We conducted a study to explore how electroencephalographers (EEGers) read EEGs and
reach clinical impressions based upon them. Eight EEGers and a rule-based computerized …

Objective To validate an artificial intelligence-based computer algorithm for detection of
epileptiform EEG discharges (EDs) and subsequent identification of patients with epilepsy …

Since the 1970s advances in science and technology during each succeeding decade have
renewed the expectation of efficient, reliable automatic epileptiform spike detection (AESD) …

Both rule-based and artificial-neural-network approaches have been applied to the task of
automating the detection of epileptiform activity (EA) in the EEG.'To date, however, few have …

This study compares an automated spike detection program to a group of 6
electroencephaographers. Since group members varied in experience, an expertise factor …

Importance Interictal epileptiform discharges (IEDs) in electroencephalograms (EEGs) are a
biomarker of epilepsy, seizure risk, and clinical decline. However, there is a scarcity of …

AMBULATORY ELECTROENCEPHALOGRAPHIC MONITORING 611 playback device
solved the last (105). Complete four-channel ambulatory cassette systems were …

We have developed and tested “off-line” an artificial neural network (ANN) that successfully
detects epileptiform discharges (EDs) when trained on EEG records marked by an …

Purpose: Performance of automatic spike‐detection algorithms and interrater reliability of
human EEG reviewers were investigated previously by using scalp EEG recordings …

The spike-and-wave detection scheme described in this report is based on the recognition of
groups of spikes and sharp waves with loosely defined temporal and inter-channel …