OPTIC FUNCTION MONITORING PROCESS AND APPARATUS

• US 20100056935A1
• Filed: 07/09/2009
• Published: 03/04/2010
• Est. Priority Date: 07/09/2008
• Status: Active Grant

• Current Assignee: Donald Bernstein, Laurence M. Mckinley, Ricardo Bravo
• Original Assignee: Donald Bernstein, Laurence M. Mckinley, Ricardo Bravo
• Inventors: Bravo, Ricardo, Bernstein, Donald, McKinley, Laurence M.
• Granted Patent: US 8,862,217 B2
• Time in Patent Office: Days
• US Class Current: 600/504
• CPC Class Codes: A61B 3/0008 provided with illuminating means A61B3/117 takes precedence; goniolenses used for laser treatment A61F9/009; illuminating means for optical instruments G02B27/00
• CPC Class Codes: A61B 3/0025 characterised by electronic signal processing, e.g. eye models
• CPC Class Codes: A61B 3/113 for determining or recording eye movement
• CPC Class Codes: A61B 3/1233 for measuring blood flow, e.g. at the retina
• CPC Class Codes: A61B 3/1241 specially adapted for observation of ocular blood flow, e.g. by fluorescein angiography
• CPC Class Codes: A61B 3/16 for measuring intraocular pressure, e.g. tonometers
• CPC Class Codes: A61B 3/18 Arrangement of plural eye-testing or -examining apparatus
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/24 Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
• CPC Class Codes: A61B 5/378 Visual stimuli
• CPC Class Codes: A61B 5/388 Nerve conduction study, e.g. detecting action potential of peripheral nerves
• CPC Class Codes: A61B 5/4041 Evaluating nerves condition
• CPC Class Codes: A61B 5/4047 afferent nerves, i.e. nerves that relay impulses to the central nervous system
• CPC Class Codes: A61B 5/4076 Diagnosing or monitoring particular conditions of the nervous system A61B5/4821, A61B5/4824 take precedence
• CPC Class Codes: A61B 5/412 Detecting or monitoring sepsis
• CPC Class Codes: A61B 5/4821 Determining level or depth of anaesthesia A61B5/1106 takes precedence
• CPC Class Codes: A61B 5/6803 Head-worn items, e.g. helmets, masks, headphones or goggles
• CPC Class Codes: A61B 5/6843 Monitoring or controlling sensor contact pressure
• CPC Class Codes: A61B 5/6844 Monitoring or controlling distance between sensor and tissue
• CPC Class Codes: A61B 5/7225 Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation A61B5/30, A61B5/316 take precedence; transformers for specific applications H01F38/00; amplifiers H03F
• CPC Class Codes: A61B 5/7282 : Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence

Abstract

A method and apparatus for monitoring optic function is provided. The apparatus and method relies on two principle modes of measuring the function of the optic nerve, namely, monitoring VEPs for neural function, and monitoring at least one additional parameter of optic function such as intraocular pressure, blood flow or location of the eye to provide a multi-variable optic function monitor. The method and apparatus is proposed for the use to diagnose and potentially prevent the incidence of POVL and anaesthesia awareness in patients during medical procedures.

Claims

1. A method of monitoring optic function comprising:

stimulating and monitoring the function of the eye by producing and measuring a visual evoke potential in at least one portion of the eye selected from the group consisting of the optic nerve, optic chiasma and the optic cortex;

monitoring the intraocular pressure of the eye;

monitoring one of either the retinal or the optic blood flow;

evaluating the visual evoked potential, the intraocular pressure and the retinal or optic blood flow for an adverse change in optic function to provide a real-time gauge of the health of the optic nerve.

2. The method of claim 1, wherein the stimulating and monitoring of the function of at least one portion of the eye includes producing a visual evoked potential in at least one of the nasal or temporal halves of the optic nerve.

3. The method of claim 1, wherein the stimulating and monitoring the function of the eye further includes positioning at least one optic function sensor proximate to the eye, said at least one optic function sensor being designed to stimulate the eye.

4. The method of claim 3, wherein the at least one optic function sense is a light emitting diode.

5. The method of claim 1, wherein the monitoring of the intraocular pressure of the eye further includes placing a pressure sensor proximate to the eye.

