Date of Award

Summer 1975

Document Type

Dissertation - Restricted

Degree Name

Doctor of Philosophy (PhD)


Biomedical Engineering

First Advisor

Ackmann, James J.

Second Advisor

Niederjohn, Russell J.

Third Advisor

Sances, Anthony


In critical management of the comatose patient, it is desirable to frequently determine the level of consciousness and to have real-time indication of any alteration in physiological status, For this purpose, computer-based non-invasive methods of patient monitoring are currently being employed in a neurosurgical intensive care unit located at Milwaukee County General Hospital. As part of this overall monitoring program, a study was conducted to comprehensively analyse the electroencephalograms (EEGs) being gathered in this neurologic unit, and to develop methods that will assist the physician in early evaluation of clinical trends. Two-channel EEGs from both normal and comatose subjects were recorded on magnetic tape, and were analysed off-line on a LINC-8 computer using a peak-detection technique. The algorithm employs a sliding window concept to detect absolute maxima (peaks) and absolute minima (troughs) in the signal. As each peak-trough combination (count) is registered, the amplitude and time-interval are measured. This count is then immediately classified into one of four major frequency bands: delta (0.5 to 4 Hz), theta (4 to 8), alpha (8 to 13), and beta (13 to 20). An amplitude threshold criteria, as well as artifact discriminators, have been incorporated into the software routine. The EEG is analysed for contiguous one-minute epochs, and two types of statistics are generated: intraband statistics, consisting of the percent time (index), average frequency, and average amplitude within each band; and inter-band statistics, composed of the mean frequency, mean amplitude, and percent artifact over all bands...



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