Enhancement of respiratory parameter estimation and asynchrony detection algorithms via the use of central venous pressure manometry

1. A respiratory monitoring apparatus comprising:

a central venous pressure (CVP) sensor configured to measure a central venous pressure signal of a patient when disposed adjacent to a heart of the patient;

at least one airway sensor configured to measure airway air flow as a function of time for the patient on a mechanical ventilator; and

at least one processor programmed to process the CVP signal to generate respiratory information for the patient by operations including:

segmenting the CVP signal based on detected breath intervals;

calculating a surrogate respiratory muscle pressure signal from the segmented CVP signal and airway air flow as a function of time; and

filtering the surrogate respiratory muscle pressure signal to remove a cardiac activity component of the surrogate respiratory muscle pressure signal.

2. The apparatus of claim 1 , wherein the generated respiratory information includes at least one of:

a surrogate intrapleural pressure signal determined from the segmented and filtered CVP signal, a surrogate respiratory muscle pressure waveform determined for each breath interval from the segmented and filtered CVP signal and a measured air flow signal integrated to determine an air volume signal.

3. The apparatus of claim 1 , further including:

an electrocardiogram (ECG) sensor configured to measure an ECG signal;

wherein the at least one processor is programmed to perform the filtering using the ECG signal received from the ECG sensor.

4. The apparatus of claim 1 , further comprising:

a mechanical ventilator configured to deliver mechanical ventilation to the patient;

wherein the at least one processor is further programmed to:

receive a start of inhalation mark as a function of time for each breath of the patient from the mechanical ventilator; and

determine breath intervals of the patient from the start of inhalation marks.

5. The apparatus of claim 1 , wherein the at least one processor is programmed to:

calculate the surrogate respiratory muscle pressure as a function of time from the equation:

P mus,surr ( t )=CVP( t )− E cw V air ( t )+ P 0 ;

where P mus,surr (t) denotes surrogate respiratory muscle pressure, CVP(t) denotes the CVP signal, E cw denotes a chest wall elastance, V air (t) denotes a lung air volume computed as an integral of the air flow as a function of time, and P 0 denotes a constant.

6. The apparatus of claim 1 , wherein the at least one processor is further programmed to:

detect a plurality of peaks in the segmented and filtered surrogate muscle pressure signal; and

generate the respiratory information including a respiratory muscle pressure waveform comprising respiratory muscle pressure waveform segments over time intervals delineated by the peaks.

7. The apparatus of claim 6 , wherein the at least one processor is further programmed to:

update settings of a constrained optimization algorithm and/or a parametric optimization algorithm of the mechanical ventilator based on the peak-detected muscle pressure signal.

8. A mechanical ventilation apparatus, comprising:

a mechanical ventilator;

at least one airway sensor configured to measure airway pressure and air flow as functions of time for the patient on the mechanical ventilator;

at least one processor programmed to:

receive a central venous pressure signal from a central venous pressure sensor;

receive the air flow signal as a function of time for the patient from the at least one airway sensor;

calculate a respiratory muscle pressure signal as a function of time for each breath of the patient from the central venous pressure signal as a function of time and the air flow signal as a function of time;

extract at least one respiratory characteristic from the respiratory muscle pressure signal by operations including:

filtering data indicative of cardiac activity of the patient from the respiratory muscle pressure signal;

determining a shape of the respiratory muscle pressure signal; and

updating settings of a constrained optimization algorithm and/or a parametric optimization algorithm of the mechanical ventilator based on the shape-detected signal.

9. The apparatus of claim 8 , further including:

an electrocardiogram (ECG) sensor configured to measure data indicative of cardiac activity of the patient;

wherein the at least one processor is further programmed to filter data indicative of cardiac activity of the patient from the respiratory muscle pressure signal using the cardiac data received from the ECG sensor.