6. The method of claim 5, wherein the pressure sensor is a tonometer.

7. The method of claim 1, wherein the monitoring one of either the retinal or the optic blood flow further includes placing a blood flow sensor proximate to the eye.

8. The method of claim 7, wherein the blood flow sensor is selected from one of either a near-infrared spectrometer or a laser Doppler velocimeter.

9. The method of claim 1, further comprising placing a location sensor proximate to said eye;

and

monitoring said location sensor to determine the placement of said eye in relation to the eye socket.

10. The method of claim 9, wherein said location sensor further comprises a pressure transducer such that the location of the eye may be adjusted based on an output from the location sensor.

11. The method of claim 1, wherein the adverse change is one of either POVL or anesthesia awareness.

12. The method of claim 1, wherein the evaluation includes producing a data plot output, said data plot being indicative of at least the level of the visual evoked potential (VEP).

13. The method of claim 12, further comprising computing an anesthesia evaluation number (AI) from the VEP data plot in accordance with the equation, AI=P2/TL1, where P2 is the vertical distance between the maximum value of the upslope of a second and largest maximum visual evoked potential (EVP2) measured by the data plot to the nadir (N3) of the EVP2, and TL1 is the temporal interval between the onset of stimulation (t₀) and the absolute magnitude of the second evoked potential (EVP2).

14. The method of claim 13, further comprising computing a second anesthesia evaluation number (AI-2) according to the equation AI-2=(AI)ⁿ, wherein n is an exponent between 0.333 and 3.

15. The method of claim 12, further comprising computing an anesthesia evaluation number (AI) from the VEP data plot in accordance with the equation, AI=(P2)^x/(TL1)^y, where P2 is the vertical distance between the maximum value of the upslope of a second and largest maximum visual evoked potential (EVP2) measured by the data plot to the nadir (N3) of the EVP2, and TL1 is the temporal interval between the onset of stimulation (t₀) and the absolute magnitude of the EVP2, and wherein x and y are an exponents between 0.333 and 3.

16. The method of claim 15, further comprising computing a second anesthesia evaluation number (AI-2) according to the equation AI-2=(P2)^x/(TL1)^y, wherein x and y are an exponents between 0.333 and 3.

17. The method of claim 16, further comprising computing a third anesthesia evaluation number (AI-3) according to the equation AI-3=(AI-2)ⁿ where n is an exponent between 0.333 and 3.

18. The method of claim 12, further comprising computing an anesthesia evaluation number (AI) from the VEP data plot in accordance with the equation, AI=(δ

(EVP2)/δ

t_max)/TL1, where (EVP2) is a second and largest maximum visual evoked potential, δ

(EVP2)/δ

t_max is the peak first time-derivative (δ

/δ

t) of EVP2, and TL1 is the temporal interval between the onset of stimulation (t₀) and the absolute magnitude of EVP2.

19. The method of claim 12, further comprising computing an anesthesia evaluation number (AI) from the VEP data plot in accordance with the equation, AI=(δ

(EVP2)/δ

t_max)^x/(TL1)^y, where (EVP2) is a second and largest maximum visual evoked potential, δ

(EVP2)/δ

t_max is the peak first time-derivative (δ

/δ

t) of EVP2, TL1 is the temporal interval between the onset of stimulation (t₀) and the absolute magnitude of EVP2, and wherein x and y are an exponents between 0.333 and 3.

20. The method of claim 19, further comprising computing a second anesthesia evaluation number (AI-2) according to the equation AI-2=(AI)ⁿ where n is an exponent between 0.333 and 3.

21. The method of claim 12, further comprising computing an anesthesia evaluation number (AI) from the VEP data plot in accordance with the equation, AI=(δ

(EVP2)/δ

t_mean)/TL1, where (EVP2) is a second and largest maximum visual evoked potential, δ

(EVP2)/δ

t_mean is the quotient of P2 and the temporal interval occurring between the onset of the upslope of EVP2 and P2, and TL1 is the temporal interval between the onset of stimulation (t₀) and the absolute magnitude of EVP2; and

where P2 is the vertical distance between the maximum value of the upslope of a second and largest maximum visual evoked potential (EVP2) measured by the data plot to the nadir (N3) of the EVP2.

