SYSTEM AND APPARATUS FOR THE NON-INVASIVE MEASUREMENT OF BLOOD PRESSURE

• US 20110196244A1
• Filed: 02/06/2009
• Published: 08/11/2011
• Est. Priority Date: 10/16/2008
• Status: Abandoned Application

• Current Assignee: Sabirmedical S L, Sabirmedical, S.L.
• Original Assignee: Sabirmedical, S.L.
• Inventors: García Llorente, Víctor Manuel, Ribas Ripoll, Vicente Jorge, Monte Moreno, Enrique
• Application Number:
  US13/123,896
• Publication Number:
  US 20110196244A1
• Time in Patent Office: Days
• US Class Current: 600/485
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/02116 of pulse wave amplitude A61B5/02225 takes precedence
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/7267 involving training the classification device

Abstract

The present invention relates to a system for the estimation of the systolic (SBP), diastolic (DBP) and average (MAP) blood pressure. Said system establishes a physiological model of the pulse wave combined with its energy for, afterwards, generating a fixed length vector containing the previous model'"'"'s values with other variables related to the user like, for example, age, sex, height, weight, etc. . . . This fixed length vector is used as an input of a function estimator system based on “random forests” for the calculation of the three variables of interest. The main advantage of this function estimator lies in that it does not impose any restriction beforehand over the function to be estimated, and it is also very reliable with heterogeneous data, as in the present invention'"'"'s case.

Claims

1. An apparatus for a non-invasive measurement of blood pressure comprising:

a stochastic model of the autoregressive mobile average type (ARMA) circulatory function on the input signal;

a stochastic model of the autoregressive mobile average type (ARMA) blood pressure pulse on the Teager-Kaiser operator of the input signal;

clinical data including one or more of sex, age, height, and body mass index and its functions; and

a function estimation system based on ‘

random forests’

.

2. An apparatus for a non-invasive measurement of blood pressure, according to claim 1, wherein an input of the function estimation system includes a vector of a fixed size including the previous models and information of the patient, wherein the information includes one or more of sex, age, height, weight, body mass index, pulse rhythm, cardiac coherence, zero-passes of the pre-processed input signal and variability of the zero-passes of the pre-processed input signal.

3. An apparatus for a non-invasive measurement of blood pressure, according to claim 1, wherein an input signal is a preprocessed plethysmographic wave optically, mechanically or acoustically obtained.

4. An apparatus for a non-invasive measurement of blood pressure, according to claim 3, wherein the plethysmographic wave has been obtained by means of a digital pulse oximeter.

5. An apparatus for a non-invasive measurement of blood pressure, according to claim 1, wherein the functions to be estimated by the function estimation system are one or more of the basic parameters SBP, DBP and MAP and linear combinations of these parameters to decrease an estimation error.

6. An apparatus for a non-invasive measurement of blood pressure, according to claim 5, which incorporates a post-processing system which performs an average of the function estimation system'"'"'s estimations to lower the systematic error and a variance of the one or more of the estimated parameters SBP, DBP and MAP.

7. An apparatus for a non-invasive measurement of blood pressure, according to claim 6, wherein the function estimation system for one or more of the SBP, DBP and MAP estimation is implemented by means of one or more DSP devices.

8. An apparatus for a non-invasive measurement of blood pressure, according to claim 1, further comprising a manual device which incorporates at least an acoustic, mechanic and/or optic catheter, comprising inside a data processing system including a CPU to reduce a variance of one or more of the estimated parameters SBP, DBP, MAP.

9. An apparatus for a non-invasive measurement of blood pressure, according to claim 8, wherein the CPU is implemented by one or more of DSP, FPGA or microcontroller devices.

10. An apparatus for a non-invasive measurement of blood pressure, according to claim 6, further comprising a storing memory, wherein the storing memory is one or more of a flash type memory other storing memory device.

11. An apparatus for a non-invasive measurement of blood pressure, according to claim 6, further comprising a exterior connection to a PC, wherein the PC connection is one or more of a serial port, Bluetooth or USB;

and a network connection, wherein the network connection is one or more of a WiFi, Zigbee or UWB.

12. An apparatus for a non-invasive measurement of blood pressure, according to claim 6, further comprising a data visualizing screen.

13. An apparatus for a non-invasive measurement of blood pressure, according to claim 6, further comprising one or more of control switches, batteries and connection to an external power source.

14. A computer-implemented, non-invasive method of measuring a blood pressure of a patient comprising:

receiving clinical parameters of the patient;

receiving an electronic photoplethysmography (PPG) signal captured from a measurement location of the patient;

extracting measurement parameters from the electronic PPG signal;

generating, by a processor, a fixed length vector based on the clinical parameters and the measurement parameters; and

performing, by a processor, a classification analysis using the fixed length vector as a seed vector; and

outputting the result of the classification analysis as an estimated blood pressure.

15. A computer-implemented method according to claim 14, wherein the classification analysis uses a random forests technique.

16. A computer-implemented method according to claim 14, wherein the classification analysis uses a support vector machine.

