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Introduction: Data from an EEG is not commonly used by psychiatrists to plan treatment and medication. However, EEG abnormalities such as isolated epileptiform discharges are found to be more prevalent in psychiatric patients, particularly those diagnosed with autism spectrum disorder (ASD). Most medications prescribed for ASD lower seizure threshold and increase side effects. Therefore, it may be prudent to order an EEG for ASD cases, especially those categorized as refractory., Methods: The data set was obtained from a multidisciplinary practice that treats a wide variety of neuroatypical children and adolescent refractory patients. This study investigated 140 nonepileptic subjects diagnosed with ASD, aged 4 to 25 years. Visual inspection of the EEG was performed to search for paroxysmal, focal, or lateralizing patterns., Results: Of the 140 subjects, the EEG data identified 36% with isolated epileptiform discharges. The χ analysis found no significant difference between genders among the three age groups. Findings indicated a high prevalence of isolated epileptiform discharges among individuals with ASD., Conclusions: Our results find that compared with the healthy population, a large number of patients with ASD have isolated epileptiform discharges despite never having a seizure. Our findings support the use of EEG in children, adolescents, and young adults with ASD, regardless of gender or age. This is particularly true for those who exhibit aggressive behaviors or those who have failed previous medication attempts with stimulants, antidepressants, and/or antipsychotics.

The routine scalp electroencephalogram (rsEEG) is the most common clinical neurophysiology procedure. The most important role of rsEEG is to detect evidence of epilepsy, in the form of epileptiform transients (ETs), also known as spike or sharp wave discharges. Due to the wide variety of morphologies of ETs and their similarity to artifacts and waves that are part of the normal background activity, the task of ET detection is difficult and mistakes are frequently made. The development of reliable computerized detection of ETs in the EEG could assist physicians in interpreting rsEEGs. We report progress in developing a standardized database for testing and training ET detection algorithms. We describe a new version of our EEGnet software system for collecting expert opinion on EEG datasets, a completely web-browser based system. We report results of EEG scoring from a group of 11 board-certified academic clinical neurophysiologists who annotated 30-s excepts from rsEEG recordings from 100 different patients. The scorers had moderate inter-scorer reliability and low to moderate intra-scorer reliability. In order to measure the optimal size of this standardized rsEEG database, we used machine learning models to classify paroxysmal EEG activity in our database into ET and non-ET classes. Based on our results, it appears that our database will need to be larger than its current size. Also, our non-parametric classifier, an artificial neural network, performed better than our parametric Bayesian classifier. Of our feature sets, the wavelet feature set proved most useful for classification., (Published by Elsevier B.V.)

Computerized detection of epileptiform transients (ETs), characterized by interictal spikes and sharp waves in the EEG, has been a research goal for the last 40 years. A reliable method for detecting ETs would assist physicians in interpretation and improve efficiency in reviewing long-term EEG recordings. Computer algorithms developed thus far for detecting ETs are not as reliable as human experts, primarily due to the large number of false-positive detections. Comparing the performance of different algorithms is difficult because each study uses individual EEG test datasets. In this article, we present EEGnet, a distributed web-based platform for the acquisition and analysis of large-scale training datasets for comparison of different EEG ET detection algorithms. This software allows EEG scorers to log in through the web, mark EEG segments of interest, and categorize segments of interest using a conventional clinical EEG user interface. This software platform was used by seven board-certified academic epileptologists to score 40 short 30-second EEG segments from 40 patients, half containing ETs and half containing artifacts and normal variants. The software performance was adequate. Interrater reliability for marking the location of paroxysmal activity was low. Interrater reliability of marking artifacts and ETs was high and moderate, respectively.