Computer-assisted EEG diagnostic review for idiopathic generalized epilepsy
| Autor: | Claire Lim, Janelle Taylor, Ewan S. Nurse, Patrick Hennessy, Robert R. Kerr, Rory Knight-Sadler, Udaya Seneviratne, Dean R. Freestone, Dulini Mendis, Mark J. Cook, Braden Moore, Shannon M. Clarke, Wendyl D'Souza, Jake Miles, Philippa J. Karoly |
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| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
Electroencephalography Diagnostic monitoring Convolutional neural network Idiopathic generalized epilepsy 03 medical and health sciences Behavioral Neuroscience Epilepsy 0302 clinical medicine Physical medicine and rehabilitation Eeg data medicine Humans Ictal 030212 general & internal medicine medicine.diagnostic_test Computers business.industry Deep learning Signal Processing Computer-Assisted medicine.disease Neurology Epilepsy Generalized Neurology (clinical) False positive rate Artificial intelligence business Algorithms 030217 neurology & neurosurgery |
| Zdroj: | Epilepsy & Behavior. 121:106556 |
| ISSN: | 1525-5050 |
| Popis: | Epilepsy diagnosis can be costly, time-consuming, and not uncommonly inaccurate. The reference standard diagnostic monitoring is continuous video-electroencephalography (EEG) monitoring, ideally capturing all events or concordant interictal discharges. Automating EEG data review would save time and resources, thus enabling more people to receive reference standard monitoring and also potentially heralding a more quantitative approach to therapeutic outcomes. There is substantial research into the automated detection of seizures and epileptic activity from EEG. However, automated detection software is not widely used in the clinic, and despite numerous published algorithms, few methods have regulatory approval for detecting epileptic activity from EEG. This study reports on a deep learning algorithm for computer-assisted EEG review. Deep convolutional neural networks were trained to detect epileptic discharges using a preexisting dataset of over 6000 labelled events in a cohort of 103 patients with idiopathic generalized epilepsy (IGE). Patients underwent 24-hour ambulatory outpatient EEG, and all data were curated and confirmed independently by two epilepsy specialists (Seneviratne et al., 2016). The resulting automated detection algorithm was then used to review diagnostic scalp EEG for seven patients (four with IGE and three with events mimicking seizures) to validate performance in a clinical setting. The automated detection algorithm showed state-of-the-art performance for detecting epileptic activity from clinical EEG, with mean sensitivity of >95% and corresponding mean false positive rate of 1 detection per minute. Importantly, diagnostic case studies showed that the automated detection algorithm reduced human review time by 80%-99%, without compromising event detection or diagnostic accuracy. The presented results demonstrate that computer-assisted review can increase the speed and accuracy of EEG assessment and has the potential to greatly improve therapeutic outcomes. This article is part of the Special Issue "NEWroscience 2018". |
| Databáze: | OpenAIRE |
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