A comparison of period amplitude analysis and FFT power spectral analysis of all-night human sleep EEG

Zero-cross and zero-derivative period amplitude analysis (PAA) data were co mpared with power spectral analysis (PSA) data obtained with the fast Fouri er transform in all-night sleep EEG from 10 subjects. Although PAA zero-cro ss-integrated amplitude showed good agreement with PSA power in 0.3-2 Hz, z ero-cross analysis appears relatively ineffective in measuring 2-4 Hz and a bove 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 m easure the entire EEG spectrum, the analytic technique should be changed fr om 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 t hree frequency bands: delta (0.3-4 Hz); sigma (12-16 Hz); and fast beta (20 -40 Hz). The frequencies between delta and sigma, and between sigma and fas t beta, did not show clear across-night oscillations using either method, a nd the two methods showed lower epoch-to-epoch agreement in these intermedi ate bands. The causes of this reduced agreement are not immediately clear, nor is it obvious which method gives more valid results. We believe that th e three strongly oscillating frequency bands represent fundamental properti es of the human sleep EEG that provide important clues to underlying physio logical mechanisms. These mechanisms are more likely to be understood if th eir dynamic properties are preserved and measured naturalistically rather t han being forced into arbitrary sleep stages or procrustean models. Both PA A and PSA can be employed for such naturalistic studies. PSA has the advant ages of applying the same analytic method across the EEG spectrum and rests on more fully developed theory. Combined zero-cross and zero-derivative PA A demonstrates EEG oscillations that closely parallel those observed with s pectral power, and the PAA measures do not rely on assumptions about the sp ectral composition of the signal. In addition, both PAA techniques can meas ure the relative contributions of wave amplitude and incidence to total pow er. These waveform characteristics represent different biological processes and respond differentially to a wide range of experimental conditions. (C) 1999 Elsevier Science Inc.