Respiration monitoring system based on sensed physiological parameters

• US 6,350,242 B1
• Filed: 03/31/1998
• Issued: 02/26/2002
• Est. Priority Date: 09/28/1995
• Status: Expired due to Term

• Current Assignee: Data Sciences U K Limited (International Business Machines Corporation)
• Original Assignee: Data Sciences U K Limited (International Business Machines Corporation)
• Inventors: Brockway, Brian P., Brockway, Robert V., Doten, Gregory P., Fundakowski, Richard A.
• Primary Examiner(s): Nasser, Robert L.
• Application Number:
  US09/052,509
• Time in Patent Office: 1,428 Days
• Field of Search: 600/484, 600/485, 600/486, 600/301, 600/481, 600/561, 600/504, 600/505, 600/500, 600/529
• US Class Current: 600/484
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/02108 from analysis of pulse wave characteristics
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00

Abstract

A system and method obtains a respiration parameter of an animal or human, such as respiratory rate, from a blood pressure, volumetric blood flow, blood velocity or stroke volume signal from a sensor for the animal or human. Specifically, the signal is processed to develop an amplitude versus time waveform. A sequence of selected features derived from individual cardiac cycles of the amplitude versus time waveform over a selected time interval are extracted from the developed amplitude versus time waveform. A mathematical model is fitted to the extracted sequence of selected features to yield a fitted mathematical model. The physiologic parameter information is computed from the fitted mathematical model.

Claims

1. A method of obtaining respiratory parameter information of an animal or human from a signal indicative of sensed variations in blood flow data of the animal or human, the method comprising:

generating a signal that represents the blood flow data;

extracting from the signal a sequence of selected features of the blood flow data over a selected time interval;

fitting a mathematical model to the extracted sequence of selected features to yield a fitted mathematical model; and

computing the respiratory parameter information from the fitted mathematical model.

2. The method of claim 1, and further comprising processing the signal to develop an amplitude versus time waveform prior to extracting the sequence of selected features.

3. The method of claim 1, wherein generating a signal comprises generating a signal that represents blood velocity data of the animal or human.

4. The method of claim 1, wherein the selected blood flow features are selected from a group of blood flow features including peak flow, minimum flow, mean flow and stroke volume for each cardiac cycle.

5. The method of claim 1, and further comprising:

extracting from a blood pressure signal a sequence of selected blood pressure features derived from individual cardiac cycles of an amplitude versus time waveform over a selected time interval;

fitting a mathematical model to the extracted sequence of selected blood pressure features to yield a fitted mathematical model;

computing the respiratory parameter information from the fitted mathematical model; and

correlating the respiratory parameter information computed from the blood pressure features with the respiratory parameter information computed from the blood flow features.

6. The method of claim 1, wherein generating a signal comprises generating a signal that represents the volumetric blood flow of the animal or human.

7. The method of claim 1, wherein the fitted mathematical model comprises curve fit data values substantially equal-spaced in time.

8. The method of claim 1, wherein computing the respiratory parameter information comprises:

testing the fitted mathematical model for critical points in accordance with a predetermined criteria;

determining from the critical points whether the critical points are a maximum or a minimum; and

computing the respiratory parameter information from successive maximum and minimum critical points.

9. The method of claim 8, wherein testing the fitted mathematical model for critical points includes locating peak values and zero-crossings of the fitted mathematical model.

10. The method of claim 1, and further comprising:

sensing variations in the blood flow data from the animal or human and providing a signal indicative of the sensed variations in the blood flow data;

transmitting the signal; and

receiving, external to the animal or human, the transmitted signal.

11. The method of claim 1, wherein computing the respiratory parameter information comprises performing spectral analysis on the fitted mathematical model.

12. An apparatus that obtains respiratory parameter information of an animal or human from a signal indicative of sensed variations in blood flow data of the animal or human, the apparatus comprising:

a sensor that generates a signal that represents the blood flow data; and

a data processing apparatus, communicatively coupled to the sensor, that extracts from the signal a sequence of selected features of the blood flow data over a selected time interval, fits a mathematical model to the extracted sequence of selected features to yield a fitted mathematical model, and computes the respiratory parameter information from the fitted mathematical model.

