Detection of encephalopathy

• US 7,231,246 B2
• Filed: 07/07/2004
• Issued: 06/12/2007
• Est. Priority Date: 07/07/2004
• Status: Active Grant

• Current Assignee: GE Healthcare Finland OY (General Electric Company)
• Original Assignee: GE Healthcare Finland OY (General Electric Company)
• Inventors: Sarkela, Mika, Rautee, Riina Anniina
• Primary Examiner(s): Hindenburg; Max F.
• Assistant Examiner(s): MALLARI, PATRICIA C
• Application Number:
  US10/886,485
• Publication Number:
  US 20060009709A1
• Time in Patent Office: 1,070 Days
• Field of Search: None
• US Class Current: 600/544
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/4076 Diagnosing or monitoring particular conditions of the nervous system A61B5/4821, A61B5/4824 take precedence

Abstract

The invention relates to detection of encephalopathy based on EEG signal data obtained from a patient. A first signal component is derived from the EEG signal data, the first signal component representing low frequencies of the EEG signal data. A first parameter indicative of disorder in the first signal component and a second parameter indicative of disorder in EEG signal data representing a first frequency band are then computed, wherein the first frequency band includes higher frequencies than said low frequencies. An indicator of encephalopathy is then determined based on the first parameter and the second parameter, the indicator of encephalopathy being indicative of encephalopathy.

Claims

1. A method for detecting a case of encephalopathy, the method comprising the steps of:

obtaining EEG signal data from a patient;

deriving a first signal component from the EEG signal data, wherein the first signal component represents a first frequency band of the EEG signal data, the first frequency band including low frequencies of the EEG signal data;

computing a first parameter indicative of disorder in the first signal component and a second parameter indicative of disorder in signal data representing a second frequency band of the EEG signal data, the second frequency band including higher frequencies than the first frequency band; and

determining an indicator of encephalopathy based on the first parameter and the second parameter, the indicator of encephalopathy being indicative of encephalopathy.

2. A method according to claim 1, wherein the first frequency band includes frequencies between approximately 0.2 Hz and approximately 10 Hz.

3. A method according to claim 1, wherein the first frequency band includes frequencies between approximately 1 Hz and approximately 4 Hz.

4. A method according to claim 1, wherein the determining step includes a step of calculating the difference of the second parameter and the first parameter.

5. A method according to claim 1, wherein the determining step includes a step of calculating the ratio of the first parameter and the second parameter.

6. A method according to claim 1, wherein the second frequency band includes frequencies between approximately 4 Hz and approximately 32 Hz.

7. A method according to claim 3, wherein the second frequency band includes frequencies between approximately 4 Hz and approximately 8 Hz.

8. A method according to claim 1, wherein the second frequency band includes substantially all frequencies of the EEG signal data obtained in the obtaining step.

9. A method according to claim 7, wherein the determining step includes a step of calculating the difference of the second parameter and the first parameter.

10. A method according to claim 1, further comprising a step of defining a threshold for detecting encephalopathy.

11. A method according to claim 10, further comprising a step of calculating a third parameter indicative of disorder in signal data representing a third frequency band of the EEG signal data.

12. A method according to claim 11, wherein the defining step includes defining the threshold, and wherein the threshold is represented by a line on a plane defined by two variables representing the indicator of encephalopathy and the third parameter.

13. A method according to claim 10, further comprising a step of making a decision on occurrence of encephalopathy based on the threshold and the indicator of encephalopathy.

14. A method according to claim 12, further comprising a step of making a decision on occurrence of encephalopathy based on the line, the indicator of encephalopathy, and the third parameter.

15. A method according to claim 11, wherein the third frequency band includes frequencies between approximately 1 Hz and an upper limit frequency of at least 13 Hz.

16. A method according to claim 11, wherein the third frequency band includes substantially all frequencies of the EEG signal data obtained in the obtaining step.

17. A method according to claim 14, wherein the step of making the decision includes a step of comparing the line with a data point defined by the indicator of encephalopathy and the third parameter.

18. A method according to claim 17, wherein the step of making the decision includes the sub-steps of:

deciding on occurrence of encephalopathy for a sequence of data points obtained during a certain measurement period, each data point representing the values of the indicator of encephalopathy and the third parameter at a particular instant during said measurement period; and

making a final decision on the occurrence of encephalopathy based on the deciding step.

19. A method according to claim 14, wherein the step of making the decision includes a step of comparing the line with a data set including a sequence of data points obtained during a certain measurement period, each data point representing the values of the indicator of encephalopathy and the third parameter at a particular instant during said measurement period.

20. A method according to claim 19, wherein the comparing step includes a step of calculating at least one descriptor indicative of the characteristics of the data set.

21. A method according to claim 20, wherein the calculating step includes calculating a center of gravity of the data set.

22. A method according to claim 21, wherein the comparing step includes calculating a distance between the threshold line and the center of gravity.

