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Since the introduction of the first, 4-channel ambulatory EEG (aEEG) system in the mid-1970s,1 neurologists have had the ability to capture and study seizures in an outpatient, natural environment. These systems record and store days of EEG data, enabling evaluation of clinical events suspicious for seizures and characterization of seizure patterns in those with established epilepsy. However, the review of long, multichannel recordings is time-consuming. Therefore, from the beginning, there has been a strong demand for computer-assisted EEG review and seizure detection tools to diminish the burden of manual review of the entire raw dataset. Automated methods of ictal event detection on aEEG face daunting challenges arising not only from the high variability of seizure onset patterns but also the confounding effects of abundant artifacts picked up by scalp electrodes in a freely moving person. Decades of intense research and improvements of our computational ability have led to the development of sophisticated seizure detection software packages, several of which are now commercially available. Most of these approaches, however, have been validated for inpatient EEG recordings, while their accuracy for clinical aEEG applications has not been well established.
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See page 649
- © 2019 American Academy of Neurology
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