee: Drager Medical AG & Co KG (Dragerwerk AG)
• Inventors: Eger, Marcus
• Granted Patent: US 8,109,269 B2
• Time in Patent Office: Days
• US Class Current: 128/204.230
• CPC Class Codes: A61B 5/037 Measuring oesophageal pressure
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/085 Measuring impedance of respiratory organs or lung elasticity
• CPC Class Codes: A61B 5/087 Measuring breath flow
• CPC Class Codes: A61B 5/224 Measuring muscular strength
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7239 using differentiation including higher order derivatives
• CPC Class Codes: A61M 16/026 specially adapted for predicting, e.g. for determining an information representative of a flow limitation during a ventilation cycle by using a root square technique or a regression analysis
• CPC Class Codes: A61M 2016/0027 pressure meter
• CPC Class Codes: A61M 2016/0036 in the breathing tube and used in both inspiratory and expiratory phase
• CPC Class Codes: A61M 2230/60 Muscle strain, i.e. measured on the user
• CPC Class Codes: A61M 2230/63 Motion, e.g. physical activity

Abstract

A method of automatically controlling a respiration system for proportional assist ventilation with a control device and with a ventilator. An electrical signal is recorded by electromyography with electrodes on the chest in order to obtain a signal u_emg(t) representing the breathing activity. The respiratory muscle pressure p_mus(t) is determined by calculating it in the control unit from measured values for the airway pressure and the volume flow Flow(t) as well as the patient'"'"'s lung mechanical parameters. The breathing activity signal u_emg(t) is transformed by means of a preset transformation rule into a pressure signal p_emg(u_emg)(t)) such that the mean deviation of the resulting transformed pressure signal p_emg(t) from the respiratory muscle pressure p_mus(t) is minimized. The respiratory effort pressure p_pat(t) is determined as a weighted mean according to p_pat(t)=a·p_mus(t)+(1−a)·p_emg(t), where a is a parameter selected under the boundary condition 0≦a≦1. The airway pressure p_aw(t) to be delivered is calculated as a function of preselected degrees of assist VA (Volume Assist) and FA (Flow Assist) by sliding adaptation as

$p_{\mathrm{aw}}\left(t_i\right)=k_0+\sum _{j=1}^nk_j·p_{\mathrm{aw}}\left(t_{i-j}\right)+\sum _{j=0}^nh_j·p_{\mathrm{pat}}\left(t_{i-j}\right)$

wherein t_i is a current point in time and t_i−j, wherein j=1, . . . , n, are previous points in time of a periodical time-discrete sampling, and k_j and h_j, wherein j=1, . . . , n are parameters dependent on resistance (R), elastance (E), positive end-expiratory pressure (PEEP), intrinsic PEEP (iPEEP), Volume Assist (VA) and Flow Assist (FA) and the sampling time Δt, and the ventilator is set by the control unit so as to provide this airway pressure p_aw(t_i)

Claims

1. A method of automatically controlling a respiration system for proportional assist ventilation of a subject with a control means and with a ventilator, which delivers a breathing gas with a pressure preset by the control unit, the method comprising the steps of:

recording an electric signal using electrodes on the chest of the subject with the signal generated by electromyography or using sensors on the chest with the signal generated by mechanomyography;

subjecting the signal to signal processing in the control means in order to obtain a signal u_emg(t) representing the breathing activity;

determining the respiratory muscle pressure p_mus(t) by calculating p_mus(t) from one of;

measured values for an airway pressure and a volume flow Flow(t) as well as from the lung mechanical parameters of the patient in the control unit,

the respiratory muscle pressure being equated with a negative airway pressure −

p_occl(t) measured during an occlusion, and

determined values provided by means of an esophageal catheter, which is equipped with pressure sensors for measuring one or more of an intrathoracic pressure p_es(t) and an abdominal pressure p_abd(t), by equating with a transdiaphragmal pressure p_abd(t)−

p_es(t), where p_abd(t) can be optionally assumed to be constant;

transforming the breathing activity signal u_emg(t) into a pressure signal p_emg(u_emg(t)) by means of a preset transformation rule, the transformation rule being determined such that a mean deviation of the resulting transformed pressure signal p_emg(t) is minimized by the respiratory muscle pressure p_mus(t) determined;

determining a respiratory effort pressure p_pat(t) by the control unit as a weighted mean according to p_pat(t)=a·

p_mus(t)+(1−

a)·

p_emg(t), in which a is a parameter selected under the boundary condition 0≦

a≦

1;

calculating the airway pressure p_aw(t) to be delivered by the ventilator of the respiration system in the control unit by a sliding adaptation as a function of preselected degrees of assist VA Volume Assist (VA) for the compensation of elastic recoil forces/resistances and Flow Assist (FA) for a compensation of the resistive forces as

2. A method in accordance with claim 1, wherein the respiratory muscle pressure p_mus(t) is calculated on the basis of the lung mechanical parameters resistance (R), elastance (E) and optionally the value for the iPEEP, the lung mechanical parameters R and E being either calculated or preset.