22. The method of claim 12, further comprising computing an anesthesia evaluation number (AI) from the VEP data plot in accordance with the equation, AI=(δ

(EVP2)/δ

t_mean)^x/(TL1)^y, where (EVP2) is a second and largest maximum visual evoked potential, δ

(EVP2)/δ

t_mean is the quotient of P2 and the temporal interval occurring between the onset of the upslope of EVP2 and P2, TL1 is the temporal interval between the onset of stimulation (t₀) and the absolute magnitude of EVP2, and wherein x and y are an exponents between 0.333 and 3; and

where P2 is the vertical distance between the maximum value of the upslope of a second and largest maximum visual evoked potential (EVP2) measured by the data plot to the nadir (N3) of the EVP2.

23. The method of claim 12, further comprising computing an anesthesia evaluation number (AI) from the VEP data plot in accordance with the equation, AI=(δ

(EVP2)/δ

t_mean)^x/(t₁^y/P2^z)^m in (μ

V/ms), where (EVP2) is a second and largest maximum visual evoked potential, δ

(EVP2)/δ

t_mean is the quotient of P2 and the temporal interval occurring between the onset of the upslope of EVP2 and P2, t₁ is the absolute magnitude of EVP2, P2 is the vertical distance between the maximum value of the upslope of a second and largest maximum visual evoked potential (EVP2) measured by the data plot to the nadir (N3) of the EVP2, and wherein x and y are an exponents between 0.333 and 3.

24. The method of claim 23, further comprising computing a second anesthesia evaluation number (AI-2) according to the equation AI-2=(AI)ⁿ where n is an exponent between 0.333 and 3.

25. The method of claim 12, further comprising computing an anesthesia evaluation number (AI) from the VEP data plot in accordance with the equation, AI=δ

(EVP2)/δ

t_max/t₁/P2 in (μ

V/ms), where (EVP2) is a second and largest maximum visual evoked potential, δ

(EVP2)/δ

t_max is the peak first time-derivative (δ

/δ

t) of EVP2, t₁ is the absolute magnitude of EVP2, and P2 is the vertical distance between the maximum value of the upslope of a second and largest maximum visual evoked potential (EVP2) measured by the data plot to the nadir (N3) of the EVP2.

26. The method of claim 12, further comprising computing an anesthesia evaluation number (AI) from the VEP data plot in accordance with the equation, AI=δ

(EVP2)/δ

t_max/t₁/P2 in (μ

V/ms), where (EVP2) is a second and largest maximum visual evoked potential, t_max δ

(EVP2)/δ

t_max is the peak first time-derivative (δ

/δ

t) of EVP2, t₁ is the absolute magnitude of EVP2, P2 is the vertical distance between the maximum value of the upslope of a second and largest maximum visual evoked potential (EVP2) measured by the data plot to the nadir (N3) of the EVP2, and wherein x, y and z are an exponents between 0.333 and 3.

27. The method of claim 12, further comprising computing an anesthesia evaluation number (AI) from the VEP data plot in accordance with the equation, AI=(δ

(EVP2)/δ

t_mean)^x/(t₁^y/P2^z)^m in (μ

V/ms), where (EVP2) is a second and largest maximum visual evoked potential, δ

(EVP2)/δ

t_mean is the quotient of P2 and the temporal interval occurring between the onset of the upslope of EVP2 and P2, t₁ is the absolute magnitude of EVP2, P2 is the vertical distance between the maximum value of the upslope of a second and largest maximum visual evoked potential (EVP2) measured by the data plot to the nadir (N3) of the EVP2, and wherein x, y, z and m are an exponents between 0.333 and 3.

28. The method of claim 27, further comprising computing a second anesthesia evaluation number (AI-2) according to the equation AI-2=(AI)ⁿ where n is an exponent between 0.333 and 3.

