Objective: To develop and test a fully automated method for estimation of the peak frequency of the posterior dominant rhythm (PDR) in a large retrospective EEG cohort.
Methods: Thresholding was used to select suitable EEG data segments for spectral estimation for electrode O1 and O2. A random sample of 100 peak frequency estimates were blindly rated by two independent raters to validate the results of the automatic PDR peak frequency estimates. We investigated the relationship with age, sex and binary EEG classification.
Results: There were 9197 eligible EEGs which resulted in a total of 6104 PDR peak frequency estimates. The relationship between automatic estimates and age was found to be consistent with the literature. The correlation between human ratings and automatic scoring was very high, rho = 0.94-0.95. There was a sex difference of d = 0.33 emerging at puberty with females having a faster PDR peak frequency than males.
Conclusions: Fully automatic PDR peak frequency estimation not dependent on annotated EEG produced results that are very close to human ratings.
Significance: PDR peak frequency can be automatically estimated. A compiled version of the algorithm is included as an app for independent use.