3. A method in accordance with claim 1, wherein the airway pressure p_aw(t) is calculated in the control unit by sliding adaptation as

p_aw(t_i)=k₁·

p_aw(t<sub>i−

1</sub>)+k₂·

p_pat(t_i)+k₃·

p_pat(t<sub>i−

1</sub>)+k₄.

4. A method in accordance with claim 1, wherein the preset transformation rule is defined by linear or non-linear regression, by means of neuronal networks, machine learning or by simple scaling.

5. A method in accordance with claim 1, wherein the rate of change (time derivative) {dot over (u)}_emg of the breathing activity signal u_emg(t) is continuously checked in the control unit to determine whether said breathing activity signal is below a threshold value criterion and the period of the breathing cycle that is constant with respect to the breathing activity is established for the duration of the period during which the signal is below the threshold value criterion, wherein the lung mechanical parameters E (elastance) or/and the lung mechanical parameter R (resistance) is only determined from measured values that were recorded during a constant period.

6. A method in accordance with claim 5, wherein the value of the breathing activity signal u_emg(t) is continuously checked in the control unit to determine whether the signal is below another threshold value criterion and a passive period of the breathing cycle is established for the duration of the period during which the signal is below said another threshold value criterion, wherein the other lung mechanical parameter elastance (E) or/and the lung mechanical parameter R (resistance) is determined only from measured values that were recorded during a passive period.

7. A method in accordance with claim 1, wherein an electromyographic signal is derived as a difference signal between two surface electrodes.

8. A method in accordance with claim 7, wherein more than two surface electrodes are used to derive electromyographic signals and difference signals are formed between two electrodes as breathing activity signals u_emg,i.

9. A method in accordance with claim 1, wherein more than one sensor is used to derive mechanomyographic breathing activity signals u_emg(t).

10. A method in accordance with claim 8, wherein every individual signal u_emg(t) is subjected to filtering and interference signal suppression as well as to envelope detection.

11. A method in accordance with claim 10, wherein the envelope detection is carried out by taking the absolute value or squaring and subsequent low-pass filtering of every individual signal u_emg,i(t).

12. A method in accordance with claim 1, wherein a maximum of the correlation between a particular signal u_emg,i and the measured signals for the negative airway pressure, volume flow (Flow) and volume is calculated as c_i for every individual signal u_emg,i(t).

13. A method in accordance with claim 12, wherein the signal u_emg,i(t) that has the closest correlation c_i is selected as the breathing activity signal u_emg(t).

14. A method in accordance with claim 11, wherein the activity signal u_emg(t) is calculated as a mean weighted with functions of the maximum correlations of the particular individual signals u_emg,i(t):

u_emg(t)=f(c₁)·

u_emg,1(t)+ . . . +f(c_n)·

u_emg,n(t).

15. A method in accordance with claim 5, wherein a state of the signal being below the threshold value criterion for the time derivative of the breathing activity signal {dot over (u)}_emg(t) is determined only if the state of the signal is below the threshold value criterion and the state persists for a minimum duration.

16. A method in accordance with claim 5, wherein the threshold value criterion for the time derivative of the breathing activity signal is adapted slidingly by determining a threshold value according to {dot over (u)}_thresh={dot over (u)}^min_emg+x·

({dot over (u)}^max_emg−

{dot over (u)}^min_emg) and determining the state of the signal is below the threshold value criterion if {dot over (u)}_emg≦

{dot over (u)}_thresh, wherein {dot over (u)}^max_emg and {dot over (u)}^min_emg are the maximum and the minimum of the time derivative of the breathing activity signal, respectively, which are measured in a previous interval and which are adapted as soon as a new maximum or minimum signal value is obtained, and wherein x is a preselected parameter (0<

x<

1).

17. A method in accordance with claim 5, wherein the threshold value criterion for the time derivative of the breathing activity signal is adapted slidingly by analyzing the measured value distribution V({dot over (u)}_emg) and upon the state of the signal being below the threshold value criterion when a signal value {dot over (u)}_emg is located within the distribution such that only p % of all measured values are located at lower values within the distribution V(u_emg) (quantile of values below the threshold value criterion), wherein p is a preset parameter <

100.

18. A method in accordance with claim 16, wherein an adaptation of the threshold value criterion is performed only if an analysis of the measured value distribution V({dot over (u)}_emg) shows that scaling-invariant parameters of the distribution have remained essentially constant.

19. A method in accordance with claim 5, wherein a fixed threshold value {dot over (u)}_thresh is preset for the threshold value criterion, and the measured values are scaled such that the continuously updated maxima and minima {dot over (u)}^max_emg and {dot over (u)}^min_emg of the time derivative of the breathing activity signal remain within a preset, fixed range of values.

20. A method in accordance with claim 6, wherein the state of the signal being below said another threshold value criterion is established for the breathing activity signal u_emg(t) only if the signal is below the threshold value criterion for a minimum duration.