13. The apparatus of claim 12, wherein the sensor comprises a blood flow sensor that uses Doppler ultrasound, transit time ultrasound or laser Doppler to generate the signal.

14. The apparatus of claim 12, wherein the data processing apparatus further generates a stroke volume waveform from the output of the sensor.

15. The apparatus of claim 12, and further comprising a transmitter coupled to the sensor and a receiver coupled to the data processing apparatus so as to communicatively couple the sensor to the data processing device.

16. A method of obtaining respiratory parameter information of an animal or human, from a sequence of stroke volume features of the animal or human, the method comprising:

fitting a mathematical model of the sequence of features to yield a fitted mathematical model; and

computing the respiratory parameter information from the fitted mathematical model.

17. The method of claim 16, wherein the stroke volume features are measured directly with a stroke volume sensor.

18. A method of obtaining respiratory parameter information of an animal or human from a signal indicative of sensed variations in blood flow data of the animal or human, the method comprising:

generating a signal that represents the blood flow data;

extracting from the signal a time-domain sequence of selected features of the blood flow data over a selected time interval;

curve-fitting a polynomial mathematical model to the extracted sequence of selected features to yield a time-domain fitted mathematical model; and

computing the respiratory parameter information from the fitted mathematical model.

19. The method of claim 18, wherein generating a signal comprises generating a signal that represents blood velocity data of the animal or human.

20. The method of claim 18, wherein the selected blood flow features are selected from a group of blood flow features including peak flow, minimum flow, mean flow and stroke volume for each cardiac cycle.

21. The method of claim 18, and further comprising:

extracting from a blood pressure signal a time-domain sequence of selected blood pressure features derived from individual cardiac cycles of an amplitude versus time waveform over a selected time interval;

fitting a mathematical model to the extracted sequence of selected blood pressure features to yield a time-domain fitted mathematical model;

computing the respiratory parameter information from the fitted mathematical model; and

correlating the respiratory parameter information computed from the blood pressure features with the respiratory parameter information computed from the blood flow features.

22. The method of claim 18, wherein the fitted mathematical model comprises curve fit data values substantially equal-spaced in time.

23. The method of claim 18, wherein computing the respiratory parameter information comprises:

testing the fitted mathematical model for critical points in accordance with a predetermined criteria;

determining from the critical points whether the critical points are a maximum or a minimum; and

computing the respiratory parameter information from successive maximum and minimum critical points.

24. The method of claim 23, wherein testing the fitted mathematical model for critical points includes locating peak values and zero-crossings of the fitted mathematical model.

25. The method of claim 18, and further comprising:

sensing variations in the blood flow data from the animal or human and providing a signal indicative of the sensed variations in the blood flow data;

transmitting the signal; and

receiving, external to the animal or human, the transmitted signal.

26. The method of claim 18, wherein computing the respiratory parameter information comprises performing spectral analysis on the fitted mathematical model.

27. An apparatus that obtains respiratory parameter information of an animal or human from a signal indicative of sensed variations in blood flow data of the animal or human, the apparatus comprising:

a sensor that generates a signal that represents the blood flow data; and

a data processing apparatus, communicatively coupled to the sensor, that extracts from the signal a time-domain sequence of selected features of the blood flow data over a selected time interval, curve-fits a polynomial mathematical model to the extracted sequence of selected features to yield a time-domain fitted mathematical model, and computes the respiratory parameter information from the fitted mathematical model.

28. The apparatus of claim 27, wherein the sensor comprises a blood flow sensor that uses Doppler ultrasound, transit time ultrasound or laser Doppler to generate the signal.

29. The apparatus of claim 27, wherein the data processing apparatus further generates a stroke volume waveform from the output of the sensor.

30. The apparatus of claim 27, and further comprising a transmitter coupled to the sensor and a receiver coupled to the data processing apparatus so as to communicatively couple the sensor to the data processing device.