Method and system for predicting human cognitive performance

• US 6,553,252 B2
• Filed: 05/04/2001
• Issued: 04/22/2003
• Est. Priority Date: 10/30/1998
• Status: Expired due to Term

• Current Assignee: The United States Of America As Represented By The Secretary Of The Army
• Original Assignee: The United States Of America As Represented By The Secretary Of The Army
• Inventors: Wesensten, Nancy Jo, Hall, Stanley W., Redmond, Daniel P., Thomas, Maria L., Belenky, Gregory L., Kamimori, Gary H., Balkin, Thomas J., Thorne, David R., Sing, Helen C.
• Primary Examiner(s): Hindenburg, Max F.
• Assistant Examiner(s): ASTORINO, MICHAEL C
• Application Number:
  US09/848,352
• Publication Number:
  US 20010021800A1
• Time in Patent Office: 718 Days
• Field of Search: 600/300-301, 600/544-545, 128/903, 128/904, 128/920, 128/923, 128/898, 434/236-238, 434/219-220, 434/118, 702/19
• US Class Current: 600/544
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/16 Devices for psychotechnics using teaching or educational appliances G09B1/00 - G09B7/00; Testing reaction times ; Devices for evaluating the psychological state
• CPC Class Codes: A61B 5/162 Testing reaction times
• CPC Class Codes: A61B 5/4806 Sleep evaluation A61B5/4821 takes precedence; devices for inducing sleep A61M21/02
• CPC Class Codes: A61B 5/4809 Sleep detection, i.e. determining whether a subject is asleep or not
• CPC Class Codes: A61B 5/4812 Detecting sleep stages or cycles
• CPC Class Codes: G16H 20/70 relating to mental therapies, e.g. psychological therapy or autogenous training
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: Y10S 128/903 Radio telemetry
• CPC Class Codes: Y10S 128/92 Computer assisted medical diagnostics

Abstract

An apparatus and method for predicting cognitive performance of an individual based on factors including sleep history and the time of day. The method facilitates the creation of predicted cognitive performance curves that allow an individual to set his/her sleep times to produce higher levels of cognitive performance. The method also facilitates the reconstruction of past cognitive performance levels based on sleep history.

Claims

1. A method for determining a predicted cognitive performance level comprising:

a. inputting a data series having wake states and sleep states of an individual,

b. selecting a function based on the wake states and sleep states in the data series,

c. calculating a cognitive performance capacity based on the selected function,

d. modulating the cognitive performance capacity with a time of day value,

e. outputting the modulated value as the predicted cognitive performance, and

when a piece of data within the data series is inconclusive, then

selecting at least two functions,

determining a cognitive performance capacity using each function,

modulating each cognitive performance capacity with a time of day value,

providing each modulated value, and

repeating the steps a through e for each modulated value at least once.

2. The method according to claim 1 further comprising:

storing the modulated values,

repeating steps b through e,

plotting a curve from the stored modulated values, and

outputting the curve representing cognitive performance level over time.

3. The method according to claim 2, wherein the data series includes past information such that the curve is used to determine the cognitive level of an individual at an earlier time.

4. The method according to claim 2, further comprising extrapolating from the curve a predictive curve based on anticipated wake states and anticipated sleep states.

5. The method according to claim 1, wherein said outputting step includes outputting the predicted cognitive performance to a display.

6. The method according to claim 1, wherein said outputting step includes outputting the predicted cognitive performance to a data file.

7. The method according to claim 1, wherein said outputting step includes outputting the predicted cognitive performance to a printing device.

8. The method according to claim 1, further comprising formulating the time of day values to represent a curve having a period of 24 hours.

9. The method according to claim 8, wherein the curve includes a first sinusoidal curve having a 24-hour period and a second sinusoidal curve having a 12-hour period.

10. The method according to claim 1, wherein the time of day values represent a curve having a period of 24 hours.

11. The method according to claim 10, wherein the curve includes a first sinusoidal curve having a 24-hour period and a second sinusoidal curve having a 12-hour period.

12. The method according to claim 1, wherein the data series is obtained from a device adapted to be attached to the individual.

13. The method according to claim 1, wherein the data series is an output of a sleep scoring system.

14. The method according to claim 1, wherein step a includes selecting from a group consisting of a wake function, a sleep function, a delay function, and a sleep inertia function.

15. The method according to claim 1, wherein step a includes selecting from a group consisting of a wake function, a sleep function, and a sleep inertia function.

16. The method according to claim 1, wherein step a includes

determining a present state for the data series as either a wake state or a sleep state,

calculating a length of time in the present state, and

selecting the function based on the length of time in the present state and the present state.

17. The method according to claim 16, wherein the selecting the function step selects from a group consisting of a wake function, a sleep function, a delay function, and a sleep inertia function.

18. The method according to claim 1, wherein when another inconclusive piece of data is found, then the selecting a function includes determining the two functions that would provide the lowest cognitive performance capacity and the highest cognitive performance capacity among the combinations of cognitive performance capacities resulting from the inconclusive pieces of data.

19. The method according to claim 1, wherein the first calculating step calculates a cognitive performance level as a percentage value such that 100% is a maximum cognitive performance capacity.

20. The method according to claim 1, wherein said method is performed in real-time.

21. A computer data signal embodied in a carrier wave readable by a computing system and encoding a computer program of instructions for executing a computer process performing a method for determining a cognitive performance level comprising:

inputting a data series having wake states and sleep states of an individual,

selecting a function based on the wake states and sleep states in the data series,

calculating a cognitive performance capacity based on the selected function,

modulating the cognitive performance capacity with a time of day value, and

outputting the modulated value as the predicted cognitive performance.