29. The method of claim 12, further comprising computing an anesthesia evaluation number (AI) from the VEP data plot in accordance with the equation, AI=(δ

(EVP2)/δ

t_max)^x/(t₁^y/P2^z)^m in (μ

V/ms), where (EVP2) is a second and largest maximum visual evoked potential, δ

(EVP2)/δ

t_max is the peak first time-derivative (δ

/δ

t) of EVP2, t₁ is the absolute magnitude of EVP2), P2 is the vertical distance between the maximum value of the upslope of a second and largest maximum visual evoked potential (EVP2) measured by the data plot to the nadir (N3) of the EVP2, and wherein x, y, z and m are an exponents between 0.333 and 3.

30. The method of claim 29, further comprising computing a second anesthesia evaluation number (AI-2) according to the equation AI-2=(AI)ⁿ where n is an exponent between 0.333 and 3.

31. The method of claims 13, 15, 18, 19, 21 to 23, 25 to 27 and 29, further comprising computing a dimensionless anesthesia evaluation index (AI-D) according to the equation, AI-D=(AI)/CV(AI), where CV is a static control value defined as the value of AI measured at a time before the induction of anesthesia.

32. The method of claims 14, 16, 20, 24, 28 and 30, further comprising computing a dimensionless anesthesia evaluation index (AI-D) according to the equation, AI-D=(AI-2)/CV(AI-2), where CV is a static control value defined as the value of AI-2 measured at a time before the induction of anesthesia.

33. The method of claim 17, further comprising computing a dimensionless anesthesia evaluation index (AI-D) according to the equation, AI-D=(AI-3)/CV(AI-3), where CV is a static control value defined as the value of AI-3 measured at a time before the induction of anesthesia.

34. The method of claims 13 to 30, further comprising calibrating the anesthesia evaluation number by measuring the anesthesia evaluation number prior to the administration of an anesthesia.

35. An optic monitor comprising:

at least one sensor band having an inner surface designed to be securedly attached to the outer surface of the eyelid;

at least one optic function sensor positioned on the inner surface of the sensor band, said at least one optic function sensor being designed to stimulate and monitor the function of at least one portion of the eye selected from the group consisting of the optic nerve, the optic chiasm and the optic cortex, and output an optic function signal;

a pressure sensor positioned on the inner surface of the sensor band, said pressure sensor being designed to monitor the intraocular pressure of the eye and output a pressure signal;

a blood flow sensor positioned on the inner surface of the sensor band, said blood flow sensor being designed to monitor one of either retinal or optic blood flow and output a blood flow signal; and

a monitor in signal communication with the at least one sensor band for recording and communicating the output signals to a user.

36. The device of claim 35, wherein the at least one optic function sensor stimulates the eye by producing a visual evoked potential in at least one of the nasal or temporal halves of the optic nerve.

37. The device of claim 36, wherein the at least one optic function sensor is a light emitting diode.

38. The device of claim 35, wherein the pressure sensor is a tonometer.

39. The device of claim 35, wherein the blood flow sensor is selected from one of either a near-infrared spectrometer or a laser Doppler velocimeter.

40. The device of claim 35, further comprising a location sensor being designed to monitor the placement of said eye in relation to the eye socket and output an eye location signal.

41. The device of claim 40, wherein the location sensor is a pressure transducer.

42. The device of claim 35, further comprising a plurality of preprogrammed thresholds for each of the output signals such that the device gives an automated warning should the preprogrammed thresholds be reached.

43. The device of claim 35, wherein the sensor band is incorporated into an eye-cover.

44. The device of claim 43, wherein the eye-cover is a pair of goggles.

45. The method of claim 35, wherein the output is a visual evoked potential (VEP) data plot indicative of at least the level of visual evoked potential.

46. The method of claim 45, further comprising computing an anesthesia evaluation number (AI) from the VEP data plot in accordance with the equation, AI=P2/TL1, where P2 is the vertical distance between the maximum value of the upslope of a second and largest maximum visual evoked potential (EVP2) measured by the data plot to the nadir (N3) of the EVP2, and TL1 is the temporal interval between the onset of stimulation (t₀) and the absolute magnitude of the second evoked potential (EVP2).

47. The method of claim 46, further comprising computing a second anesthesia evaluation number (AI-2) according to the equation AI-2=(AI)ⁿ, wherein n is an exponent between 0.333 and 3.

