title: A comparison of period amplitude analysis and FFT power spectral analysis of all-night human sleep EEG.
abstract: Zero-cross and zero-derivative period amplitude analysis (PAA) data were compared with power spectral analysis (PSA) data obtained with the fast Fourier transform in all-night sleep EEG from 10 subjects. Although PAA zero-cross-integrated amplitude showed good agreement with PSA power in 0.3-2 Hz, zero-cross analysis appears relatively ineffective in measuring 2-4 Hz and above waves. However, PAA zero-derivative measures of peak-trough amplitude correlated well with PSA power in 2-4 Hz. Thus, while PAA appears able to measure the entire EEG spectrum, the analytic technique should be changed from zero cross to zero derivative at about 2 Hz in human sleep EEG. PAA and PSA both demonstrate robust and interrelated across-night oscillations in three frequency bands: delta (0.3-4 Hz); sigma (12-16 Hz); and fast beta (20-10 Hz). The frequencies between delta and sigma, and between sigma and fast beta, did not show clear across-night oscillations using either method, and the two methods showed lower epoch-to-epoch agreement in these intermediate bands. The causes of this reduced agreement are not immediately clear, nor is it obvious which method gives more valid results. We believe that the three strongly oscillating frequency bands represent fundamental properties of the human sleep EEG that provide important clues to underlying physiological mechanisms. These mechanisms are more likely to be understood if their dynamic properties are preserved and measured naturalistically rather than being forced into arbitrary sleep stages or procrustean models. Both PAA and PSA can be employed for such naturalistic studies. PSA has the advantages of applying the same analytic method across the EEG spectrum and rests on more fully developed theory. Combined zero-cross and zero-derivative PAA demonstrates EEG oscillations that closely parallel those observed with spectral power, and the PAA measures do not rely on assumptions about the spectral composition of the signal. In addition, both PAA techniques can measure the relative contributions of wave amplitude and incidence to total power: These waveform characteristics represent different biological processes and respond differentially to a wide range of experimental conditions.
authors.affiliation: Tokyo Institute of Psychiatry, Psychophysiology Department, Setagaya, Japan. uchida@prit.go.jp
authors.forename: S
authors.initials: S
authors.lastname: Uchida
authors.forename: I
authors.initials: I
authors.lastname: Feinberg
authors.forename: J D
authors.initials: JD
authors.lastname: March
authors.forename: Y
authors.initials: Y
authors.lastname: Atsumi
authors.forename: T
authors.initials: T
authors.lastname: Maloney
mesh_terms: D000328:Adult; D001611:Beta Rhythm; D002540:Cerebral Cortex / Q000502:physiology; D003700:Delta Rhythm; D005260:Female; D005583:Fourier Analysis; D006801:Humans; D008297:Male; D017286:Polysomnography / Q000295:instrumentation*; D012016:Reference Values; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D012894:Sleep Stages / Q000502:physiology*; D013826:Theta Rhythm