10. The apparatus of claim 8 , wherein the at least one processor is programmed to:

receive a start of inhalation mark as a function of time for each breath of the patient and a start of exhalation mark as a function of time for each breath of the patient from the mechanical ventilator; and

determine a duration of each breath of the patient as a function of time from the start of inhalation and the start of exhalation marks by segmenting the received marks and the air flow signal as a function of time to determine each breath of the patient.

11. The apparatus of claim 8 , wherein the at least one processor is programmed to:

calculate the respiratory muscle pressure signal as a function of time from the received central venous pressure and air volume signals by operations including computing a difference between the central venous pressure signal and a function of the air volume signal.

12. The apparatus of claim 8 , wherein the at least one processor is programmed to:

calculate the respiratory muscle pressure signal as a function of time from the equation:

P mus,surr ( t )=CVP( t )− E cw V air ( t )+ P 0 ;

wherein P mus,surr (t) is the respiratory muscle pressure signal, CVP corresponds to central venous pressure, E cw corresponds to elastance of a chest wall, V air (t) denotes a lung air volume computed as an integral of the air flow as a function of time, and P 0 corresponds to a constant;

display, on a display, the calculated of respiratory muscle pressure signal.

13. The apparatus of claim 8 , wherein the at least one processor is programmed to:

extract a plurality of peaks in the filtered surrogate muscle pressure signal, the peaks corresponding to a shape of the filtered signal.

14. The apparatus of claim 8 , further including:

a classification processor programmed to classify an asynchrony of the extracted signal according to at least one of:

no asynchrony;

ineffective triggering;

auto-triggering;

delayed cycling off;

early cycling off; and

double triggering.

15. The apparatus of claim 14 , wherein the at least one processor is programmed to at least one of:

suggest a recommendation for a medical professional to adjust the settings of the mechanical ventilator to correct the asynchrony, wherein the recommendation is displayed on the display; and

automatically adjust the settings of the mechanical ventilator to correct the asynchrony.

16. A non-transitory storage medium storing instructions readable and executable by one or more microprocessors to perform a method of monitoring breathing patterns of a patient, the method comprising:

receiving a central venous pressure value from a central venous pressure sensor;

receiving values of at least one of airway pressure and air flow as a function of time for the patient from at least one airway sensor;

segmenting the received values to determine each breath of the patient;

calculate a surrogate signal of respiratory muscle pressure as a function of time generated by the patient on the mechanical ventilator from the equation:

P mus,surr ( t )=CVP( t )− E cw V air ( t )+ P 0 ;

wherein P mus,surr (t) corresponds to surrogate respiratory muscle pressure, CVP corresponds to central venous pressure, E cw corresponds to elastance of a chest wall, V air denotes a lung air volume computed as an integral of the air flow as a function of time, and P 0 corresponds to a constant;

filtering data indicative of cardiac activity of the patient from the surrogate muscle pressure signal using the cardiac data received from an ECG sensor;

extracting a plurality of peaks in the filtered signal corresponding to a shape of the filtered signal; and

updating settings of a constrained optimization algorithm and/or a parametric optimization algorithm of the mechanical ventilator based on the peak-detected signal.

17. The non-transitory storage medium of claim 16 , wherein the at least one microprocessor is further programmed to:

receive a start of inhalation mark as a function of time for each breath of the patient and a start of exhalation mark as a function of time for each breath of the patient from the mechanical ventilator; and

determine a duration of each breath of the patient as a function of time from the start of inhalation and the start of exhalation marks.

18. The non-transitory storage medium of claim 16 , wherein the at least one microprocessor is further programmed to:

display, on a display, the calculated value of respiratory muscle pressure.

19. The non-transitory storage medium of claim 16 , wherein the at least one microprocessor is further programmed to:

classify an asynchrony of the extracted signal according to at least one of:

no asynchrony;

ineffective triggering;

auto-triggering;

delayed cycling off;

early cycling off; and

double triggering;

and

at least one of:

suggest a recommendation for a medical professional to adjust the settings of the mechanical ventilator to correct the asynchrony, wherein the recommendation is displayed on the display; and

automatically adjust the settings of the mechanical ventilator to correct the asynchrony.