48. The method of claim 45, further comprising computing an anesthesia evaluation number (AI) from the VEP data plot in accordance with the equation, AI=(P2)^x/(TL1)^y, where P2 is the vertical distance between the maximum value of the upslope of a second and largest maximum visual evoked potential (EVP2) measured by the data plot to the nadir (N3) of the EVP2, and TL1 is the temporal interval between the onset of stimulation (t₀) and the absolute magnitude of the EVP2, and wherein x and y are an exponents between 0.333 and 3.

49. The method of claim 48, further comprising computing a second anesthesia evaluation number (AI-2) according to the equation AI-2=(P2)^x/(TL1)^y, wherein x and y are an exponents between 0.333 and 3.

50. The method of claim 49, further comprising computing a third anesthesia evaluation number (AI-3) according to the equation AI-3=(AI-2)ⁿ where n is an exponent between 0.333 and 3.

51. The method of claim 45, further comprising computing an anesthesia evaluation number (AI) from the VEP data plot in accordance with the equation, AI=(δ

(EVP2)/δ

t_max)/TL1, where (EVP2) is a second and largest maximum visual evoked potential, δ

(EVP2)/δ

t_max is the peak first time-derivative (δ

/δ

t) of EVP2, and TL1 is the temporal interval between the onset of stimulation (t₀) and the absolute magnitude of EVP2.

52. The method of claim 45, further comprising computing an anesthesia evaluation number (AI) from the VEP data plot in accordance with the equation, AI=(δ

(EVP2)/δ

t_max)^x/(TL1)^y, where (EVP2) is a second and largest maximum visual evoked potential, δ

(EVP2)/δ

t_max is the peak first time-derivative (δ

/δ

t) of EVP2, TL1 is the temporal interval between the onset of stimulation (t₀) and the absolute magnitude of EVP2, and wherein x and y are an exponents between 0.333 and 3.

53. The method of claim 52, further comprising computing a second anesthesia evaluation number (AI-2) according to the equation AI-2=(AI)ⁿ where n is an exponent between 0.333 and 3.

54. The method of claim 45, further comprising computing an anesthesia evaluation number (AI) from the VEP data plot in accordance with the equation, AI=(δ

(EVP2)/δ

t_mean)/TL1, where (EVP2) is a second and largest maximum visual evoked potential, δ

(EVP2)/δ

t_mean is the quotient of P2 and the temporal interval occurring between the onset of the upslope of EVP2 and P2, and TL1 is the temporal interval between the onset of stimulation (t₀) and the absolute magnitude of EVP2; and

where P2 is the vertical distance between the maximum value of the upslope of a second and largest maximum visual evoked potential (EVP2) measured by the data plot to the nadir (N3) of the EVP2.

55. The method of claim 45, further comprising computing an anesthesia evaluation number (AI) from the VEP data plot in accordance with the equation, AI=(δ

(EVP2)/δ

t_mean)^x/(TL1)^y, where (EVP2) is a second and largest maximum visual evoked potential, δ

(EVP2)/δ

t_mean is the quotient of P2 and the temporal interval occurring between the onset of the upslope of EVP2 and P2, TL1 is defined as the temporal interval between the onset of stimulation (t₀) and the absolute magnitude of EVP2, and wherein x and y are an exponents between 0.333 and 3; and

where P2 is the vertical distance between the maximum value of the upslope of a second and largest maximum visual evoked potential (EVP2) measured by the data plot to the nadir (N3) of the EVP2.

56. The method of claim 45, further comprising computing an anesthesia evaluation number (AI) from the VEP data plot in accordance with the equation, AI=(δ

(EVP2)/δ

t_mean)^x/(t₁^y/P2^z)^m in (μ

V/ms), where (EVP2) is a second and largest maximum visual evoked potential, δ

(EVP2)/δ

t_mean is the quotient of P2 and the temporal interval occurring between the onset of the upslope of EVP2 and P2, t₁ is the absolute magnitude of EVP2, P2 is the vertical distance between the maximum value of the upslope of a second and largest maximum visual evoked potential (EVP2) measured by the data plot to the nadir (N3) of the EVP2, and wherein x and y are an exponents between 0.333 and 3.

