Method and system for predicting human congnitive performance

• US 6,740,032 B2
• Filed: 03/04/2003
• Issued: 05/25/2004
• Est. Priority Date: 10/30/1998
• Status: Expired due to Term

• Current Assignee: Daniel P. Redmond, David R. Thorne, Gary H. Kamimori, Gregory L. Belenky, Helen C. Sing, Maria L. Thomas, Nacy J.O. Wesensten, Stanley W. Hall, Thomas J. Balkin
• Original Assignee: Daniel P. Redmond, David R. Thorne, Gary H. Kamimori, Gregory L. Belenky, Helen C. Sing, Maria L. Thomas, Nacy J.O. Wesensten, Stanley W. Hall, Thomas J. Balkin
• Inventors: Hall, Stanley W., Redmond, Daniel P., Thomas, Maria L., Belenky, Gregory L., Kamimori, Gary H., Balkin, Thomas J., Thorne, David R., Sing, Helen C., Wesensten, Nacy Jo
• Primary Examiner(s): Nasser, Robert L.
• Application Number:
  US10/377,602
• Publication Number:
  US 20030163027A1
• Time in Patent Office: 448 Days
• Field of Search: 600/300, 600/544
• US Class Current: 600/300
• 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 5/4806 Sleep evaluation A61B5/4821 takes precedence; devices for inducing sleep A61M21/02
• CPC Class Codes: A61B 5/4812 Detecting sleep stages or cycles
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

An apparatus and method for predicting cognitive performance of an individual based on factors including preferably 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 providing a cognitive performance level comprising:

receiving a data series representing at least one wake state and at least one sleep state,

selecting a function based on the data series,

determining a cognitive performance capacity using the selected function,

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

providing the modulated value.

2. The method according to claim 1, further comprising repeating the selecting, determining, and modulating steps for at least two pieces of the data series.

3. The method according to claim 2, wherein the providing step includes displaying the modulated value to an individual located proximate to where the data series is received.

4. The method according to claim 1, wherein the time of day value is selected from a series of time of day values representing a curve having a period of 24 hours.

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

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

7. The method according to claim 1, further comprising:

storing the predicted cognitive performance level, and

repeating the selecting, determining, modulating, and storing steps at least once.

8. The method according to claim 7, further comprising plotting a curve based on the stored predicted cognitive performance levels.

9. An apparatus for providing a cognitive performance level comprising:

means for receiving a data series having at least one wake state and at least one sleep state,

means for selecting a function based on the data series,

means for determining a cognitive performance capacity using the selected function,

means for storing a series of time of day values,

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

means for providing the modulated value.

10. The apparatus according to claim 9, wherein the selecting means selects the function from a group consisting of a wake function, a sleep function, and a sleep inertia function.

11. The apparatus according to claim 9, wherein the selecting means selects the function from a group consisting of a wake function, a sleep function, a delay function, and a sleep inertia function.

12. The apparatus according to claim 9, wherein the stored time of day values represent a curve having a period of 24 hours.

13. 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.

14. The method according to claim 13 further comprising:

storing the predicted cognitive performance,

repeating the selecting, calculating, modulating and outputting steps of claim 13,

plotting a curve from the stored modulated values, and

outputting the curve representing cognitive performance level over time.

15. The method according to claim 14, 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.

16. The method according to claim 14, further comprising extrapolating from the curve a predictive curve based on anticipated wake states and anticipated sleep states.

17. The method according to claim 13, wherein said outputting step includes outputting the predicted cognitive performance to a display.

18. The method according to claim 13, wherein said outputting step includes outputting the predicted cognitive performance to a data file.

19. The method according to claim 13, wherein said outputting step includes outputting the predicted cognitive performance to a printing device.

20. The method according to claim 13, further comprising formulating the time of day values to represent a curve having a period of 24 hours.

21. The method according to claim 20, wherein the curve includes a first sinusoidal curve having a 24-hour period and a second sinusoidal curve having a 12-hour period.

22. The method according to claim 13, wherein the time of day values represent a curve having a period of 24 hours.

23. The method according to claim 13, wherein the data series is obtained from a device attached to the individual.

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

25. The method according to claim 13, wherein the selecting step selects from a group consisting of a wake function, a sleep function, a delay function, and a sleep inertia function.

26. The method according to claim 13, wherein the selecting step selects from a group consisting of a wake function, a sleep function, and a sleep inertia function.

27. The method according to claim 13, wherein the selecting step includes

determining the 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.

28. The method according to claim 13, wherein the first calculating step calculates a cognitive performance level as a percentage value such that 100% is a maximum cognitive performance capacity.

29. 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.

30. The method according to claim 29, wherein said outputting step includes outputting a value of a point on the modulated first curve to a display.

31. The method according to claim 29, wherein said outputting step includes outputting a value of a point on the modulated first curve to a data file.

32. The method according to claim 29, wherein said outputting step includes outputting a value of a point on the modulated first curve to a printing device.

33. The method according to claim 29, further comprising extrapolating from the modulated first curve a predictive curve based on anticipated wake states and anticipated sleep states.

34. The method according to claim 29, 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.

35. The method according to claim 29, 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.

36. The method according to claim 29, wherein the data series is obtained from a device attached to the individual.

37. The method according to claim 29, wherein the data series is an output of a sleep scoring system.

38. The method according to claim 29, wherein the selecting step selects from a group consisting of a wake function, a sleep function, a delay function, and a sleep inertia function.

39. The method according to claim 29, wherein the selecting step includes:

determining the 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.

40. The method according to claim 29, wherein the calculating step includes calculating a cognitive performance level as a percentage value such that 100% is a maximum cognitive performance capacity.