DIGITAL SIMULATION AID IN DESIGNING AN AUTOMATIC EEG ANALYZER

Through period analysis, analog sleep EEG information was compressed into a series of numbers representing the incidence of intervals generated by zero-crossing of the EEG and its first derivative (digital differential) for 11-min. epochs. The resulting measurement vectors served not only for prelim...

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Bibliographic Details
Main Authors: Lessard,Charles S, Hughes,Harry M
Format: Report
Language:English
Subjects:
Anatomy and Physiology
Computer Hardware
Computer Programming and Software
COMPUTER PROGRAMS
DATA PROCESSING
DIGITAL COMPUTERS
ELECTROENCEPHALOGRAPHY
PHYSIOLOGY
SIMULATION
SLEEP
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Summary:Through period analysis, analog sleep EEG information was compressed into a series of numbers representing the incidence of intervals generated by zero-crossing of the EEG and its first derivative (digital differential) for 11-min. epochs. The resulting measurement vectors served not only for preliminary assessment of the descriptors as stage discriminators, but also for subjective comparison of EEG signals between leads for the same stages of sleep. In efforts to generate decision surfaces for dividing sleep into five stages (I through IV, and rapid eye movements), multivariate linear discriminant analysis was employed. To limit the number of variables for training and for discrimination of a sleep night, data from only one subject were used. A 23-variable set appeared to be the best choice. According to research results, 85% accuracy can be obtained on any night of sleep for a subject, provided that the training set is from the same subject. (Author) Errata sheet inserted.