57. The method of claim 56, further comprising computing a second anesthesia evaluation number (AI-2) according to the equation AI-2=(AI)ⁿ where n is an exponent between 0.333 and 3.

58. The method of claim 45, further comprising computing an anesthesia evaluation number (AI) from the VEP data plot in accordance with the equation, AI=δ

(EVP2)/δ

t_max/t₁/P2 in (μ

V/ms), where (EVP2) is a second and largest maximum visual evoked potential, δ

(EVP2)/δ

t_max is the peak first time-derivative (δ

/δ

t) of EVP2, t₁ is the absolute magnitude of EVP2, and P2 is the vertical distance between the maximum value of the upslope of a second and largest maximum visual evoked potential (EVP2) measured by the data plot to the nadir (N3) of the EVP2.

59. The method of claim 45, further comprising computing an anesthesia evaluation number (AI) from the VEP data plot in accordance with the equation, AI=δ

(EVP2)/δ

t_max/t₁/P2 in (μ

V/ms), where (EVP2) is a second and largest maximum visual evoked potential, δ

(EVP2)/δ

t_max is the peak first time-derivative (δ

/δ

t) of EVP2, t₁ is the absolute magnitude of EVP2, P2 is the vertical distance between the maximum value of the upslope of a second and largest maximum visual evoked potential (EVP2) measured by the data plot to the nadir (N3) of the EVP2, and wherein x, y and z are an exponents between 0.333 and 3.

60. The method of claim 45, further comprising computing an anesthesia evaluation number (AI) from the VEP data plot in accordance with the equation, AI=(δ

(EVP2)/δ

t_mean)^x/(t₁^y/P2^z)^m in (μ

V/ms), where (EVP2) is a second and largest maximum visual evoked potential, δ

(EVP2)/δ

t_mean is the quotient of P2 and the temporal interval occurring between the onset of the upslope of EVP2 and P2, t₁ is the absolute magnitude of EVP2, P2 is the vertical distance between the maximum value of the upslope of a second and largest maximum visual evoked potential (EVP2) measured by the data plot to the nadir (N3) of the EVP2, and wherein x, y, z and m are an exponents between 0.333 and 3.

61. The method of claim 60, further comprising computing a second anesthesia evaluation number (AI-2) according to the equation AI-2=(AI)ⁿ where n is an exponent between 0.333 and 3.

62. The method of claim 45, further comprising computing an anesthesia evaluation number (AI) from the VEP data plot in accordance with the equation, AI=(δ

(EVP2)/δ

t_max)^x/(t₁^y/P2^z)^m in (μ

V/ms), where (EVP2) is a second and largest maximum visual evoked potential, δ

(EVP2)/δ

t_max is the peak first time-derivative (δ

/δ

t) of EVP2, t₁ is the absolute magnitude of EVP2, P2 is the vertical distance between the maximum value of the upslope of a second and largest maximum visual evoked potential (EVP2) measured by the data plot to the nadir (N3) of the EVP2, and wherein x, y, z and m are an exponents between 0.333 and 3.

63. The method of claim 62, further comprising computing a second anesthesia evaluation number (AI-2) according to the equation AI-2=(AI)ⁿ where n is an exponent between 0.333 and 3.

64. The method of claims 45, 47, 48, 51, 52 to 54, 56 to 58 and 60, further comprising computing a dimensionless anesthesia evaluation index (AI-D) according to the equation, AI-D=(AI)/CV(AI), where CV is a static control value defined as the value of AI measured at a time before the induction of anesthesia.

65. The method of claims 46, 49, 53, 57, 61 and 63, further comprising computing a dimensionless anesthesia evaluation index (AI-D) according to the equation, AI-D=(AI-2)/CV(AI-2), where CV is a static control value defined as the value of AI-2 measured at a time before the induction of anesthesia.

66. The method of claim 50, further comprising computing a dimensionless anesthesia evaluation index (AI-D) according to the equation, AI-D=(AI-3)/CV(AI-3), where CV is a static control value defined as the value of AI-3 measured at a time before the induction of anesthesia.

67. The method of claims 45 to 63, further comprising calibrating the anesthesia evaluation number by measuring the anesthesia evaluation number prior to the administration of an anesthesia.