System and method for analysis of respiratory cycle-related EEG changes in sleep-disordered breathing

• US 20040254493A1
• Filed: 06/14/2004
• Published: 12/16/2004
• Est. Priority Date: 06/13/2003
• Status: Active Grant

• Current Assignee: Altarum Institute, Board of Regents of the University of Michigan (University of Michigan)
• Original Assignee: Altarum Institute, Board of Regents of the University of Michigan (University of Michigan)
• Inventors: Chervin, Ronald D., Burns, Joseph W., Roussi, Christopher, Subotic, Nikolas S.
• Granted Patent: US 7,190,995 B2
• Time in Patent Office: Days
• US Class Current: 600/544
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/087 Measuring breath flow
• CPC Class Codes: A61B 5/374 Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
• CPC Class Codes: A61B 5/4818 Sleep apnoea

Abstract

A system and method for determining respiratory cycle-related EEG changes (RCREC) for a subject with sleep-disordered breathing are provided. The method includes receiving an EEG signal from the subject using at least one sensor, and defining at least two respiratory cycle segments within each respiratory cycle. The method further includes determining an EEG power of the EEG signal during each of the at least two respiratory cycle segments, and determining RCREC by calculating a difference between a maximum EEG segment power and a minimum EEG segment power.

Claims

1. A method for determining respiratory cycle-related EEG changes (RCREC) for a subject, the method comprising:

receiving an EEG signal from the subject using at least one EEG sensor;

defining at least two respiratory cycle segments within each respiratory cycle;

determining an EEG power of the EEG signal during each of the at least two respiratory cycle segments; and

determining RCREC by calculating a difference between a maximum EEG segment power and a minimum EEG segment power.

2. The method according to claim 1, wherein determining an EEG power includes digitally filtering the EEG signal.

3. The method according to claim 2, wherein digitally filtering the EEG signal utilizes at least one 5^th order Butterworth filter.

4. The method according to claim 1, wherein determining the EEG power includes taking a short-time Fourier transform of the EEG signal.

5. The method according to claim 1, wherein determining an EEG power includes determining the EEG power for one or more frequencies within at least one of a delta (approximately 1-4 Hz) frequency range, a theta (approximately 5-7 Hz) frequency range, an alpha (approximately 8-12 Hz) frequency range, a sigma (approximately 13-15 Hz) frequency range, and a beta (approximately 16-30 Hz) frequency range.

6. The method according to claim 1, further comprising normalizing the EEG power for each respiratory cycle segment by a mean EEG power during the entire respiratory cycle.

7. The method according to claim 1, further comprising averaging the EEG segment powers over a plurality of respiratory cycles.

8. The method according to claim 1, further comprising receiving a respiratory signal from the subject.

9. The method according to claim 8, wherein the respiratory signal includes at least one of a nasal airflow and an oral airflow.

10. The method according to claim 1, wherein the respiratory signal includes at least one of a chest excursion and an abdominal excursion.

11. The method according to claim 1, wherein the respiratory signal includes an intrathoracic pressure.

12. The method according to claim 1, wherein the respiratory signal includes respiratory muscle activity.

13. The method according to claim 1, wherein the at least two respiratory segments include four fixed time segments defined by maxima, minima, and midpoints of inspiratory and expiratory phases of the respiratory cycle.

14. The method according to claim 1, wherein receiving the EEG signal includes recording the signal over about the first three hours of nocturnal sleep.

15. The method according to claim 1, further comprising amplifying the EEG signal.

16. The method according to claim 1, further comprising converting the EEG signal from analog to digital format.

17. The method according to claim 1, further comprising storing EEG data in memory.

18. The method according to claim 1, further comprising displaying EEG data on a display.

19. The method according to claim 1, further comprising comparing values of RCREC before and after intervention to treat sleep-disordered breathing.

20. The method according to claim 1, further comprising comparing RCREC values with values of a sleepiness measure.

21. A method for analyzing respiratory cycle related EEG changes (RCREC) in a subject with sleep-disordered breathing, the method comprising:

receiving an EEG signal from the subject using at least one sensor;

receiving a respiratory signal to define at least two respiratory cycle segments within each respiratory cycle;

determining an EEG power of the EEG signal for each of the respiratory cycle segments within at least one frequency range;

normalizing the EEG power for each respiratory cycle segment by a mean EEG power during the entire respiratory cycle;

averaging the normalized EEG segment powers over a plurality of respiratory cycles; and

determining RCREC by calculating a difference between a maximum EEG segment power and a minimum EEG segment power.

22. A system for determining respiratory cycle-related EEG changes (RCREC) in a subject, the system comprising:

at least one EEG sensor arranged obtain an EEG signal from the subject;

a respiratory measurement device for defining at least two segments of each respiratory cycle;

a processor in communication with the at least one sensor and the respiratory measurement device, the processor determining an EEG power of the EEG signal for each of the at least two respiratory cycle segments, and determining RCREC by calculating a difference between a maximum EEG segment power and a minimum EEG segment power.

23. The system according to claim 22, wherein the processor utilizes digital filtering of the EEG signal to determine the EEG power.

24. The system according to claim 23, wherein the processor utilizes at least one 5^th order Butterworth filter.

25. The system according to claim 22, wherein the processor utilizes a short-time Fourier transform of the EEG signal to determine the EEG power.

26. The system according to claim 22, wherein the processor determines the EEG power for one or more frequencies within at least one of a delta (approximately 1-4 Hz) frequency range, a theta (approximately 5-7 Hz) frequency range, an alpha (approximately 8-12 Hz) frequency range, a sigma (approximately 13-15 Hz) frequency range, and a beta (approximately 16-30 Hz) frequency range.

27. The system according to claim 22, wherein the processor normalizes the EEG power for each respiratory cycle segment by a mean EEG power during the entire respiratory cycle.

28. The system according to claim 22, wherein the processor averages the EEG segment powers over a plurality of respiratory cycles.

29. The system according to claim 22, wherein the respiratory measurement device includes at least one airflow sensor for measuring at least one of a nasal airflow and an oral airflow.

30. The system according to claim 29, wherein the at least one airflow sensor includes a thermocouple.

31. The system according to claim 22, wherein the respiratory measurement device includes at least one position sensor for measuring at least one of a chest excursion and an abdominal excursion.

32. The system according to claim 22, wherein the respiratory measurement device includes at least one pressure sensor for measuring intrathoracic pressure.

33. The system according to claim 22, wherein the respiratory measurement device includes at least one EMG sensor for measuring respiratory muscle activity.

34. The system according to claim 22, further comprising an amplifier in communication with the at least one EEG sensor for amplifying the EEG signal.

35. The system according to claim 22, further comprising an A/D converter in communication with the processor for converting the EEG signal from analog to digital format.

36. The system according to claim 22, further comprising memory in communication with the processor for storing EEG data.

37. The system according to claim 22, further comprising a display in communication with the processor for displaying EEG data.

38. A computer readable medium for determining respiratory cycle-related EEG changes (RCREC) in a subject, the medium comprising:

computer readable instructions for determining an EEG power of a received EEG signal for a plurality of respiratory cycle segments, and determining RCREC by calculating a difference between a maximum EEG segment power and a minimum EEG segment power.

39. The medium according to claim 38, further comprising computer readable instructions for normalizing the EEG power for each respiratory cycle segment by a mean EEG power during the entire respiratory cycle.

40. The medium according to claim 38, further comprising computer readable instructions for averaging the normalized EEG segment powers over a plurality of respiratory cycles.