A QUANTITATIVE METHOD FOR CLASSIFICATION OF EEG IN THE FETAL BABOON

Electroencephalographic (EEG) activity is used as a primary indicator of sleep states in adults and infants of many species and in the ovine fetus. We recently reported that the baboon fetus exhibits visually d iscernable patterns of EEG activity. One pattern of activity, characte rized by the intermittent presence of repetitive bursts of high-voltag e EEG, is indistinguishable from trace alternant (TA). TA is a distinc tive pattern of EEG activity found only during early stages of develop ment in primates. TA is the predominant pattern of EEG activity during quiet sleep in human infants <2 mo of age. The focus of this study wa s to derive quantitative parameters that would discriminate TA from ot her activity and then to develop a method for automated categorization of EEG patterns. Results demonstrate that several parameters derived from frequency-domain analyses are related to visually coded EEG state s. Among these parameters, high-frequency power (12-24 Hz) and spectra l-edge frequency are good discriminators of EEG patterns. This paper d escribes a new parameter, EEG ratio, computed as spectral power in the rectified EEG within a band that corresponds to the frequency of burs ts of activity during TA (0.03-0.20 Hz) divided by power in the 12- to 24-Hz band. This new composite parameter of EEG activity provides a m arkedly better correlate of visually coded EEG than any of the individ ual parameters tested. Using cluster analysis, we devised a method for objective minute-by-minute dichotomization of EEG ratio. The method p roduces results that agree with visual coding of EEG activity 87.1% of the time. This automated classification of fetal baboon EEG activity will facilitate the study of the ontogeny of sleep states and the effe cts of clinically relevant perturbations such as hypoxia or drugs on c entral nervous system function during fetal life.