Anesthesia and sedation monitoring system and method

• US 20040243017A1
• Filed: 02/03/2004
• Published: 12/02/2004
• Est. Priority Date: 05/06/2003
• Status: Abandoned Application

• Current Assignee: Everest Biomedical Instruments Co. (Stryker Corporation)
• Original Assignee: Everest Biomedical Instruments Co. (Stryker Corporation)
• Inventors: Causevic, Elvir
• Application Number:
  US10/485,750
• Publication Number:
  US 20040243017A1
• Time in Patent Office: Days
• US Class Current: 600/544
• CPC Class Codes: A61B 5/0836 Measuring rate of CO2 production
• CPC Class Codes: A61B 5/145 Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissuemeasuring of blood pressure or blood flow A61B5/02; non-radiation detecting or locating of foreign bodies in blood A61B5/06
• CPC Class Codes: A61B 5/1455 using optical sensors, e.g. spectral photometrical oximeters
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/372 Analysis of electroencephalograms
• CPC Class Codes: A61B 5/38 Acoustic or auditory stimuli
• CPC Class Codes: A61B 5/4821 Determining level or depth of anaesthesia A61B5/1106 takes precedence
• CPC Class Codes: A61B 5/6843 Monitoring or controlling sensor contact pressure
• CPC Class Codes: A61B 5/726 using Wavelet transforms

Abstract

A method for monitoring the depth of anesthesia experienced by a patient includes identifying a change in one or more evoked bio-potentials (100) using a wavelet transform, and calculating at least one index (108) indicative of the depth of anesthesia experienced by the patient based on the changes in the evoked bio-potentials (100) over a period of time during which anesthesia is administered to the patient. Optionally, changes in random electroencephalogram activity, pulse oximetry measurements, and blood gas measurements are combined with the changes in the evoked bio-potentials (100) in calculations of the index. The resulting indices are optionally displayed in a graphical representation (110) of the level of anesthesia experienced by the patient.

Claims

1. A method for determining the depth of anesthesia experienced by a patient, comprising the steps of:

stimulating the patient with a repetitive stimulus;

obtaining signal data representative of a series of evoked potential waveforms generated by the patient in response to said stimulus;

extracting at least one signal feature from said obtained signal data with at least one wavelet transform; and

calculating a representation of the depth of anesthesia experienced by the patient from said at least one extracted signal feature.

2. The method of claim 1 wherein said step of calculating said representation includes determining a change in said at least one extracted signal feature representative of said series of evoked potential waveforms over said period of time.

3. The method of claim 1 wherein said repetitive stimulus is an audio stimulus, and said of evoked potential waveforms are auditory evoked potential waveforms.

4. The method of claim 3 wherein said obtained signal data is representative of said series of auditory evoked potentials includes signal data representative of at least one auditory brainstem response;

and

wherein said step of calculating said representation includes determining a change in said auditory brainstem response over said period of time.

5. The method of claim 3 wherein said obtained signal data is representative of said series of auditory evoked potentials includes signal data representative of at least one auditory middle latency response;

and

wherein said step of calculating said representation includes determining a change in said auditory middle latency response over said period of time.

6. The method of claim 3 wherein said obtained signal data is representative of said series of auditory evoked potentials includes signal data representative of at least one auditory late response;

and

wherein said step of calculating said representation includes determining a change in said auditory late response over a period of time.

7. The method of claim 1 further including the step of obtaining additional signal data representative of random electroencephalogram activity in the patient over said period of time;

and

wherein said step of calculating a representation of the depth of anesthesia experienced by the patient from said obtained signal data further utilizes said obtained additional signal data.

8. The method of claim 7 wherein said signal data representative of said random electroencephalogram activity comprises a series of waveforms, and

wherein utilizing said obtained additional signal data includes:

(a) determining a change in said random electroencephalogram activity over said period of time; and

(b) denoising said random electroencephalogram activity signal data with at least one wavelet transform.

9. The method of claim 1 wherein said at least one wavelet transform comprises a discrete wavelet transform.

10. The method of claim 1 wherein said at least one wavelet transform comprises a continuous wavelet transform.

11. The method of claim 1 further including the step of:

obtaining at least one additional physiological measurement from the patient, said at least one additional physiological measurement selected from the set of blood gas measurements and breath gas measurements;

calculating a representation of the depth of anesthesia experienced by the patient from said at least one extracted signal feature and said at least one additional physiological measurement.

12. The method of claim 1 further including the step of denoising said obtained signal data with at least one wavelet transform prior to extracting at least one signal feature from said obtained signal data.

13. The method of claim 1 further including the step of selecting an orthogonal wavelet for said wavelet transform.

14. The method of claim 1 further including the step of selecting an bi-orthogonal wavelet for said wavelet transform.

15. The method of claim 1 wherein the step of extracting at least one signal feature from said obtained signal data with at least one wavelet transform includes calculating at least one wavelet coefficient.

16. A method for monitoring the depth of anesthesia in a patient, comprising the steps of:

obtaining signal data corresponding to a series of auditory evoked potentials in the patient over a period of time;

denoising said obtained signal data with at least one wavelet transform;

identifying at least one change in said obtained signal data over said period of time;

obtaining additional signal data corresponding to random electroencephalogram activity in the patent during said period of time;

identifying at least one change in said additional signal data over said period of time; and

calculating at least one index indicative of the depth of anesthesia experienced by the patient utilizing said identified changes in said signal data and said identified changes in said additional signal data over said period of time.