21. A method in accordance with claim 6, wherein said another threshold value criterion for the breathing activity signal is adapted slidingly by determining a threshold value according to u_thresh=u^min_emg+x·

(u^max_emg−

u^min_emg) and establishing a state with the signal below the threshold value criterion if u_emg≦

u_thresh, wherein u^max_emg and u^min_emg are the maximum and minimum breathing activity signal values, respectively, which were measured in a previous interval and which are adapted as soon as a new maximum or minimum signal value is obtained, and wherein x is a preselected parameter (0<

x<

1).

22. A method in accordance with claim 6, wherein said another threshold value criterion for the breathing activity signal is adapted slidingly by analyzing the measured value distribution V(u_emg) and establishing the state that the signal is below said another threshold value criterion if a signal value u_emg(t) is located within the distribution V(u_emg) such that only p % of all measured values are located at lower values within the distribution V(u_emg) (quantile of values below the threshold value criterion), wherein p is a preset parameter <

100.

23. A method in accordance with claim 21, wherein adaptation of the other threshold value criterion is performed only if an analysis of the measured value distribution V(u_emg) shows that scaling-invariant parameters of the distribution (e.g., skewness, kurtosis) have remained essentially constant.

24. A method in accordance with claim 6, wherein a fixed threshold value u_thresh is preset for said another threshold value criterion and the measured values are scaled such that the continuously updated maxima and minima u^max_emg and u^min_emg of the breathing activity signal remain within a preset, fixed range of values.

25. A method in accordance with claim 1, wherein the lung mechanical parameter resistance (R) is determined by means of occlusion methods during a passive or constant period of the breathing cycle.

26. A method in accordance with claim 1, wherein the lung mechanical parameter resistance (R) is determined by means of an end-expiratory occlusion.

27. A method in accordance with claim 1, wherein the lung mechanical parameter “

intrinsic PEEP”

(iPEEP) is determined by means of an end-expiratory occlusion.

28. A method in accordance with claim 6, wherein the lung mechanical parameter elastance (E) is determined by determining a respiratory time constant τ

during a passive period of the breathing cycle during inspiration or expiration according to E=R/τ

, wherein R is the resistance determined in advance.

29. A method in accordance with claim 5, wherein the lung mechanical parameter elastance (E) is determined by regression between the volume administered and the calculated alveolar pressure p_alv(t)=p_aw(t)−

R·

Flow(t) during a constant or passive period of inspiration or expiration.

30. A method in accordance with claim 1, wherein the lung mechanical parameters resistance (R), elastance (E) and intrinsic PEEP (iPEEP) are subjected each to a sliding averaging to determine time-based mean values <

R>

, <

E>

, <

iPEEP>

.

31. A method in accordance with claim 1, wherein an end-expiratory occlusion is used to determine the transformation rule.

32. A method in accordance with claim 31, wherein a p0.1 occlusion, corresponding to an occlusion for a time of 0.1 sec, is used as the end-expiratory occlusion.

33. A method in accordance with claim 3, wherein parameter k₁ is determined according to

34. A method in accordance with claim 3, wherein parameter k₂ is determined according to

35. A method in accordance with claim 3, wherein parameter k₃ is determined according to

36. A method in accordance with claim 3, wherein parameter k₄ is determined according to

37. A respirator comprising:

a ventilator for supplying breathing gas with an adjustable pressure;

electromyographic or mechanomyographic sensors for recording a breathing activity signal u_emg(t);

a measured value recording means for recording measured values for an airway pressure and volume flow Flow(t) and for determining a tidal volume Vol(t);

a control and analysis unit for;

determining a respiratory muscle pressure p_mus(t) using the signals determined for the breathing activity u_emg(t), airway pressure and volume flow by calculating p_mus(t) either (I) from measured values for the airway pressure and the volume flow Flow(t) as well as the patient'"'"'s lung mechanical parameters, or (II) by determining p_mus(t) by equating with the negative airway pressure −

p_occl(t) measured during an occlusion, or (III) by determining p_mus(t) by means of an esophageal catheter, which is equipped with pressure sensors for measuring an intrathoracic pressure p_es(t) and optionally an abdominal pressure p_abd(t), by equating with a transdiaphragmal pressure p_abd(t)−

p_es(t), wherein p_abd(t) can be optionally assumed to be constant;

transforming the breathing activity signal u_emg(t) by means of a preset transformation rule into a pressure signal p_emg(u_emg(t)), wherein the transformation rule is determined such that a mean deviation of the resulting transformed pressure signal p_emg(t) of the determined respiratory muscle signal p_mus(t) is minimized;

determining a respiratory effort pressure p_pat(t) as a weighted mean according to p_pat(t)=a·

p_mus(t)+(1−

a)·

pa_emg(t), wherein a is a parameter selected under the boundary condition 0≦

a≦

1;

calculating the airway pressure p_aw(t) to be delivered by said ventilator as a function of preselected degrees of Volume Assist (VA) for compensation of elastic recoil forces/resistances and Flow Assist (FA) for compensation of the resistive forces by sliding adaptation by