INTRODUCTION:Seizure is a common comorbidity in patients with brain tumor. It may be the presenting symptom or develop after the tumor diagnosis. The underlying pathophysiology of brain tumor-related epilepsy remains poorly understood. METHODS:A comprehensive literature review of Pubmed English articles from 1980-2017 was performed to summarize current knowledge and treatment options of brain tumor-related epilepsy. RESULTS:Multiple factors have been found to contribute to tumor-related epilepsy, including tumor type, speed of tumor growth, location, and tumor burden. The underlying pathogenesis of epilepsy is not clear but perturbations in the peri-tumoral regions, both structural and cellular communications, have been implicated. CONCLUSIONS:Surgical and medical treatments of tumor-related epilepsy remain challenging as additional factors such as the extent of surgical resection, interactions with tumor-related oncological treatments and anti-epileptic medication related side effects need to be considered.

Purpose

Existing automated seizure detection algorithms report sensitivities between 43% and 77% and specificities between 56% and 90%. The algorithms suffer from false alarms when applied to neonatal EEG because of the high degree of nurse handling and rhythmic patting used to soothe neonates. Computer vision technology that quantifies movement in real time could distinguish artifactual motion and improve automated neonatal seizure detection algorithms.

Methods

The authors used video EEG recordings from 43 neonates undergoing monitoring for seizures as part of the NEOLEV2 clinical trial. The Persyst neonatal automated seizure detection algorithm ran in real time during study EEG acquisitions. Computer vision algorithms were applied to extract detailed accounts of artifactual movement of the neonate or people near the neonate though dense optical flow estimation.

Results

Using the methods mentioned above, 197 periods of patting activity were identified and quantified, of which 45 generated false-positive automated seizure detection events. A binary patting detection algorithm was trained with a subset of 470 event videos. This supervised detection algorithm was applied to a testing subset of 187 event videos with 8 false-positive events, which resulted in a 24% reduction in false-positive automated seizure detections and a 50% reduction in false-positive events caused by neonatal care patting, while maintaining 11 of 12 true-positive seizure detection events.

Conclusions

This work presents a novel approach to improving automated seizure detection algorithms used during neonatal video EEG monitoring. This artifact detection mechanism can improve the ability of a seizure detector algorithm to distinguish between artifact and true seizure activity.