17. A computer-implemented method according to claim 14, further comprising training a classification analysis algorithm using a set of clinical parameters of a plurality of patients, a set of PPG signals from the plurality of patients, and a set of blood pressure values from the plurality of patients.

18. A computer-implemented method according to claim 17, wherein the classification analysis algorithm produces an estimated blood pressure without requiring calibration after the training is complete.

19. A computer-implemented method according to claim 14, wherein the clinical parameters include at least one of:

sex, age, weight, height, health information, food consumption, time of day, body mass index, and heart rate.

20. A computer-implemented method according to claim 14, wherein the measurement parameters include at least one of:

a shape of the PPG signal, a distance between pulses of the PPG signal, a variance of the PPG signal, an energy of the PPG signal, and a change in energy of the PPG signal.

21. A computer-implemented method according to claim 14, wherein the step of extracting utilizes a stochastic model of a physiology of a circulatory system.

22. A computer-implemented method according to claim 21, wherein the stochastic model is of the autoregressive moving average (ARMA) type.

23. A computer-implemented method according to claim 14, further comprising using an error estimation technique to finalize a value of the estimated blood pressure.

24. A computer readable storage medium having instructions stored thereon that, when executed by a processor, cause the processor to execute a method comprising:

receiving clinical parameters of the patient;

receiving a photoplethysmography (PPG) signal captured from a measurement location of the patient;

extracting measurement parameters from the PPG signals;

generating a fixed length vector based on the clinical parameters and the measurement parameters; and

performing a classification analysis using the fixed length vector as a seed vector; and

outputting the result of the classification analysis as an estimated blood pressure.

25. A computer readable storage medium according to claim 24, wherein the classification analysis uses a random forests technique.

26. A computer readable storage medium according to claim 24, wherein the classification analysis uses a support vector machine.

27. A computer readable storage medium according to claim 24, further comprising instructions that, when executed by the processor, cause the processor to train a classification analysis algorithm using a set of clinical parameters of a plurality of patients, a set of PPG signals from the plurality of patients, and a set of blood pressure values from the plurality of patients.

28. A computer readable storage medium according to claim 27, wherein the classification analysis algorithm produces an estimated blood pressure without requiring calibration after the training is complete.

29. A computer readable storage medium according to claim 24, wherein the clinical parameters include at least one of:

sex, age, weight, height, health information, food consumption, time of day, body mass index, and heart rate.

30. A computer readable storage medium according to claim 24, wherein the measurement parameters include at least one of:

a shape of the PPG signal, a distance between pulses of the PPG signal, a variance of the PPG signal, an energy of the PPG signal, and a change in energy of the PPG signal.

31. A computer readable storage medium according to claim 24, wherein extracting measurement parameters utilizes a stochastic model of a physiology of a circulatory system.

32. A computer readable storage medium according to claim 31, wherein the stochastic model is of the autoregressive moving average (ARMA) type.

33. A computer readable storage medium according to claim 24, wherein the instructions, when executed, further cause the processor to use an error estimation technique to finalize a value of the estimated blood pressure.

34. A non-invasive apparatus for measuring a blood pressure of a patient, comprising:

a processor; and

a computer readable storage medium coupled to the processor, wherein the storage medium includes instructions which, when executed by the processor, cause the processor to;

receive clinical parameters of the patient;

receive an electronic photoplethysmography (PPG) signal captured from a measurement location of the patient;

extract measurement parameters from the electronic PPG signal;

generate a fixed length vector based on the clinical parameters and the measurement parameters; and

perform a classification analysis using the fixed length vector as a seed vector; and

output the result of the classification analysis as an estimated blood pressure.

35. An apparatus according to claim 34, further comprising a plethysmographic blood pressure sensor configured to measure changes in a tissue volume in a location of a patient.

36. An apparatus according to claim 34, wherein the classification analysis uses a random forests technique.

37. An apparatus according to claim 34, wherein the classification analysis uses a support vector machine.

38. An apparatus according to claim 34, wherein the instructions, when executed by the processor, additionally cause the processor to train a classification analysis algorithm using a set of clinical parameters of a plurality of patients, a set of PPG signals from the plurality of patients, and a set of blood pressure values from the plurality of patients.

39. An apparatus according to claim 38, wherein the classification analysis algorithm produces an estimated blood pressure without requiring calibration after the training is complete.

40. An apparatus according to claim 34, wherein the clinical parameters include at least one of:

sex, age, weight, height, health information, food consumption, time of day, body mass index, and heart rate.

41. An apparatus according to claim 34, wherein the measurement parameters include at least one of:

a shape of the PPG signal, a distance between pulses of the PPG signal, a variance of the PPG signal, an energy of the PPG signal, and a change in energy of the PPG signal.

42. An apparatus according to claim 34, wherein the extracting of measurement parameters utilizes a stochastic model of a physiology of a circulatory system.

43. An apparatus according to claim 42, wherein the stochastic model is of the autoregressive moving average (ARMA) type.