23. A method according to claim 12, further comprising a step of graphically presenting the line and a data set including a sequence of data points obtained during a certain measurement period, each data point representing the values of the indicator of encephalopathy and the third parameter at a particular instant during said measurement period.

24. A method according to claim 1, further comprising a step of supplying the indicator of encephalopathy as input data to a device controlling infusion of a sedative to the patient.

25. A method according to claim 1, wherein the determining step includes determining a time series of the indicator of encephalopathy.

26. A method according to claim 25, further comprising a step of making a prognosis on cerebral state of the patient.

27. An arrangement for detecting a case of encephalopathy, the arrangement comprising:

measurement means for obtaining EEG signal data from a patient;

first means for deriving a first signal component from the EEG signal data, wherein the first signal component represents a first frequency band of the EEG signal data, the first frequency band including low frequencies of the EEG signal data;

computing means for computing a first parameter indicative of disorder in the first signal component and a second parameter indicative of disorder in signal data representing a second frequency band of the EEG signal data, the second frequency band including higher frequencies than the first frequency band; and

determination means for determining an indicator of encephalopathy based on the first parameter and the second parameter, the indicator of encephalopathy being indicative of encephalopathy.

28. An arrangement according to claim 27, further comprising means for defining a threshold for detecting encephalopathy.

29. An arrangement according to claim 28, further comprising calculation means for calculating a third parameter indicative of disorder in signal data representing a third frequency band of the EEG signal data.

30. An arrangement according to claim 29, wherein the means for defining the threshold are configured to define a threshold represented by a threshold line for a plane defined by two variables representing the indicator of encephalopathy and the third parameter.

31. An arrangement according to claim 28, further comprising decision-making means for making a decision on occurrence of encephalopathy based on the threshold and at least one variable measured from the patient, the at least one variable including the indicator of encephalopathy.

32. An arrangement according to claim 30, further comprising decision-making means for making a decision on occurrence of encephalopathy based on the threshold line, the indicator of encephalopathy, and the third parameter.

33. An arrangement according to claim 30, further comprising display means for graphically presenting the threshold line and a data set including a sequence of data points obtained during a certain measurement period, each data point representing the values of the indicator of encephalopathy and the third parameter at a particular instant during said measurement period.

34. An arrangement according to claim 27, wherein the first frequency band includes frequencies between approximately 0.2 Hz and approximately 10 Hz.

35. An arrangement according to claim 27, wherein the first frequency band includes frequencies between approximately 1 Hz and approximately 4 Hz.

36. An arrangement according to claim 27, wherein the second frequency band includes frequencies between approximately 4 Hz and approximately 32 Hz.

37. An arrangement according to claim 35, wherein the second frequency band includes frequencies between approximately 4 Hz and approximately 8 Hz.

38. An arrangement according to claim 29, wherein the third frequency band includes frequencies between approximately 1 Hz and an upper limit frequency of at least 13 Hz.

39. A method for assisting a physician, the method comprising the steps of:

obtaining EEG signal data from a patient;

deriving a first signal component from the EEG signal data, wherein the first signal component represents a first frequency band of the EEG signal data, the first frequency band including low frequencies of the EEG signal data;

computing a first parameter indicative of disorder in the first signal component and a second parameter indicative of disorder in signal data representing a second frequency band of the EEG signal data, the second frequency band including higher frequencies than the first frequency band;

determining an indicator of encephalopathy based on the first parameter and the second parameter, the indicator of encephalopathy being indicative of encephalopathy; and

in response to the determining step, compiling a treatment recommendation for a physician.

40. A method according to claim 39, wherein the compiling step includes a recommendation of a type of medication to be used.

41. An arrangement for detecting a case of encephalopathy, the arrangement comprising:

a measurement device configured to obtain EEG signal data from the patient;

a first calculator configured to derive a first signal component from the EEG signal data, wherein the first signal component represents a first frequency band of the EEG signal data, the first frequency band including low frequencies of the EEG signal data;

a second calculator configured to calculate a first parameter indicative of disorder in the first signal component and a second parameter indicative of disorder in signal data representing a second frequency band of the EEG signal data, the second frequency band including higher frequencies than the first frequency band; and

a third calculator configured to determine an indicator of encephalopathy based on the first parameter and the second parameter, the indicator of encephalopathy being indicative of encephalopathy.

42. A computer program embodied on a computer-readable medium for detecting a case of encephalopathy, the computer program executing the steps of:

deriving a first signal component from EEG signal data measured from a patient, wherein the first signal component represents a first frequency band of the EEG signal data, the first frequency band including low frequencies of the EEG signal data;

computing a first parameter indicative of disorder in the first signal component and a second parameter indicative of disorder in signal data representing a second frequency band of the EEG signal data, the second frequency band including higher frequencies than the first frequency band; and

determining an indicator of encephalopathy based on the first parameter and the second parameter, the indicator of encephalopathy being indicative of encephalopathy.