Automated EEG analysis: Characterizing the posterior dominant rhythm

► We introduce a method to automatically characterize the frequency, amplitude, symmetry and reactivity of the posterior dominant rhythm. ► A certainty value provides the clinician with a confidence score of each estimate. ► In more than 92.5% of the cases the PDR frequency was estimated within 1.2...

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Bibliographic Details
Published in:Journal of neuroscience methods 2011-08, Vol.200 (1), p.86-93
Main Authors: Lodder, Shaun S., van Putten, Michel J.A.M.
Format: Article
Language:English
Subjects:
Adolescent
Adult
Aged
Aged, 80 and over
Alpha Rhythm - physiology
Cerebral Cortex - physiology
Certainty
Child
Child, Preschool
Computerized interpretation
Electroencephalography
Electroencephalography - methods
Electroencephalography - standards
Female
Humans
Infant
Male
Middle Aged
Models, Neurological
Posterior dominant rhythm
Reproducibility of Results
Signal Processing, Computer-Assisted
Young Adult
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Summary:► We introduce a method to automatically characterize the frequency, amplitude, symmetry and reactivity of the posterior dominant rhythm. ► A certainty value provides the clinician with a confidence score of each estimate. ► In more than 92.5% of the cases the PDR frequency was estimated within 1.2 Hz of visual estimates. Automated interpretation of clinical EEG recordings will reduce subjectivity and visual bias from analysis and can reduce the time required for interpretation. As a first step in the design of a fully automated system, a method is presented to characterize the main properties of the posterior dominant rhythm (PDR), in particular its frequency, symmetry and reactivity. The presented method searches for dominant peaks in the EEG spectra during eyes-closed states with a three-component curve-fitting technique. From the fitted curve, the frequency and amplitude are estimated. The symmetry and the reactivity are found using the spectral power at the PDR frequencies. In addition, a certainty value is introduced as a measure of confidence for each estimate. The method was evaluated on a test set of 1215 clinical EEG recordings and compared to the PDR frequencies obtained from the visual analysis, as reported in the diagnostic reports. The calculated PDR frequencies were within 1.2 Hz of the visual estimates in 92.5% of the cases. Even higher accuracies were reached when estimates with low certainty values were discarded. The presented method quantifies essential features of the PDR with a matched accuracy to visual inspection, making it a feasible contribution to the design of a fully automated interpretation system.
ISSN:0165-0270
1872-678X
DOI:10.1016/j.jneumeth.2011.06.008