17. The method of claim 16 wherein said signal data includes data representative of a auditory brainstem response in the patient;

and

wherein the step of identifying at least one change in said signal data includes identifying a change in said data representative of said auditory brainstem response.

18. The method of claim 16 wherein said signal data includes data representative of a auditory middle latency response in the patient;

and

wherein the step of identifying at least one change in said signal data includes identifying a change in said data representative of said auditory middle latency response.

19. The method of claim 16 wherein said signal data includes data representative of a auditory late response in the patient;

and

wherein the step of identifying at least one change in said signal data includes identifying a change in said data representative of said auditory late response.

20. The method of claim 16 wherein said signal data includes signal data corresponding to an auditory brainstem response in the patient, signal data corresponding to an auditory middle latency response in the patient, and signal data corresponding to an auditory late response in the patient;

wherein the step of identifying at least one change in said signal data includes identifying a change in said auditory brainstem response signal data; and

wherein the step of calculating said at least one index includes utilizing said identified change in said auditory brainstem response signal data to calculate an index indicative of the depth of anesthesia in the brain of the patient.

21. The method of claim 20 wherein the step of identifying at least one change in said signal data includes identifying a change in said auditory middle latency response signal data;

and

wherein the step of calculating said at least one index includes utilizing said identified change in said auditory middle latency response signal data to calculate a second index indicative of the depth of anesthesia in the brain of the patient.

22. The method of claim 21 wherein the step of identifying at least one change in said signal data includes identifying a change in said auditory late response signal data;

and

wherein the step of calculating said at least one index includes utilizing said identified change in said auditory late response signal data to calculate a third index indicative of the depth of anesthesia in the brain of the patient.

23. The method of claim 22 further including the steps of:

providing a graphical representation of the depth of anesthesia experienced by the patient; and

mapping the values of the first, second, and third indices of the depth of anesthesia in the brain of the patient onto said graphical representation.

24. The method of claim 23 wherein the step of providing said graphic representation includes providing a graphical representation of a brain having at least a first region representative of a brainstem, at least a second region representative of a midbrain, and at least a third region representative of a cortex;

wherein a value representative of said first index is mapped onto said first region;

wherein a value representative of said second index is mapped onto said second region; and

wherein a value representative of said third index is mapped onto said third region.

25. The method of claim 24 wherein each of said mapped values is a visually distinct shade of gray.

26. The method of claim 24 wherein each of said mapped values is a visually distinct color.

27. The method of claim 16 wherein identifying at least one change in said signal data includes denoising said signal data with at least one wavelet transform.

28. The method of claim 27 wherein identifying at least one change in said signal data includes denoising said signal data with at least one discrete wavelet transform.

29. The method of claim 16 wherein identifying at least one change in said additional signal data includes denoising said additional signal data with at least one wavelet transform.

30. The method of claim 29 wherein identifying at least one change in said additional signal data includes denoising said additional signal data with at least one discrete wavelet transform.

31. The method of claim 16 wherein the step of calculating said at least one index indicative of the depth of anesthesia experienced by the patient includes analyzing said change in said signal data and analyzing said change in said additional signal data to obtain a single index indicative of the depth of experienced by the patient.

32. A method for monitoring neural activity in a patient, comprising the steps of monitoring one or more bio-potential signals evoked in the patient over a period of time, extracting features from said monitored bio-potential signals with at least one wavelet transform;

and observing changes in said features extracted from said bio-potential signals responsive to at east one repetitive stimulus over said period of time.

33. The method of claim 32 further including the step of denoising said monitored bio-potential signals with at least one wavelet transform prior to extracting said features.

34. The method of claim 32 wherein said repetitive stimulus is an auditory stimulus, and wherein said observed changes are changes in an auditory brainstem response.

35. The method of claim 34 wherein said observed changes further include changes in an auditory middle latency response.

36. The method of claim 34 wherein said observed changes further include changes in an auditory late response.

37. The method of claim 32 further including the step of monitoring random electroencephalogram activity of the patient for changes over said period of time.

38. The method of claim 32 further including the steps of:

obtaining at least one pulse oximetry measurement from the patient during said period of time; and

utilizing said changes in said bio-potential together with said at least one pulse oximetry measurement to generate a representation of the depth of anesthesia experienced by the patient.

39. The method of claim 32 further including the steps of:

obtaining at least one blood gas measurement from the patient during said period of time; and

utilizing said observed changes together with said at least one blood gas measurement to generate a representation of neural activity of the patient.

40. The method of claim 32 further including the steps of:

obtaining at least one breath gas measurement from the patient during said period of time; and

utilizing said observed changes together with said at least one breath gas measurement to generate a representation of neural activity of the patient.

41. The method of claim 40 wherein said at least one breath gas measurement is a CO₂ measurement.

42. The method of claim 32 further including the steps of:

obtaining at least one blood gas measurement from the patient during said period of time;

obtaining at least one breath gas measurement from the patient during said period of time; and

utilizing said observed changes together with said at least one breath gas measurement, and said at least one breath gas measurement, to generate a representation of neural activity of the patient.

43. The method of claim 32 further including the step of providing a graphical representation of the neural activity of the patient based on said observed changes.

44. The method of claim 43 wherein the step of providing said graphic representation includes providing a graphical representation of a brain having at least a first region representative of a brainstem, at least a second region representative of a midbrain, and at least a third region representative of a cortex;

and

mapping said observed changes to at least one associated region.

45. The method of claim 44 wherein each of said mapped observed changes are represented with a visually distinct shade of gray.

46. The method of claim 44 wherein each of said mapped observed changes are represented with a visually distinct color.