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Early and accurate diagnosis of patients with psychogenic nonepileptic seizures (PNES) leads to appropriate treatment and improves long-term seizure prognosis. However, this is complicated by the need to record seizures to make a definitive diagnosis. Suspicion for PNES can be raised through knowledge that patients with PNES have increased somatic sensitivity and report more positive complaints on...
The definitive diagnosis of the type of epilepsy, if it exists, in medication-resistant seizure disorder is based on the efficient combination of clinical information, long-term video-electroencephalography (EEG) and neuroimaging. Diagnoses are reached by a consensus panel that combines these diverse modalities using clinical wisdom and experience. Here we compare two methods of multimodal computer-aided...
The application of machine learning to epilepsy can be used both to develop clinically useful computer-aided diagnostic tools, and to reveal pathologically relevant insights into the disease. Such studies most frequently use neurologically normal patients as the control group to maximize the pathologic insight yielded from the model. This practice yields potentially inflated accuracy because the groups...
Interictal electroencephalography (EEG) has clinically meaningful limitations in its sensitivity and specificity in the diagnosis of epilepsy because of its dependence on the occurrence of epileptiform discharges. We have developed a computer‐aided diagnostic (CAD) tool that operates on the absolute spectral energy of the routine EEG and has both substantially higher sensitivity and negative predictive...
Developing EEG-based computer aided diagnostic (CAD) tools would allow identification of epilepsy in individuals who have experienced possible seizures, yet such an algorithm requires efficient identification of meaningful features out of potentially more than 35,000 features of EEG activity. Mutual information can be used to identify a subset of minimally-redundant and maximally relevant (mRMR) features...
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