1. Clinical Applications of Spectral Analysis Winni Hofman, PhD University of Amsterdam Medcare Amsterdam

2. Introduction of spectrum calculations Spectra and sleep staging Spectral analysis for differentiating various night patterns Spectral analysis and arousals Frequency analysis of snoring Outline 4 Examples of applications of Spectral Analysis

3. Spectral Analysis is a tool to: Quantify the frequency content of signals, like EEG or snore signal, for further calculations Help you with your manual sleep stage scoring Detect problems in the recording, e.g. 60 Hz noise

4. Frequency content of a signal EEG frequency bands: Beta- >12 Hz Sigma- 12 –14 Hz Alpha- 8 –12 Hz Theta- 4 – 7 Hz Delta- 0.5 – 4 Hz

5. Frequency content of a signal R&K definition of sleep EEG: Stage 1: Relatively low voltage, mixed frequency EEG in 2 – 7 cps range without rapid eye movements Stage 2: 12-14 cps sleep spindles and K complexes on a background of relatively low voltage, mixed frequency EEG Stage 3: 20%-50% of high amplitude, slow wave activity Etc…….

6. Complex signals Signals like the EEG are complex signals, existing of many superimposed waveforms with: –Various frequencies –Various amplitudes –Various phase relationships

7. Describing frequency content of a signal Sine waves are used for description because they can be defined exactly by their: –Frequency –Amplitude –Phase

8. Sine wave

9. Cosine wave 1 cycle

10. Spectral Analysis describes a signal: By calculating the contributions of the various superimposed frequency components Shows these contributions in a Power Spectrum plot

11. Spectral analysis and Sleep Stages Differences in frequencies between sleep stages are visible in a Power Spectrum

12. R&K definition of stage Wake: Wake: alpha activity and/or low voltage, mixed frequency activity

13. Wake Alpha peak

14. R&K definition of stage 2: Stage 2: 12-14 cps sleep spindles and K complexes on a background of relatively low voltage, mixed frequency EEG

15. Stage 2 Theta peak Alpha peak

16. R&K definition of stage 4: Stage 4: > 50% of high amplitude, slow wave activity

17. Stage 4 Delta peak

18. R&K definition of stage REM: Stage REM: a relatively low voltage, mixed frequency EEG (in conjunction with episodic REMs and low amplitude EMG)

19. Stage REM Theta peak

20. Delta waves with superimposed alpha

21. Power spectrum S4 with alpha

22. Spectral colour plot Shows changes in frequency content of a signal over longer time period (for example a whole night) –Colour: power –Y-axis: frequency –X-axis: time Each point in time in the Spectral colour plot represents a power spectrum of a 30 sec epoch

23. Spectral analysis and various sleep patterns Changes over time in the distribution of the various frequencies follows changes in sleep pattern: –Rhythmicity in delta sleep –REM sleep periodicity –Periods of wakefullness

24. Delta activity Low voltage mixed frequency in REM sleep 50 Hz notch filter

25. Transition to stage Wake

26. Delta Alpha

27. Normal sleep pattern

28. Insomnia sleep pattern

29. CPAP titration Sleep pattern during CPAP titration

30. Heinzer et al., Chest 2002

31. Spectral analysis and arousal (Black et al., Am.J.Respir.Crit.CareMed., 2000) Apnea can be followed by arousal Micro-arousals are often not visually recognized as arousals according to ASDA criteria Spectral analysis can give more insight into EEG alterations occurring after an apnea

32. Arousal and Eso Black et al., 20.., Chest

33. Arousal and Eso Black et al., 2000, Am.J.Respir.Crit.CareMed.

34. Arousal and Eso Black et al., 2000, Am.J.Respir.Crit.CareMed.

35. Arousal and Eso Black et al., 2000, Am.J.Respir.Crit.CareMed.

36. Spectral analysis of snore sounds Differences in frequency content of snoring sounds might be clinically important (high versus low frequency)

37. Snores with high frequency sound vibrations

38. Snores with low frequency sound vibrations

39. High frequency snore sound vibrations

40. Low frequency snore sound vibrations