Automated Quantification of Periodic Discharges in Human Electroencephalogram.

ABSTRACT

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.