22. A computer-readable medium having computer-executable instructions for a method for determining a cognitive performance level comprising:

inputting a data series having wake states and sleep states of an individual,

selecting a function based on the wake states and sleep states in the data series,

calculating a cognitive performance capacity based on the selected function,

modulating the cognitive performance capacity with a time of day value, and

outputting the modulated value as the predicted cognitive performance.

23. A method for determining a predicted cognitive performance comprising:

inputting a data series having wake states and sleep states of an individual,

selecting a function based on the wake states and sleep states in the data series,

calculating a cognitive performance capacity based on the selected function,

approximating a first curve of calculated cognitive performance capacities,

modulating the first curve with a second curve representing time of day rhythms, and

outputting the modulated first curve representing the predicted cognitive performance; and

wherein the data series includes past wake states and past sleep states such that the modulated first curve is used to determine the cognitive level of an individual at an earlier time.

24. The method according to claim 23, wherein said outputting step includes outputting a value of a point on the modulated first curve to a display.

25. The method according to claim 23, wherein said outputting step includes outputting a value of a point on the modulated first curve to a data file.

26. The method according to claim 23, wherein said outputting step includes outputting a value of a point on the modulated first curve to a printing device.

27. The method according to claim 23, further comprising extrapolating from the modulated first curve a predictive curve based on anticipated wake states and anticipated sleep states.

28. The method according to claim 23, further comprising formulating the second curve having a period of 24 hours such that the second curve includes a first sinusoidal curve having a 24-hour period and a second sinusoidal curve having a 12-hour period.

29. The method according to claim 23, wherein the second curve has a period of 24 hours such that the second curve includes a first sinusoidal curve having a 24-hour period and a second sinusoidal curve having a 12-hour period.

30. The method ,according to claim 23, wherein the data series is obtained from a device adapted to be attached to the individual.

31. The method according to claim 23, wherein the data series is an output of a sleep scoring system.

32. The method according to claim 23, wherein the selecting step selects from a group consisting of a wake function, a sleep function, a delay function, and a sleep inertia function.

33. The method according to claim 23, wherein the selecting step includes:

determining a present state for the data series as either the wake state or the sleep state,

calculating a length of time in the present state, and

selecting the function based on the length of time in the present state and the present state.

34. The method according to claim 33, wherein the selecting the function step selects from a group consisting of a wake function, a sleep function, a delay function, and a sleep inertia function.

35. The method according to claim 23, wherein the calculating step includes calculating a cognitive performance level as a percentage value such that 100% is a maximum cognitive performance capacity.

36. The method according to claim 23, wherein said method operates in real-time.

37. A computer data signal embodied in a carrier wave readable by a computing system and encoding a computer program of instructions for executing a computer process performing a method for determining a predicted cognitive performance comprising:

inputting a data series having wake states and sleep states of an individual,

selecting a function based on the wake states and sleep states in the data series,

calculating a cognitive performance capacity based on the selected function,

approximating a first curve of calculated-cognitive performance capacities,

modulating the first curve with a second curve representing time of day rhythms, and

outputting the modulated first curve representing the predicted cognitive performance.

38. A computer-readable medium having computer-executable instructions for a method for determining a predicted cognitive performance comprising:

inputting a data series having wake states and sleep states of an individual,

selecting a function based on the wake states and sleep states in the data series,

calculating a cognitive performance capacity based on the selected function,

approximating a first curve of calculated cognitive performance capacities,

modulating the first curve with a second curve representing time of day rhythms, and

outputting the modulated first curve representing the predicted cognitive performance.

39. An apparatus comprising:

input means for receiving data,

interpretation means for analyzing the received data and selecting a calculation function based on the composition of the received data,

determination means for calculating a cognitive performance capacity using the selected calculation function,

storing means for storing modulation data, and

modulating means for modulating the cognitive performance capacity with modulation data to produce a predicted cognitive performance.

40. The apparatus of claim 39, further comprising a means for displaying the predicted cognitive performance.

41. The apparatus of claim 39, wherein said interpretation means includes means for sleep scoring the received data.

42. The apparatus of claim 39, wherein said interpretation means determines a time representing a length of time of a current state of the data.

43. The apparatus of claim 39, wherein said interpretation means determines a time representing a transition from one state to another.

44. The apparatus of claim 39, wherein the modulating on data represents variations over a 24-hour period.

45. The apparatus of claim 39, wherein said modulating means time sequences the cognitive performance capacity with the modulation data.

46. The apparatus of claim 39, further comprising storage means for storing the predicted cognitive performance from said modulating means.

47. The apparatus of claim 46, wherein said storage means stores the predicted cognitive performance as a series of data.

48. The apparatus of claim 39, further comprising means for communicating at least one of the cognitive performance capacity from said determination means and the predicted cognitive performance from said modulating means to a central device.

49. The apparatus of claim 48, wherein said communicating means communicates using telemetry.

50. A computer readable material having program code embodied therein for generating a predictive cognitive performance level measure based on a sleep/wake history of an individual, said material comprising:

means for analyzing sleep/wake input data and selecting a function based on the analysis,

means for calculating a cognitive performance capacity based on the sleep/wake input data and the selected function,

means for modifying the calculated cognitive performance capacity based on stored data, and

means for outputting the modified cognitive performance capacity as a predicted cognitive performance level.

51. The computer readable material according to claim 50, wherein means for modifying includes means for matching of the stored data with the cognitive performance capacity based on temporal data connected to the input sleep/wake data.