A retrospective study of the correlation between duration of monitoring in the epilepsy monitoring unit and diagnostic yield

Adenan, Mohammad Hijaz; Khalil, Mohamed; Loh, Kai Sheng; Kelly, Luke; Shukralla, Arif; Klaus, Stephen J.; Kilbride, Ronan; Mullins, Gerard; Widdess‐Walsh, Peter; Kinney, Michael; Delanty, Norman; El‐Naggar, Hany

doi:10.1016/j.yebeh.2022.108919

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…The differentiation of these transient neurological events can be challenging without simultaneous video-electroencephalographic monitoring (VEM): 30% of patients with presumed epilepsy who undergo VEM instead have functional seizures and 10% of patients who present for prolonged seizure to emergency rooms in clinical trials had functional and not epileptic seizures ( 7–9 ). VEM often requires hospitalization, tends to last for less than 10 days at a time, and is primarily available at tertiary care centers ( 10 ). Therefore, one key clinical challenge is to develop hardware and software technologies for highly accurate, reliable, and long-term detection of epileptic seizures.…”

Section: Introductionmentioning

confidence: 99%

The present and future of seizure detection, prediction, and forecasting with machine learning, including the future impact on clinical trials

Kerr,

McFarlane,

Figueiredo Pucci

2024

Front. Neurol.

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Seizures have a profound impact on quality of life and mortality, in part because they can be challenging both to detect and forecast. Seizure detection relies upon accurately differentiating transient neurological symptoms caused by abnormal epileptiform activity from similar symptoms with different causes. Seizure forecasting aims to identify when a person has a high or low likelihood of seizure, which is related to seizure prediction. Machine learning and artificial intelligence are data-driven techniques integrated with neurodiagnostic monitoring technologies that attempt to accomplish both of those tasks. In this narrative review, we describe both the existing software and hardware approaches for seizure detection and forecasting, as well as the concepts for how to evaluate the performance of new technologies for future application in clinical practice. These technologies include long-term monitoring both with and without electroencephalography (EEG) that report very high sensitivity as well as reduced false positive detections. In addition, we describe the implications of seizure detection and forecasting upon the evaluation of novel treatments for seizures within clinical trials. Based on these existing data, long-term seizure detection and forecasting with machine learning and artificial intelligence could fundamentally change the clinical care of people with seizures, but there are multiple validation steps necessary to rigorously demonstrate their benefits and costs, relative to the current standard.

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Section: Introductionmentioning

confidence: 99%