Epilepsy research
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The concept of a preictal state is based on the belief that it may be possible to predict seizures before they occur. The preictal state is viewed as a time period when a seizure is practically inevitable, or at least a period of greatly increased seizure probability. Changes in seizure frequency may provide insight into how seizure probability increases after brain injury. ⋯ Also, spontaneous recurrent seizures frequently occurred in clusters, suggesting that the intra-cluster seizure intervals are periods of high seizure probability. Thus, seizure probability progressively increases as a function of time after an epileptogenic brain injury, and is particularly high between seizures within a cluster, as compared to the time between clusters. These data suggest that the detectors of the preictal state need to be accurate (and tested) over a very wide range of seizure probabilities, and that studies on the physiological events that occur during seizure clusters may provide insight on the properties of the preictal state.
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To assess the reporting of adverse events (AEs) in randomised controlled trials (RCTs) of antiepileptic drugs (AEDs) using the CONSORT statement for harms 2004, and to determine if reporting has changed since introduction of this standard. ⋯ Reporting of AEs in RCTs of AEDs is poor and has not improved since the publication of the CONSORT guidelines on the reporting of harms. Commercially funded trials were better reported than non-commercially funded trials and trials recruiting adults were better reported than trials recruiting children. These findings have serious implications as poor reporting precludes bias being detected and hinders adequate risk benefit analyses. Journal editors, authors and reviewers should be encouraged to follow current guidance.
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Comparative Study Controlled Clinical Trial
Diffusion tensor imaging can localize the epileptogenic zone in nonlesional extra-temporal refractory epilepsies when [(18)F]FDG-PET is not contributive.
Surgical outcome in patients with nonlesional refractory partial epilepsies could be improved by a more precise definition of the epileptogenic zone (EZ). The value of interictal FDG-PET hypometabolism, voxel-based-morphometry (VBM) and diffusion tensor imaging (DTI) is still debated. We compared the sensitivity and specificity of these noninvasive techniques in localizing the EZ with stereo-electroencephalography (SEEG) results. ⋯ For extra-TLE, combining vPET and DTI results increased the number of pertinent abnormalities detected especially for circumscribed changes in frontal lobe epilepsy (FLE). Combining vPET and DTI was the more efficient strategy for extra-TLE, allowing the detection of pertinent abnormalities in FLE when FDG-PET alone was not contributive. Combining sPET or VBM with vPET was less useful.
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Clinical Trial
Utility of diffusion tensor imaging tractography in decision making for extratemporal resective epilepsy surgery.
To assess the utility of diffusion tensor imaging tractography (DTIT) in decision making in patients considered for extratemporal resective epilepsy surgery. ⋯ DTIT is a noninvasive imaging strategy that can be used effectively in planning resection of epileptogenic lesions at or close to eloquent cortical areas. DTIT helps in predicting postoperative neurological outcome and thereby assists in surgical decision making and in preoperative counseling of patients with extratemporal focal epilepsies.
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The automatic detection and classification of epileptic EEG are significant in the evaluation of patients with epilepsy. This paper presents a new EEG classification approach based on the extreme learning machine (ELM) and nonlinear dynamical features. The theory of nonlinear dynamics has been a powerful tool for understanding brain electrical activities. ⋯ The statistics indicate that the differences of those nonlinear features between interictal and ictal EEGs are statistically significant. The ELM algorithm is employed to train a single hidden layer feedforward neural network (SLFN) with EEG nonlinear features. The experiments demonstrate that compared with the backpropagation (BP) algorithm and support vector machine (SVM), the performance of the ELM is better in terms of training time and classification accuracy which achieves a satisfying recognition accuracy of 96.5% for interictal and ictal EEG signals.