Automated Quantification of Periodic Discharges in Human Electroencephalogram.

Periodic discharges (PDs) are pathologic patterns of epileptiform
discharges repeating at regular intervals, commonly detected in the human
electroencephalogram (EEG) signals in patients who are critically ill. The
frequency and spatial extent of PDs are associated with the tendency of PDs to
cause brain injury, existing automated algorithms do not quantify the
frequency and spatial extent of PDs. The present study presents an algorithm
for quantifying frequency and spatial extent of PDs. The algorithm quantifies
the evolution of these parameters within a short (10-14 second) window, with a
focus on lateralized and generalized periodic discharges. We test our
algorithm on 300 ``easy'', 300 ``medium'', and 240 ``hard'' examples (840
total epochs) of periodic discharges as quantified by interrater consensus
from human experts when analyzing the given EEG epochs. We observe $95.0\%$
agreement with a 95\% confidence interval (CI) of $[94.9\%, 95.1\%]$ between
algorithm outputs with reviewer clincal judgement for easy examples, $92.0\%$
agreement (95\% CI $[91.9\%, 92.2\%]$) for medium examples, and $90.4\%$
agreement (95\% CI $[90.3\%, 90.6\%]$) for hard examples. The algorithm is
also computationally efficient and is able to run in $0.385 \pm 0.038$ seconds
for a single epoch using our provided implementation of the algorithm. The
results demonstrate the algorithm's effectiveness in quantifying these
discharges and provide a standardized and efficient approach for PD
quantification as compared to existing manual approaches.